Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.\nBefore understanding how to choose the best machine learning algorithms let us briefly look into the types of machine learning algorithms. The machine learning algorithm can be broadly classified into three groups.\nSupervised Learning Algorithm\nUnsupervised Learning Algorithm\nReinforcement Learning Algorithm\nRegression\nK-Mean Clustering\nMonte Carlo\nRandom Forest\nKNN\nMarkov Decision Process\nDecision Tree\nSVD\nQ Learning\nSupervised learning model: Trained with the dataset which consists of labels for both Input and output.\nUnsupervised learning model: Trained with the dataset that has input features but does not have labels for the output.\nReinforcement learning model: Trains itself based on a set of actions and makes the decision.\nNow, let’s learn when to use these algorithms rather than how to use these algorithms. Choosing the correct algorithm is dependent on multiple variables. A good understanding of data science plays a very significant role in categorizing the problem statement.\nThe factors we need to consider while categorizing and solving the problem are:\nKnowledge of Data\n: The data’s structure and complexity help dictate the right algorithm\nAccuracy Requirements\n: Different questions demand different degrees of accuracy, which influences algorithm selection\nProcessing Speed\n: Algorithm choice may depend on the time constraints in place for a given analysis\nVariables\n: The unique features considered while training the model for the optimal result and accuracy help determine the right algorithm.\nParameters\n: Factors such as the number of iterations directly relate to the training time needed when generating output.\nOur first step is to analyze the data and observe the patterns and any hidden insights by visualizing the data. The insights from data visualization will help in making an initial decision on which algorithm to choose for solving the given problem.\nAs an example, if the dataset has input and output labels, a supervised learning algorithm will be best for the problem. Supervised learning can be segregated further based on the type of output. For any numeric output, one should choose a regression algorithm to train the model, but for any class output, one should target a classification algorithm to train the model.\nIf the dataset has no output labels, choosing an unsupervised learning algorithm will be a wise decision, for example, the K-Mean clustering algorithm comes in handy in dividing the dataset into a smaller cluster or set based on similar attributes.\nThe characteristics and behavior of the dataset also play an important role in making the ideal algorithm selection. For example, if the data is sequential, like stock price or heart rate, then the algorithm that fits best is the Markov model and decision tree.\nAnother important factor in deciding the algorithm is speed and accuracy. There is a tradeoff between speed and accuracy. If accuracy is not vital, perhaps when estimating a value, we can reduce the processing time, thus increasing the execution speed. Conversely, we can achieve better accuracy by slowing the execution speed.\nAnother factor that plays an important role in choosing the correct algorithm is the number and types of parameters we pass while training the model. These parameters may include iteration cycle, splitting train and test datasets, error tolerance, and more. The training time for a given model is directly proportional to the number of parameters included. When multiple parameters are required to train the model, SVM (Support Vector Machine) algorithms work well.\nReal-Life Use Cases\nLinear Regression\nThis can be used to predict housing prices based on multiple variables including square footage, the presence of a balcony, the number of bathrooms, total rooms, kitchen area, etc.\nEach of these variables plays an important role in predicting the accurate housing price. A co-relation of 1 indicates a perfect relationship and accurate housing price, anything less than 0.9 can safely be assumed as unreliable prediction.\nLogistic Regression\nCurrently, bank customers face the constant threat of credit card and identity fraud. As a result, the financial sector is working to devise new techniques to avoid these problems. One of the steps banks have taken to reduce fraud is by leveraging logistic regression to build a classifier, known as the LogR classifier, which adapts to the user’s behavior based on his historic data. As a result, financial institutions can more accurately distinguish their true customers from any impersonator.\nSupport Vector Machine (SVM)\nThis helps in handwriting detection using a support vector machine classifier. It has achieved accuracy of up to 96% when \nconverting handwritten notes\n into text-based data.\nRandom Forest\nRandom Forest algorithms can be used to predict equipment failure and schedule just-in-time preventive maintenance. The failure of the equipment, such as utility poles or transformers, can be caused by cracks, corrosion pitting, spalls on rolling surfaces, and more. These failures can be catastrophic and financially devastate a given organization.\nTaking advance of Random Forest machine learning algorithms and AI to monitor these assets may help in \ndetecting failures at their earliest stages\n, reducing or preventing damage to the asset and the organization’s bottom line altogether.\nK-Mean\nUber, Lyft and other rideshare apps utilize K-Mean machine learning algorithms for optimal placing and clustering of their vehicles based on the peak demand. As a result, customers enjoy reduced wait times and helps the rideshare companies dictate surge pricing when supply is low and demand is high.\nIn Conclusion\nAs we have discussed, machine learning algorithms generally fall into one of three categories – supervised learning, unsupervised learning, and reinforcement learning. When addressing different types of business challenges, analysts should carefully consider the characteristics of the data, speed and accuracy requirements, variables, and parameters in order to yield the desired insights.\nBy using the proper algorithms, organizations can expect to benefit from stronger data insights that reflect the unique conditions that guide each business process. As their algorithms continually learn and improve, so will the data-driven decisions that result.

Selecting the correct machine learning algorithm can be difficult, but doing so is critical in order to answer a given question with high speed and accuracy. In this blog, we will introduce three types of machine learning algorithms and explain how to select the right one when tackling business problems. Then, we share a few real-world use cases for the different algorithm types.

How to Choose the Right Machine Learning Algorithm

With the size and scope of collected data expanding with each passing day, it is very important to continually analyze available data to drive better-informed business and policy decisions. To a large extent, handwritten data remains unexplored and unanalyzed. If we can analyze handwritten text data, we can minimize the hurdles and save the manpower involved in digitizing handwritten data.\nIn this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.\nBelow is an example of converting simple handwritten text into digital words, which can be easily ingested into a CSV file or database.\nUsing the applications listed in the instructions below, we are able to convert the scanned images or PDF files into digital text. Our approach for this solution requires us to first convert PDF documents to an image format. As we are processing images, it is better to convert all the images to the same size. We then need to define the source from which we are extracting the data from the image. This requires knowing the coordinates of the data to be extracted.\nSteps to Convert Handwritten Text into Digital Data\nThe prerequisites for this exercise are to install Google Cloud Vision, Python 3, Handprint, Keras, NumPy, pandas, pdf2image and cv2.\nConvert the PDF document to an image using the following python command.
\nimages = convert_from_path(file)\nSave the images to a directory so that this will be used for data extraction.
\nfor img in images:
   img.save(path + "\\" + fileName + '.jpg', 'JPEG')\nRead the image using following command.
\nimage = cv2.imread(filename)\n
\nPass the image to the pytesseract to convert the image to data item.
\nTo view the keys, run the below command.\nCheck the extracted text data.
\nExtract the coordinates for the required text using the following command.\nRepeat the same process for all the required text fields.\nUse the extracted coordinates to crop the image for the required field and save the cropped image to a specific location.
\nDownload the credentials file after setting up the Cloud Vision account on Google.
\nProcess these cropped images using Google’s Cloud Vision to extract handwritten values.
\nWe then extract the required data from the Json files.
\nWe have created a Python dictionary to store the extracted information. Convert the dictionary to dataframe for easy processing.
\nAs this example deals with only two images, the instructions did not include many validations. For a real-world application, analysts should validate the extracted data based on confidence levels and filter out the accurate data.\nIn Conclusion\nHandwritten image and text processing is very useful across many industries, including healthcare, insurance, and utilities. Various use cases include invoice processing, processing of inspection forms, employee onboarding, analyzing reviews and survey data and so on. One prescient application for utilities and other organizations relying on distributed field services is the digitization of handwritten notes taken by field technicians when completing repairs or regularly scheduled maintenance. By digitizing these notes, \nand even incorporating process automation to do so\n, utilities are better situated to retain critical handwritten information regarding asset health, maintenance history, and more.

In this blog, we cover how handwritten text data can be processed using Python and Google Cloud Vision. Cloud vision offers pre-trained ML models which are very powerful, and we do not need to do any pre-training.

How to Process Handwritten Text Using Python and Cloud Vision

By Gokuleshwar Raju Nannapuraju, HEXstream integrations and analytics consultant\nIn this blog post, we navigate through the revolutionary impact of Oracle Cloud Infrastructure–Data Integration (OCI DI), offering a non-technical overview that illuminates its benefits and underscores why it stands as a true game-changer in reshaping the integration landscape for a seamlessly connected future.\nWhat Is OCI DI? \nOCI DI, an abbreviation for Oracle Cloud Infrastructure–Data Integration, serves as a cloud-based ETL (Extract, Transform, Load) tool designed to foster seamless connections between source and target databases. This powerful tool empowers users to efficiently cleanse, transform, and reshape data, streamlining the integration process for enhanced efficiency and reliability.\nUnderstanding Data Integration\nData integration involves the transformation of data from various sources, enabling seamless connectivity between different databases. This process includes connecting to source and target databases, cleansing and transforming the data through mapping and logic definition. Mapping from source to target databases enables data transformation either through manual execution or scheduled jobs based on desired frequencies.\nAdvantages of OCI DI\n1. Zero maintenance: Unlike traditional data-integration tools that require regular patching and service monitoring, OCI DI requires zero maintenance. Users can rest easy, knowing that they are free from any maintenance activities or concerns regarding application versions.\n2. No installation required: OCI DI eliminates the need for time-consuming installations. With a few simple steps, users can create a workspace and promptly start utilizing OCI DI.\n3. Basic configuration: Once the workspace is established, users can effortlessly connect to the source and target databases. OCI DI eliminates the need for complex configurations, ensuring a streamlined integration experience.\n4. Cost-effectiveness: OCI DI is an affordable option for businesses, and its unique pricing model charges users based on data usage and integration needs. There is no need to constantly start or stop services, as costs are calculated according to the actual usage.\n5. Enhanced speed: Regardless of the data volume being integrated, OCI DI utilizes powerful Spark engines in the background, ensuring high-speed execution once the job is initiated. This enables efficient data-integration processes for improved productivity.\nThe Significance Of Zero Maintenance\nLet’s circle back to that zero-maintenance benefit of OCI DI, which alleviates the burdens associated with managing services and ensuring timely startups. Users can enjoy uninterrupted integration capabilities without the need for downtime or patching activities. By eliminating these concerns, OCI DI optimizes business operations and maximizes efficiency.\nIn summary, OCI DI is transforming the integration landscape with its user-friendly features, cost-effectiveness, and maintenance-free experience. By leveraging the power of Oracle Cloud Infrastructure, businesses can enhance their data-integration capabilities and achieve seamless connectivity across various databases. Embrace OCI DI and unlock a new level of integration efficiency for your business.


Transforming Integration: The Evolving Benefits Of Oracle Cloud Infrastructure Data Integration

By Arun Kota, HEXstream senior director analytics\nAn intricately crafted disaster-recovery system stands as a vital pillar for utility organizations, ensuring swift and efficient recovery in the aftermath of catastrophes. In the dynamic landscape of IT, disasters take various forms, ranging from network outages and equipment failures to application glitches and natural calamities, which all pose imminent risks to the integrity of your applications. \nAs we can’t predict when a disaster will strike, the best option is to have a plan on how to quickly recover. \nAn important step in disaster-recovery planning is determining the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for an application. \nRecovery Time Objective (RTO) serves as a crucial metric, reflecting the maximum allowable downtime for a specific application following a disaster, with a general rule that emphasizes shorter RTOs for applications of higher criticality.\nRecovery Point Objective (RPO) signifies the timeframe, post-disaster, during which an application can endure data loss without negatively affecting the enterprise, underscoring the critical need for tailored recovery strategies based on data sensitivity and business-continuity priorities.\nIn crafting a robust disaster-recovery plan, it is imperative to factor in both RTO and RPO, built upon a strategic alignment that harmonizes the goal of timely application recovery with a tolerance for data loss. This dual consideration facilitates the development of a comprehensive, cost-effective, and resilient framework for disaster preparedness.\nMultiple Approaches for Disaster Recovery in OCI\nOracle Cloud Infrastructure (OCI) provides four different approaches in disaster recovery.
Selecting the appropriate disaster-recovery solution for an OCI application hinges on various factors such as availability needs, data durability, as well as the specific requirements for both RTO and RPO, necessitating a thoughtful evaluation to align the chosen solution with the organization’s unique considerations.\n1.    Backup and Restore–This approach backs up critical data and systems into offsite storage on regular intervals. In the event of a disaster, data can be restored from these backups to bring the systems back to their previous states. This is a relatively cheaper approach, but it usually takes more time. The backup-and-restore approach is usually used for noncritical applications.\n2.    Pilot Light–In this approach, a minimal version of the environment is always running in the cloud. In the event of a disaster, the infrastructure can quickly scale up to full capacity. The
RPO and RTO are shorter, but a little costly  compared to the backup-and-restore tactic. The
Pilot Light approach is usually used for critical applications.\n3.    Active/Passive or Warm Standby–In this approach, a fully installed and configured application is on standby and ready to run when required. This setup reduces the time required to switch to a fully operational state in the event of a disaster. The RPO is in seconds and RTO is in minutes; the cost is significantly more expensive than Pilot Light. The
Active/Passive or Warm Standby approach is usually used for critical applications.\n4.    Active/Active–In this approach, the applications are deployed in Active/Active mode and are actively serving. Every transaction is registered  in both applications. Both RPO and RTO are almost zero. This approach has the highest cost, as the applications are running in parallel. The Active/Active approach is used for extremely critical applications.\nOCI disaster recovery can be created using multiple regions. Choosing the right DR approach for an application is very important. The criticality of the applications, data consistency, cost and complexity need to be factored in when deciding the DR approach for an application.\n\nLEARN HOW HEXSTREAM CAN BOOST YOUR EFFICIENCY WITH ORACLE CLOUD INFRASTRUCTURE. CLICK HERE TO CONTACT US. \n\n


Recover Faster! New Approaches To Disaster Recovery With Oracle Cloud Infrastructure (OCI)

By \nGokuleshwar Raju Nannapuraju\n, HEXstream BI/ETL consultant\nAs you may have heard, Oracle has released version 2.9 of its Oracle Utilities Analytics Warehouse (OUA), bringing several new features and improvements. Let’s break down these updates in an easy-to-understand way for non-technical readers…\nSupport for Microservices Architecture for Configuring Oracle GoldenGate\nStarting with version 2.9, OUA now supports the Microservices Architecture for Oracle GoldenGate. This is a modern way to configure and manage Oracle GoldenGate, a tool that helps move data between different databases.\nSo what does this actually mean? \nSimpler and more efficient: Microservices make the configuration and management of data transfers more streamlined and scalable.\nOut with the old: The older "Classic" architecture for setting up Oracle GoldenGate is no longer supported. The configuration module "JKM BI Oracle to Oracle GoldenGate" has been updated to work with this new architecture.\nSupport for Oracle Analytics Server 2024\nOUA 2.9 is now compatible with Oracle Analytics Server version 2024 (version 7.6). It also continues to support the 2023 version. This is important, primarily, because of the enhanced analytics; with this support, users can leverage the latest features and improvements in Oracle Analytics Server 2024, providing better data analysis and visualization capabilities.\nUpdated Supported Platforms\nThe release includes updates on the platforms that are supported, ensuring compatibility with the latest server and client-side technologies.\nServer-side:\nOperating System (Server): Oracle Linux 7.x, 8.x (64-bit), Oracle Solaris 11.4 on
     SPARC (64-bit)\nFMW App Server: WebLogic 12.2.1.4.0\nJDK: JDK 8u411\nOracle Database: Enterprise Edition 19.x, Client for Linux 19\nOracle Analytics Server: 7.0 (2023) and 7.6 (2024)\nOracle Data Integrator (ODI): 12.2.1.4\nApex: 23.2\nOracle GoldenGate Microservices: 19.1\nOracle REST Data Service: 21.4.3\nClient-side:\nClient Operating System: Windows 10 (64-bit)\nBrowser Versions:\nMicrosoft Edge 120.0.2210.77\nMozilla Firefox 121.0\nGoogle Chrome 120.0.6099.109\nSupported Source Application Versions\nThe following source application versions are supported in this Oracle Utilities Analytics Warehouse release:\nCustomer Care and Billing: 2.9.0.1.1\nCustomer to Meter: 2.9.0.1.1\nNetwork Management System: 2.6.0.1.1, 2.3.0.2.0\nWork and Asset Management: 2.4.0.1.1\nMeter Data Management: 2.5.0.1.1\nMobile Workforce Management: 2.3.0.3.0, 2.2.0.x.x\nSupported Upgrade Paths\nThe following upgrade path is available for Oracle Utilities Analytics Warehouse in this release:\nFrom: V2.8.0.1.2 To: V2.9.0.0.0\nSummary\nExisting clients are encouraged to upgrade to OUA 2.9 because the new release supports all the latest versions of source applications and integrates seamlessly with new applications used in OUA. This upgrade will not only resolve existing bugs, but also introduce new features, providing a more robust and efficient analytics solution.\nAs for me, I'm excited to see these upgrades in action across utilities, as they will not only resolve existing issues, but also enhance compatibility with the latest OAS versions and new source applications. This will be incredibly beneficial for end users.\nClick \nhere\n for the official Oracle documentation. \nAnd click \nhere\n to connect with HEXstream to learn how we can help you take advantage of this upgrade. \n
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Oracle Updated OUA…So How Do You Benefit From It?

By Vaishnavi Medikundam, HEXstream BI developer\nOracle has added new facial-intelligence features to Oracle Cloud, offering innovative tools for businesses to enhance their data usage. In the rapidly evolving world of technology, Oracle Cloud is continually innovating, and this new feature is transforming how businesses use data.\nUnderstanding Facial Intelligence\nFacial intelligence goes beyond facial recognition. It's about decoding human expressions, emotions and interactions—providing a deeper understanding of user behavior. Oracle Cloud's new capabilities in this realm promise to change industries that rely on data-driven decision-making. \nKey Features\nOracle Cloud introduces precise facial recognition, emotion analysis, improved user authentication, and analytics/insights. \nOracle Cloud's upgraded facial-recognition system ensures unmatched accuracy, revolutionizing security in sectors like finance and healthcare. The system is equipped to analyze facial expressions, enabling businesses to gauge customer sentiments in real-time. This feature opens new avenues for personalized customer experiences and targeted marketing strategies. \nSay goodbye to traditional passwords. Oracle Cloud's facial intelligence can be integrated and provide a secure and convenient way for users to access sensitive information. \nThe gathered facial-intelligence data isn't just about recognition; it's about insights. Businesses can now leverage analytics to understand customer preferences, optimize workforce management, and make data-driven decisions. \nBenefits Across Industries\n·       Enhanced customer profiling fosters personalized shopping experiences, driving customer satisfaction and loyalty.\n·       Improved patient authentication and emotion analysis elevate healthcare outcomes and patient experiences.\n·      Strengthened security measures (including advanced fraud detection) that can fortify the financial sector against evolving threats. \nOracle Cloud's commitment to innovation continues to empower businesses worldwide. The introduction of advanced facial-intelligence capabilities heralds a new era in data-driven decision-making, promising efficiency, security and unparalleled insights. \nAs we embrace these new capabilities, the future looks brighter and more intelligent than ever before. 

Here's Looking At You: New Facial-Intelligence Capabilities On Oracle Cloud 

By Siddartha Reddy Katukuri, HEXstream integrations and analytics consultant \nFirst, let’s clarify what, exactly, we mean by “event streaming.”\nIn the utility industry, events refer to any significant data points or changes in state, such as
readings from meters, sensors, pipelines or transactional records. Event streaming enables applications and systems to act on data instantly, allowing
utilities to monitor consumption, predict outages, and optimize resource distribution
in real time.\nBy utilizing event streaming, businesses can unlock the full potential of their data. This
capability allows for real-time monitoring of consumption, predictive
maintenance, and optimized resource distribution, depending on the data at
hand. For example, energy providers can identify grid issues before they
escalate, improving preventive measures and service reliability. In industries
like water management, real-time sensor data can instantly detect leaks,
reducing resource wastage and enhancing service efficiency. \nEvent streaming ultimately enables companies to proactively respond to data-driven insights, fostering better business agility and service quality.\nHarnessing the Power of Oracle Integration Cloud and Apache Kafka for Event Streaming\nOracle Integration Cloud
(OIC) offers seamless integration capabilities with both cloud and on-premises
systems, supported by a wide range of pre-built adapters for various
applications. Apache Kafka’s architecture, optimized for high-scale and
high-performance environments, makes it ideal for organizations managing large
data volumes that require real-time insights.\nOIC’s toolkit extends to process automation, including workflow automation, approval routing, and task management. It simplifies data transformation with visual tools that map data
between systems, supporting conditional transformations. Kafka, in turn, brings
valuable features like event reprocessing and replay from specific points in
time, a critical function for fault-tolerant systems. This capability
complements OIC, which might otherwise need external storage for persistent
event handling.\nTogether, OIC and Kafka
create a powerful solution for modern, data-driven applications by combining
streamlined integration with resilient event streaming. This partnership
enables real-time data processing and efficient handling of large volumes,
enhancing agility and responsiveness across interconnected systems. For
example, in high-demand sectors like oil and gas, the OIC-Kafka combination
facilitates real-time monitoring of machinery and supply chains, enabling quick
responses to operational changes and ensuring continuous production. Businesses
seeking to optimize data flows and build scalable, reactive applications will
find OIC and Kafka to be a highly effective solution.\nEfficient Integration for Business Scenarios with OIC and Kafka\nOIC and Apache Kafka
provide a modern event-processing platform that meets diverse business needs
across industries. With OIC’s prebuilt adapters and Kafka’s effective
partitioning and topic management, this integration provides a flexible,
scalable solution for real-time data routing, workflow approvals, and business
flows. \nFor example, when Kafka
streams its data, OIC can trigger workflows to calculate energy consumption,
bill customers in real time via SMS or email, grid sensors disruptions and—if irregular
data points are detected—OIC workflows can initiate an alert for field teams to
investigate potential issues.\nApache Kafka’s
reprocessing and replay features make it an ideal choice for fault-tolerant
applications, maintaining data continuity during downtimes or recovery.
Although OIC does not inherently store messages, Kafka’s event retention allows
for long-term data storage and replay, adding resilience to event-driven architectures.
This pairing of OIC’s real-time integration strengths with Kafka’s fault
tolerance and data persistence is invaluable for modern applications.\nConnectivity between OIC and Kafka Streaming\nConnectivity between OIC
and Kafka is achieved through OIC’s prebuilt adapters, including the Apache
Kafka Adapter and Confluent connectors. These adapters enable direct
integration with Kafka, facilitating essential operations and enhancing
real-time data streaming and processing capabilities across platforms.\nThis seamless
connectivity is especially advantageous for companies requiring immediate data
availability across platforms such as Azure Event Hub. By leveraging Kafka’s
streaming capabilities alongside OIC’s integration, businesses in sectors like
healthcare can improve patient care through real-time data sharing across
applications. This direct connectivity ensures that systems can communicate
without time delays, helping organizations maintain data accuracy and operational
efficiency. As event data flows seamlessly, business ecosystems become more
responsive to changes, maintaining high performance across multi-cloud or
hybrid environments.\nTo ensure secure,
scalable integration connection, it’s essential to implement encryption,
authentication, and strong monitoring practices. Both OIC and Apache Kafka
provide advanced encryption capabilities including SASL, SSL/TLS, TDE and SFTP
encryptions to protect data throughout its lifecycle, from transfer to storage,
making them suitable for handling sensitive data across various industries,
including finance, utilities and healthcare.\nThe Future of Event Streaming with OIC and Kafka\nFuture advancements in
OIC and Kafka promise even tighter integration and more sophisticated event
handling, setting the stage for innovative applications across industries. \nFor sectors like utilities
and logistics, these advancements will mean greater accuracy in real-time
monitoring of infrastructure, tracking supply chains, and managing production cycles,
ensuring minimal delays and optimized resource allocation. \nAs the event streaming
landscape evolves, businesses that adopt and integrate these tools effectively
will be positioned to lead in their markets, harnessing real-time insights to
drive innovation and maintain a competitive advantage. Embracing the future of
event streaming with OIC and Kafka enables organizations to stay ahead of the
curve, adapting seamlessly to an ever-changing, data-driven world.\n


Integrating Oracle Integration Cloud (OIC) With Apache Kafka For Event Streaming

By Ashwin Kota, HEXstream integrations and analytics lead \nElectricity is something we often take for granted. When we flip a switch, we expect the lights to turn on and the TV to hum to life. However, maintaining this consistent flow of power is no easy task. Utilities work hard to keep power reliable and avoid interrutions. \nBut how do they measure how well they’re doing?\nIn this article we’ll take a look at four key metrics that utilities use to measure the reliability of electric systems: SAIDI, SAIFI, CAIDI and MAIFI. Let’s see what these metrics mean and why they matter to all of us.\nWhy Track Reliability Metrics?\nReliability in the electric system is all about the dependability of the power supply. Tracking reliability metrics helps utilities figure out where they need to make improvements. The goal is to keep power interruptions—also known as outages—as rare and short as possible. By studying reliability, utilities can see where they are doing well and where they need to make changes. This improves customer satisfaction, as fewer people experience the inconvenience of outages.\nUnderstanding the Key Reliability Metrics\nWhen it comes to reliability, there are four main metrics utilities look at. These metrics, often created using outage data, help utilities understand both the frequency and the duration of interruptions.\nSAIDI (System Average Interruption Duration Index):\n SAIDI measures the average amount of time that customers experience outages in a given year. If the SAIDI score is 120 minutes,that means the average customer experiences a total of two hours of outages each year. Lower SAIDI values mean fewer or shorter outages, which is a good thing.\nSAIFI (System Average Interruption Frequency Index):\n While SAIDI focuses on how long outages last, SAIFI looks at how often they happen. If the SAIFI score is 1.5, it means that, on average, customers experience about 1-2 outages per year. Like SAIDI, a lower SAIFI score is better, as it means fewer interruptions.\nCAIDI (Customer Average Interruption Duration Index):\n CAIDI tells us how quickly the utility can restore power when an outage does happen. To get CAIDI, divide SAIDI by SAIFI; f SAIDI is 120 minutes and SAIFI is 1.5, CAIDI would be 80, meaning the average time to restore power is around 80 minutes. Utilities aim to keep CAIDI as low as possible so customers don’t stay without power for long.\nMAIFI (Momentary Average Interruption Frequency Index):\n Unlike the other three metrics, MAIFI focuses on very brief interruptions (those lasting less than five minutes). These short interruptions are common but can still be annoying. MAIFI helps utilities track these momentary blips in service and figure out ways to reduce them.\nThe Role of Major Event Days (MEDs)\nNot all outages are created equal. Some are caused by routine issues, like equipment failures, while others happen due to large-scale events, like major storms, earthquakes, or wildfires. These large, unpredictable events are called Major Event Days (MEDs). Since these events can cause big disruptions, utilities separate them from the usual daily data to get a clearer picture of regular performance.\nLet’s break down how each metric is calculated with and without MEDs:\nSAIDI\nWith MEDs: When we include MEDs, SAIDI will be higher because these big events cause longer outages. This version shows the impact of rare but high-impact events on the system.\nWithout MEDs: By removing MEDs from SAIDI, we get an average that shows how the system performs on a normal day. This helps utilities understand the typical reliability of their system without the influence of extreme events.\nSAIFI\nWith MEDs: When calculated with MEDs, SAIFI shows how often outages happen, including during extreme events. This helps utilities plan for responses to emergencies.\nWithout MEDs: Removing MEDs from SAIFI shows how often interruptions occur in normal conditions. A lower SAIFI score here is a good sign of the system’s stability day-to-day.\nCAIDI\nWith MEDs: Including MEDs in CAIDI reveals how quickly power is restored after any type of outage, including major events. This gives an idea of how well a utility can respond during emergencies.\nWithout MEDs: Without MEDs, CAIDI shows how efficiently outages are resolved in regular situations. A lower CAIDI here suggests a utility is well-prepared to handle everyday interruptions swiftly.\nMAIFI\nWith MEDs: MEDs have less of an impact on MAIFI, as momentary outages are usually short and not caused by major events.\nWithout MEDs: Since MAIFI focuses on brief interruptions, it remains mostly stable regardless of MEDs. This score primarily helps track minor reliability issues that occur during normal operation.\nThe Difference Between With and Without MEDs is Important\nBy looking at metrics both with and without MEDs, utilities get a fuller picture of their grid’s reliability. Here’s why it matters:\nWith MEDs: Including MEDs in calculations shows the utility’s ability to handle severe disruptions. It helps the utility understand the grid’s resilience and prepare for worst-case scenarios.\nWithout MEDs: Calculations without MEDs provide a baseline, showing how well the system works under normal circumstances. This version of the metrics helps utilities improve routine reliability by targeting areas that need attention.\nTogether, these versions help utilities balance both daily reliability and readiness for rare events, enabling a more reliable and robust grid overall.\nHow Utilities Use Reliability Data\nThe information from these metrics doesn’t just sit on a shelf. Utilities use reliability data to make decisions about where and how to invest in the grid. Here are some examples:\nTargeting Problem Areas: By identifying the circuits with the worst SAIDI and SAIFI scores, utilities can focus their efforts on areas that need the most improvement. These “worst-performing circuits” often become the top priority for repairs and upgrades.\nPlanning for Future Needs: In regions where outages are more common, utilities can strengthen infrastructure or install new technology to prevent future interruptions. This could include adding automated systems that make it easier to detect and respond to outages quickly.\nTracking Improvement Over Time: By comparing metrics over the years, utilities can measure the effectiveness of their investments. A lower SAIDI or SAIFI over time shows that their reliability projects are working, which is a positive sign for both the utility and its customers.\nMoving Toward a Reliable Future\nReliability isn’t a one-time fix; it requires constant attention and improvement. By using these metrics, utilities can understand the strengths and weaknesses of their systems. This knowledge helps them make informed decisions that lead to a better, more dependable power supply. For customers, it means fewer power interruptions and quicker service restoration when outages do happen.\nIn short, SAIDI, SAIFI, CAIDI and MAIFI are the cornerstones of electric-system reliability. By keeping an eye on these metrics, utilities can keep power flowing smoothly and ensure that we rarely have to think about what it takes to keep the lights on.

Navigating The Modern Grid: A Deep Dive Into Utilities’ Reliability Metrics SAIDI, SAIFI, CAIDI & MAIFI

By Satish Saini, HEXstream utilities industry specialist  \nRecent Hurricanes Helene and Milton have highlighted how extreme weather events are affecting America’s power transmission and distribution infrastructure. (In a nutshell, they are exposing serious cracks.)   \nThe efficient management of fleet and field-crew services and resources are critical for quick power restoration, to minimize outages and to ensure safety for citizens. But efficient management isn’t always in place. Smart utilities are leveraging advanced technologies through coordinated and collaborated efforts, enabling them to effectively launch large-scale recovery operations based on improved decision-making insights. \nLet’s explore some of the key technologies being used to manage fleet and field-crew services efficiently: \nGeographic Information Systems (GIS) \nprovide spatial visualization and mapping capabilities, helping identify and prioritize areas impacted by hurricanes with meaningful information on affected grid assets, customer pockets and track-crew locations in real time with updated estimated times of restoration (ETRs). \nMobile workforce & fleet-management systems \nhelp\n \nstreamline field operations by providing dispatchers and field crews with real-time updates, job assignments, and work-order management during emergencies. Equipped with GPS and telematics, these systems include capabilities like mobile workorders, automated job-scheduling, real-time communication, and route-optimization for fleets to access assignments, to report progress, and to update the status of restoration tasks. \nAdvanced Distribution Management Systems (ADMS) including Outage Management (OMS) \nplatforms integrate various utility operations with detailed information on grid assets and infrastructure, including real-time monitoring of the distribution network, fault location and isolation, and some automated switching operations. These systems help utilities direct field crews to the most critical areas, reducing outage duration by detecting which feeders, circuit sections and assets are out of service and suggesting the best course of action to restore power, which guides field crews accordingly. \nAdvanced Metering Infrastructure (AMI)\n data through relevant analytics plays a key role in enhancing outage detection, response and restoration efforts for utilities. By leveraging real-time data from customer smart meters along with their exact locations, utilities can improve operational efficiency, efficiently managing field crews and resources to reduce downtime and enhance customer satisfaction. \nDrone and aerial-inspection technology \nequipped with high-resolution cameras and thermal sensors have the capability to inspect power lines and infrastructure for damage in hard-to-reach or hazardous areas, reducing the need for manual inspections. These tools quicken damage assessment. \nData analytics & data-management systems \nthrough data-storage\n \nplatforms enable utilities to store, access, share and analyze critical data related to network assets, outage history, past events and operational data and crew assignments. Cloud infrastructure ensures data availability and continuity even during power outages, which are key elements in disaster recovery. In particular, cloud-based storage systems equipped with efficient data-synchronization provide a centralized hub for data-sharing and collaboration, ensuring that all crews, whether on-site or remote, are working with consistent and up-to-date information. \nSome of the leading technology solutions and service providers for utilities include ADMS and OMS by various vendors like ABB, GE, Shneider and Survalent. Oracle Field Service Cloud offers end-to-end field-service management with real-time scheduling, routing, and mobile capabilities. IBM Maximo is a comprehensive asset and workforce-management solution that supports field operations and integrates with other enterprise systems.  \nAnd HEXstream’s Utility360 combined with our AMI Analytics offering is an efficient solution to effectively manage field crews, outages ETRs and reliability indices reporting. \nData management and analytics play a pivotal role with the massive volumes of data generated by multiple sources at modern utilities; this data is related to operations, outages, asset-management, field crews and workforce management, and customer-information systems. \nHEXstream, with our decades of expertise in all of these fields, can guide utilities to not just weather the current turbulent state of utilities, but absolutely thrive within it.  \n\nWANT YOUR FIELD CREWS TO WORK SMARTER? CONNECT WITH US HERE TO LEARN HOW HEXSTREAM CAN HELP.\n

Work Is Getting Harder For Fleets And Field Crews, Their Tools And Technologies Are Getting Smarter

By Ashwin Kota, HEXstream integrations and analytics lead \nIn the world of databases, especially with Oracle, there are several features designed to keep data safe and operations smooth. One critical feature is Oracle Archive Logs. Understanding what archive logs are, how they work, and the benefits and challenges they bring can help us see just how valuable they are for data management.\nWhat are Archive Logs?\nArchive logs are essentially copies of Oracle’s redo log files. Redo logs capture every change made to the database, helping Oracle keep track of transactions. When these redo logs fill up, Oracle “switches” to a new redo log file, and the filled log file is then “archived.” Archiving these logs means storing them in a safe location on the disk rather than overwriting them.\nIn simple terms, archive logs act like a historical record of all database transactions. They help create a trail of changes, which is invaluable for data recovery, point-in-time restoration, and much more.\nThe Lifecycle of Redo and Archive Logs\nHere’s a quick look at the stages of redo and archive log management:\nRedo Log Generation: As transactions are processed, changes are recorded in redo logs.\nLog Switching: When the redo log file fills up, Oracle switches to a new one.\nArchiving: The filled redo log file is then archived, creating an archive log.\nLog Cleanup: After successful backups, archive logs can be deleted to free up storage space.\nWhy are Archive Logs Used?\nData Recovery: Archive logs are crucial for recovering lost data. If there’s a system failure or accidental deletion, archive logs can restore the database to a previous state by applying the changes from the archived logs.\nPoint-in-Time Recovery: Archive logs allow for precise recovery to a specific moment. If an error occurs, the database can be restored to just before that time. This is valuable for handling unintended changes or even malicious actions.\nContinuous Backup: Oracle archive logs support backups while the database is live, allowing for continuous operation without downtime. A full backup combined with archived logs means you can restore data to any point after the backup.\nData Replication: Archive logs can replicate data to another database, creating a standby database for disaster recovery or business continuity.\nAdvantages of Archive Logs\nEnhanced Data Security: Archive logs improve data security by enabling a complete history of changes. In case of errors, the database can be restored with minimal data loss.\nFlexible Recovery Options: Archive logs support point-in-time recovery, letting administrators choose the exact state to which the database is restored. For example, if a ransomware attack occurs, archive logs can restore the database to its state before the attack, protecting data integrity.\nReduced Downtime: Because archive logs support live backups, businesses with high availability needs (like banks and e-commerce platforms) can avoid long downtimes.\nImproved Disaster Recovery: By leveraging archive logs, businesses can replicate data to another database at a different location. This ensures that, even in the event of a major disaster, data can still be retrieved.\nDisadvantages of Archive Logs\nHigh Storage Needs: Archive logs take up significant space, which can affect storage costs and capacity. Regular cleanup and storage management are necessary to prevent archive logs from overwhelming server space.\nPerformance Overhead: Managing archive logs requires additional processing power, which can slightly harm overall database performance.\nManagement Complexity: Setting up archive logs can be complex, requiring careful configuration and monitoring. Ensuring proper storage allocation, monitoring, and cleanup processes adds complexity.\nTips for Managing Archive Logs Effectively\nMonitor Storage Usage: Regularly check the space used by archive logs. Use the following command to viewyour archive log destination:\nsql\nCopy code\nSHOW PARAMETER LOG_ARCHIVE_DEST;\nAutomate Backups: Backing up archive logs regularly is essential for both data recovery and space management. You can automate backups using RMAN (Recovery Manager):\nsql\nCopy code\nRMAN> BACKUP ARCHIVELOG ALL;\nAutomate Log Deletion: To prevent archive logs from filling up storage, configure automatic deletion of old logs. The following command deletes archive logs older than seven days:\nsql\nCopy code\nDELETE ARCHIVELOG ALL COMPLETED BEFORE
'SYSDATE-7';\nAlways ensure you have a recent backup before deleting old logs.\nUse a Retention Policy: Set a retention policy for archive logs to keep only the logs needed for the most recent recovery point. This approach helps save space and simplifies log management.\nEnable Archiving Carefully: Enabling archiving requires a few simple steps, but it is essential to do it correctly to avoid errors:\nCheck the Archive Log Mode:\nsql\nCopy code\nARCHIVE LOG LIST;\nShut Down and Mount the Database:\nsql\nCopy code\nSHUTDOWN IMMEDIATE;\nSTARTUP MOUNT;\nEnable Archive Logging:\nsql\nCopy code\nALTER DATABASE ARCHIVELOG;\nOpen the Database:\nsql\nCopy code\nALTER DATABASE OPEN;\nHow to Use Archive Logs for Replication\nIn environments that require high availability, archive logs can be used to replicate the database to a secondary location. This replication helps in setting up a “standby” database, which is a copy of the primary database kept up-to-date by continuously applying changes from archive logs. This setup provides redundancy and allows the secondary database to take over if the primary database fails.\nPractical Scenarios for Archive Logs\nRecovering from Accidental Deletion: If a user accidentally deletes data, archive logs enable recovery to just before the deletion.\nProtection Against Ransomware: If ransomware affects a database, archive logs can be used to roll back to a time before the attack, preserving data integrity.\nDisaster Preparedness: In areas prone to natural disasters, archive logs facilitate quick replication to an off-site database, ensuring data is safe even in emergencies.\nConclusion\nOracle archive logs are a powerful tool in the world of data management. They are essential for ensuring data integrity, supporting flexible recovery options, and maintaining efficient backup processes. With proper configuration and management, archive logs provide enhanced security, reduced downtime, and robust disaster-recovery options, making them invaluable for any critical database environment.\nIn today’s data-driven world, effective use of Oracle archive logs can transform your approach to data management. By enabling archive logging and following best practices for monitoring, backing up, and cleaning up logs, businesses can achieve a highly reliable, secure and resilient database system.\n\nLEARN HOW ORACLE ARCHIVE LOGS CAN BE USED AT YOUR UTILITY. CLICK HERE TO CONNECT WITH US. \n


The Power Of Oracle Archive Logs: Essential For Data Recovery And Beyond

By Ashwini Nagendra Prasad, HEXstream lead—Integrations Center of Excellence\nProper IT/OT integration ensures all the systems within an enterprise work in harmony. It boosts productivity and enhances utilities’ day-to-day operations. The goal of integration is to streamline and simplify communication between systems while solving the application-silo problem.\nIntegration is not just about merging technologies or systems; it’s about aligning all aspects of the business—people, processes and culture. A holistic integration approach focuses on the synergy between different departments, ensuring they work together smoothly from day one. By addressing all components of the business, organizations can achieve deeper, more sustainable integration.\nBut if we all agree on the importance of integration, why do so many of us struggle to achieve it?\nI see many companies struggle, since they often simply overlook the importance of integration. Integration can be underestimated as simply data exchange. Companies often fail to implement proper integration strategies. Also, integrating for the sake of technology alone is a missed opportunity; integration should be a means to achieve meaningful business outcomes, not just another box to check.\nWhy Traditional Integration Falls Short?\nTraditional integration strategies have often been linear, focusing primarily on merging systems and aligning processes with limited consideration for other factors like vision, employee engagement and sustainability. While these methods can provide short-term operational efficiency, they often ignore the broader, long-term needs of the organization. Blaming the product for the shortcomings and resistance to changes affects Integration when in reality the integration strategy—not the product—it to blame.\nHow to Approach Integration with a Fresh Perspective?\nTo create a more sustainable and effective integration strategy, organizations must rethink their approach. Here are key elements of integration with a fresh perspective:\nStart with a clear vision: Companies must clearly define what integration will entail at their enterprise, uUnderstanding the business drivers, scope and scale of integration while assessing the current architecture and existing systems.\nInvolve stakeholders early: Engage business teams, operation teams and other stakeholders early on and gather and utilize diverse perspectives.\nLeverage the technology: Designing the correct integration architecture that includes the right type of data exchange/data enrichment/data transformation, the right integration patterns, robust error-handling and exception management, and scalability and performance.\nEmphasize security and compliance to include comprehensive security measures and robust compliance protocols for communcation.\nScalability and future-proofing ensures your approach is change-adaptable and there is room for future extensions and improvements.\nIT/OT integration is complex. That’s just a fact. And while they often focus on technical tasks, companies must not forget the broader, holistic aspects of integration. To maximize the benefits of integration, companies should adopt a comprehensive, strategic approach that considers all facets—technical, business and operational—throughout the entire lifecycle of the project.\nHow is your organization approaching integration? What challenges have you encountered and what strategies have you implemented? Are you struggling with your integration (be it cloud or on-prem or custom built)? I’d love to connect with you to help define the right integration strategy for you and help define the standards and governance needed to standardize your process for the entire project lifecycle. \nClick here to contact me.\n

Rethinking Our Approach To IT OT Integration

By Ashwin Kota, HEXstream integrations and analytics lead \nIn today’s utility landscape, ensuring service reliability and effective outage management is essential to meet the demands of regulators and customers. Utility360 is designed to help utilities streamline their data-handling processes (especially for tracking outages) and ensuring compliance with reliability metrics. This post showcases how one utility leveraged Utility360’s ETL capabilities to significantly improve data accuracy, efficiency and customer service.\n1. Improved Reliability with Reduced Data-Processing Times\nThis utility company had previously encountered challenges recalculating outage data, as frequent updates placed a heavy load on their resources. Every recalculation affected the entire dataset, causing delays and limiting metric accuracy. With the Reliability Lock Date feature implementation in Utility360, the utility could isolate recalculations to only the most recent records, reducing processing times and preserving historical data integrity. This change freed up resources to address current outages more efficiently and provided more accurate, real-time metrics without compromising system performance.\n2. Enhanced Data Quality through Automated Audits\nMaintaining data consistency and accuracy can be demanding, especially when manually auditing large datasets. By introducing Audit Dashboard, the utility automated daily checks for discrepancies, such as missing entries or duplicates. This automation greatly reduced the time spent on manual data reviews, enabling the team to focus on priority tasks and ensuring stakeholders received accurate, timely information. Data reliability improved significantly, enhancing confidence in the utility’s reports.\n3. Efficient Outage Management with Switch-Sheet Merging\nDuplicate outage records across various data sources led to inefficiencies and skewed metrics. The Switch-Sheet Merging feature implementation in Utility360 enabled this utility to consolidate duplicate entries, creating a single, unified view of each outage event. This consolidation led to more accurate metrics, particularly for indices like SAIDI, SAIFI and MAIFI, and gave the operations team a clearer understanding of service interruptions. With streamlined records, the team could make faster, data-driven decisions that improved service reliability.\n4. Compliance Improvement with Momentary Outage Exclusion\nTo meet regulatory standards, utilities often need to report only sustained outages. However, including momentary outages (under 60 seconds) previously led to inflated metrics. The Momentary Outage Exclusion feature in Utility360 automatically filtered out these brief outages, ensuring that only relevant data was used in reliability calculations. This enhanced regulatory compliance, provided more accurate reports, and helped maintain a clearer view of genuine service quality.\n5. Real-Time Insights through Customized Dashboards\nOutage management relies on having immediate access to current data. The customizable dashboards in Utility360 allowed this utility’s team to monitor outages, customer impacts, and performance trends in real-time. By tailoring these dashboards, the team could quickly view the most relevant information for rapid decision-making. Over time, the dashboards revealed patterns in outage data, supporting proactive maintenance and improving resource allocation.\n6. Accurate Customer Impact Metrics\nRepeated outages affecting the same customer within short timeframes can distort reliability metrics. The 5-Minute Consolidation Rule logic in Utility360 grouped such outages within a five-minute window as single events, providing a realistic picture of customer impact. This rule improved customer satisfaction by enabling the utility to respond accurately to concerns and present a more truthful measure of service quality, avoiding the misleading effects of inflated metrics.\n7. Incremental Data Loads and Efficient Reporting with Reliability Lock Date\nLarge volumes of outage data can place substantial demands on system resources. With incremental data loading and the Reliability Lock Date feature, Utility360 helped this utility limit recalculations to recent data only, reducing processing load and preserving historical-data integrity. \nThis approach supported consistent, reliable reporting while also saving on computational costs, allowing the utility to analyze long-term trends and inform infrastructure-planning decisions.\nConclusion
\n
By implementing Utility360, this utility transformed its approach to reliability and outage-data management. Key features like automated audits, custom dashboards, and incremental data recalculations enhanced the accuracy of metrics, streamlined operations, and improved compliance. \nOverall, Utility360’s robust ETL capabilities positioned the utility as a proactive, data-driven leader, well-prepared for the challenges of today’s dynamic energy landscape.\n\nCLICK HERE TO CONNECT WITH US TO LEARN MORE ABOUT HOW UTILITY360 CAN OPTIMIZE YOUR OPERATIONS.\n \n


Enhancing Reliability And Outage Management With Utility360’s ETL Tool

By Ashwin Kota, HEXstream integrations and analytics lead \nAn execution plan is essentially a roadmap that the Oracle Database follows to execute a SQL statement. It outlines every step, from how the data will be accessed to the order of operations and resources required. Execution plans are generated by the Oracle Optimizer, which carefully evaluates the SQL statement to find the most efficient path for retrieving data.\nUnderstanding execution plans is crucial because they can significantly improve database performance, guide effective resource management, and help troubleshoot slow queries.\nHow to Generate an Execution Plan\nTo start analyzing a query, you’ll need to generate its execution plan. Here’s how to do it in Oracle SQL:\nWrite the SQL Query: Begin with the SQL statement you want to analyze.\nUse the EXPLAIN PLAN Command: Before your SQL statement, add the EXPLAIN PLAN FOR command. For example:\nsql\nCopy code\nEXPLAIN PLAN FOR SELECT * FROM employees WHERE department_id = 10;\nDisplay the Execution Plan: After running the EXPLAIN PLAN command, you can view the execution plan by running:\nsql\nCopy code\nSELECT * FROM TABLE(DBMS_XPLAN.DISPLAY());\nFor those who prefer visual aids, tools like Oracle SQL Developer and Toad for Oracle provide graphical interfaces to view and analyze execution plans with useful visual metrics.\nKey Components of an Execution Plan\nAn execution plan consists of various components that give insights into how a query will be executed. Here are some key terms:\nCost: A numeric indicator estimating the resources required for the query. Lower costs typically suggest a more efficient plan, though the cost alone isn’t always the best metric for performance.\nRows: This is an estimate of the number of rows processed at each step. If the estimate is high, it could indicate a performance issue, especially if fewer rows are needed.\nOperation: The actions Oracle will perform, including:\nTABLE ACCESS FULL: Reads all rows in a table. While common, full table scans can be costly for large tables.\nINDEX RANGE SCAN: Uses an index to retrieve a range of rows, usually faster than a full table scan.\nJOIN: Shows how tables are joined (e.g., nested loop, hash join), which can impact performance based on the join type and data structure.\nThese elements are organized hierarchically, showing the flow of data and how operations interact. Understanding this helps pinpoint performance bottlenecks.\nAnalyzing Execution Plans for Performance Improvement\nOnce you have generated an execution plan, the next step is analysis. Here are some techniques for identifying performance issues and improving query efficiency:\nIdentify High-Cost Operations\n—High-cost operations are typically the biggest contributors to slow query performance. If you see an operation with a cost much higher than others, it’s likely a bottleneck. Common high-cost operations include:\nFull Table Scans: If the plan shows a TABLE ACCESS FULL on a large table, check if an index could be used to reduce the number of rows scanned.\nExpensive Join Operations: Some joins, like nested loop joins on large tables, can slow down queries. Consider restructuring the query or changing the join type (e.g., hash joins may be more efficient for large datasets).\nEvaluate Join Methods\n—Joins are often at the heart of complex queries, and inefficient joins can drastically impact performance. Here are some common join types in Oracle and when they might be used:\nNested Loop Joins: Typically used when one table is much smaller than the other. This join type works well with indexed tables but may slow down for larger tables.\nHash Joins: Useful for large datasets without indexes. Hash joins create an in-memory hash table, allowing for faster matching.\nIf a join method appears inefficient, consider altering the join type or adding an index to the joined columns.\nCheck for Full Table Scans\n—Full table scans occur when Oracle reads every row in a table. While full scans are sometimes necessary, they can slow down performance, especially on large tables. If a full scan appears avoidable, try:\nAdding Indexes: For columns frequently used in WHERE clauses or joins, indexing can help avoid full scans.\nRewriting Queries: Sometimes restructuring a query or breaking it into smaller parts can reduce the need for a full scan.\nCompare Execution Plans for Query Variants\n—Run the same query with slight modifications, such as different WHERE clauses or added indexes, and compare execution plans. This approach allows you to identify the variations that yield the most efficient plan.\nLook at Estimated Rows vs. Actual Rows\n—The estimated number of rows processed can help predict how well Oracle’s optimizer understands the data. If the estimates seem off (e.g., estimating far more rows than expected), it might be helpful to update statistics on the table or index.\nImproving SQL Performance Using Execution Plans\n—Execution plans can guide various
performance-improvement strategies. Here are a few steps to enhance SQL performance based on insights from execution plans:\nOptimize Index Usage: If a query shows frequent table scans, consider creating indexes on frequently queried columns.\nUpdate Statistics Regularly: Outdated statistics can lead to inaccurate estimates. Regularly update statistics so that the optimizer has an accurate view of table contents, which helps create efficient plans.\nRewrite Complex Queries: If the query is overly complex, try breaking it into simpler steps or using temporary tables. This can reduce the overall load and improve readability.\nUse Bind Variables: Bind variables reduce parsing and can improve performance for frequently run queries by reusing execution plans.\nCheck for Inefficient Nested Loops: Nested loops are sometimes chosen incorrectly by the optimizer. If this appears in the plan and doesn’t make sense given the data volume, consider using hints or adjusting indexes to guide the optimizer towards a more efficient join type.\nIdentifying Root Causes of Performance Issues Using Execution Plans\nExecution plans provide clues for diagnosing performance bottlenecks. Here’s a strategy for identifying root causes:\nExamine Cost and Rows: High-cost operations and high row estimates are primary indicators of inefficiency. Focus on reducing these areas first.\nEvaluate Join Efficiency: Ensure that join methods match the data size. If joins seem to be the issue, experiment with indexes on the join columns or alternative join methods.\nCheck Wait Events: Use Oracle’s SQL Trace and TKPROF tools to analyze SQL execution and identify wait events. This information, along with the execution plan, helps to see where resources are consumed.\nCompare Execution Plan Before and After Changes: When you make an adjustment (e.g., adding an index), generate the execution plan again to confirm that it improves performance.\nConclusion\nUnderstanding execution plans in Oracle SQL is essential for optimizing query performance and ensuring efficient resource use. By carefully analyzing execution plans, database administrators and developers can identify bottlenecks, reduce costs, and keep the database running smoothly.\nRegularly reviewing execution plans not only improves individual query performance but also contributes to the overall health of the database. As you gain experience with execution plans, you’ll find that they offer valuable insights into query optimization, helping you make data-driven decisions for better database management.


Understanding Execution Plans In Oracle SQL...A Guide To Performance Optimization

By Swathi Ambati, HEXstream data solutions engineer\n \n“We’re now building a lot of our applications in APEX. And our productivity gains are, again, a factor of 10. And we build the applications in one tenth the time, or one tenth the amount of people, or at one tenth the cost. But these are not typical low-code applications. These are applications that can scale to millions of users and all over the world.”  Larry Ellison, Oracle founder and CTO \nIf you’ve heard of Oracle APEX but aren’t quite sure what it is, what it does, or how and where you can use it, you're in the right place. This article will provide clear, straightforward answers to all your questions. \nThe ultimate low-code platform for web applications\n \nOracle Application Express (APEX) is a low-code development platform that enables users to build scalable, secure and data-driven web applications using only a web browser. APEX is fully integrated with the Oracle database and enables both developers and business users to create applications with minimal coding, making it an ideal solution for rapid application development. \nKey features of Oracle APEX\n \nSeamless integration with the Oracle database. \nNative Oracle database support—APEX runs directly in the Oracle database and ensures high performance. \nSQL, PL/SQL and REST API support—developers can easily access and manipulate data. \nAutomatic data synchronization and JSON support—facilitates real-time updates and seamless integration with external sources. \nEnterprise-class security\n \nRobust authentication and authorization controls—supports various authentication methods, including Single Sign-On (SSO) and OAuth 2.0. \nIntegrated protection against SQL Injection & Cross-Site Scripting (XSS)—ensures secure application development without additional configurations. \nComprehensive auditing and session management—helps monitor and track application activity. \nPerformance\n \nOptimized for high performance—runs natively within the Oracle database and eliminates the need for middleware. \nHandles thousands of concurrent users—ideal for high-volume enterprise applications. \nSupports cloud and on-premises deployment—easily deploy applications in the Oracle Cloud or in an on-premises environment. \nHow does Oracle APEX work\n? \nOracle APEX follows a simple three-tier architecture that ensures efficient and fast application execution: \nWeb browser: Users interact with the APEX application through a browser. \nOracle REST Data Services (ORDS): ORDS acts as a bridge and forwards requests from the browser to the Oracle database. \nOracle database: The entire business logic, data-processing and rendering takes place in the database. The response is then sent back to the browser. \nThis architecture offers latency-free data access and ensures maximum performance and scalability for APEX applications. \nCommon use cases for Oracle APEX\n \nAPEX is used across industries to create modern, data-driven applications. Here are some of the most common use cases: \nBusiness applications: Internal tools, HR portals and CRM (Customer Relationship Management) applications. \nData reports and dashboards: Easy creation of interactive reports and real-time analytics. \nWorkflow automation: Automate business processes with APEX processes and dynamic actions. Modernizes legacy systems by converting outdated Excel spreadsheets, forms and reports into web-based applications. \nPublicly accessible applications: Develops self-service portals for customers, vendors and suppliers. \nCloud and hybrid deployments: Deploys applications in the Oracle Cloud, on-premises or in hybrid environments. \nQuick demo:\n Create an APEX application in minutes! Oracle APEX makes application development incredibly fast. With just one simple data set you can upload data, define relationships and configure UI components in minutes. Take a look: \n\nThis is a quick and efficient way to create an app from a .csv file. Not only does it generate the app, but it also creates the necessary database table to store the data. I downloaded a dataset from \nData.gov\n, and within minutes, I had a comprehensive overview of elevator and escalator outages across all NYC stations over the years, along with a detailed report featuring advanced search and filtering options. \nWant to see a step-by-step tutorial on how to create an APEX application? Stay tuned for the next article, where we explore the power of APEX + AI! And \nclick here\n to connect with us about a Proof of Concept (PoC) tailored to your requirements using Oracle APEX!  

Ten Times Faster! Ten Times Cheaper! New Wins With Oracle APEX

By Devika Gayapu, HEXstream data-solutions engineer\nThe Oracle Business Intelligence Cloud Connector (BICC) is a specialized tool designed for efficiently extracting large volumes of business-intelligence data and related content from Oracle applications. Built to handle the heavy lifting of transferring vast amounts of data, Oracle BICC ensures that no data is lost during the process. It also offers robust security by adhering to the security standards of Oracle Applications Cloud.\nOracle BICC can manage large data volumes while supporting both full and incremental data extracts. It uses data stores to facilitate the movement of data. In simple terms, data stores act as intermediary storage areas, enabling staging, transformation, and finally loading of data to its destination, while maintaining data integrity and consistency throughout the process. By utilizing BICC Data Stores, organizations can automate the extraction process, scheduling both one-time and incremental extracts based on needs.\nOracle BICC can extract data from various Oracle products, such as Oracle Fusion Applications (including Oracle CX, Oracle HCM, Oracle ERP and Oracle SCM), and transfer it to external storage. These extracts can be either complete or partial and can be scheduled or run on-demand to ensure that data remains up-to-date without requiring manual intervention. BICC also supports incremental extracts, which capture only the data that has changed
since the last extraction. Additionally, Oracle BICC can monitor data extracts and check logs for updates.\nOracle BICC utilizes both SOAP and REST APIs for data extraction and integration. SOAP API is primarily used for scheduling and triggering BICC extracts, whether they are full or incremental, while the REST API offers more flexibility for real-time data integration. REST APIs are data-driven, enabling access to resources via URIs. SOAP, on the other hand, is more structured and focused on specific functionalities. \nWhile REST APIs are preferred for better performance and scalability—particularly for low-volume, real-time transactions or when integrating with external systems—SOAP is generally used for managing tasks like scheduling BICC extract jobs. REST is favored for direct data access and updates.\nOverall, BICC provides a streamlined and optimized approach to syncing data, making it easier for businesses to maintain accurate, up-to-date data for reporting and decision-making. \nAnd while Oracle BICC is currently used to extract data from Oracle Cloud applications like Oracle CX, Oracle HCM, Oracle ERP and Oracle SCM, in the future Oracle plans to utilize it in other functional areas, such as Oracle EWDI applications.\nWe’ll keep you posted. \n\nCLICK HERE TO CONNECT WITH US TO LEARN HOW BICC BEEN BENEFIT YOUR UTILITY.\n \n


Supercharged Data Extraction And Integration With Oracle BICC

By Bharath Moola, HEXstream Data Solutions Engineer\n\nOrganizations leveraging Oracle Analytics Cloud (OAC) often seek to integrate with Microsoft Power BI for enhanced visualization and self-service analytics. This blog outlines efficient ways to connect these platforms for seamless data analysis.\nUsing RPD in Power BI\nOracle Analytics Cloud utilizes the Repository Database (RPD) as a metadata layer to structure and manage data models. RPD plays a critical role in integrating OAC with Power BI by defining the logical relationships between data sources and ensuring seamless connectivity. By leveraging RPD, users can create subject areas, define calculations, and
establish security constraints before exposing the data to Power BI.\nConnecting Power BI to an RPD-enabled data source requires setting up an Oracle BI Server ODBC connection. Once configured, Power BI can access subject areas and logical tables designed in the RPD. This approach ensures that the predefined business logic and calculations embedded in the RPD are retained, providing a consistent analytical experience across both platforms. Additionally, using RPD allows organizations to manage role-based
security and data access policies centrally, ensuring compliance with governance standards.\nTo integrate the RPD with Power BI:\nConfigure Oracle BI ODBC drivers and set up connection details within Power BI.\nFetch structured data from OAC’s logical model.\nBuild Power BI reports without modifying the source structure.\nThis method is especially useful for organizations managing large and complex datasets, ensuring that data remains structured and governed efficiently.\nPrerequisites\nMicrosoft Power BI Desktop must be up to date:\n\nOracle Analytics Cloud should be running:\n\nDownload the latest version of the Oracle Analytics Power BI Connector:\n\n \n\n
Place the latest MEZ file in the following location for your user: \n( c: users/<username>/Power Bi desktop/customconnections):\n\nSteps to Connect Oracle Analytics Cloud to Power BI\nIn Power BI Desktop: Click on Options and Settings:\n\nUnder GLOBAL > Security: Select (Not Recommended) Allow any extension to load without validation or warning:\n\nAdjust memory settings:\nUnder GLOBAL > Data Load, set the value for Maximum memory used per
      simultaneous evaluation (MB) based on your machine’s available memory:\n\nObtain Power BI API Key from OAC\nLog in to OAC > Navigate to User Profile > Copy the Power BI API Key:\n\nLog in to Power BI desktop > Get Data > Search for Oracle Analytics >Select Oracle analytics (Beta) > Paste the API Key in Power BI when prompted:\n\nAuthenticate with OAC and complete the login process:\n\nClick Connect.\nAccessing Subject Areas in Power BI\nOnce connected, the screen below will be displayed:\n\nExample: Power BI Dashboard Built Using OAC Subject Areas\n\nIntegrating OAC with Power BI using RPD ensures a seamless transition of pre-modeled data while maintaining governance, security, and analytical consistency. By leveraging OAC’s logical layer, professionals can extract meaningful insights without compromising data integrity. With the right configurations, Power BI serves as a powerful front-end visualization tool while OAC manages backend data processing and modeling.\n\nLEARN HOW OUR TEAM CAN OPTIMIZE YOUR POWER BI EFFORTS. CLICK HERE TO CONTACT WITH US.\n


A Quick Guide: Integrating Oracle Analytics Cloud With Power BI

By John Apostolo, HEXstream application developer \n \nModern AI models face a paradox: achieving state-of-the-art performance requires immense computational power, yet the cost of scaling remains prohibitive. While dense architectures have traditionally set the standard for reliability and performance, they suffer from inefficiency at scale. Meanwhile as a counterbalance, Mixture-of-Experts (MoE) offers a promising path toward higher efficiency but introduces complexity in model routing. \nIn this post, we explore how a dual approach—refining dense models while leveraging MoE—enables AI to break through scalability limitations while maintaining performance and efficiency. We also examine the significance of high-quality pre-training and future-proofing techniques for long-context and multimodal AI. \nHigh-quality pretraining on curated data–the foundation of AI reasoning\n \nThere is a problem with unfiltered data.  \nMany open-source models rely on massive datasets scraped indiscriminately from the internet. While sheer volume can improve knowledge coverage, it also introduces noise, redundancy, and factual inaccuracies—leading to models that struggle with logical consistency and deep reasoning. \nRather than prioritizing scale over quality, a high-performing AI model should be trained on high-quality, diverse, and well-structured sources such as books, academic papers, and formally vetted content. Filtering out low-value internet chatter minimizes hallucinations and ensures the model learns concepts over memorization. \nWhy this matters\n \nHigher factual accuracy—reduces misinformation propagation. \nBetter logical reasoning—moves beyond surface-level pattern recognition. \nEnhanced multilingual support—ensures robust understanding across languages. \nRedefining dense model efficiency: smarter scaling over larger models\n \nThe problem: compute-heavy, unsustainable growth \nDense models, by design, activate all parameters for every token processed. While this guarantees consistency and predictability, it also leads to exponential compute costs as models scale. \nOptimized dense model design\n \nA breakthrough in dense architecture isn’t just about adding more layers or parameters—it’s about refining efficiency per token. By carefully optimizing: \nNetwork depth and width for better weight utilization. \nTokenization strategies to reduce redundant processing. \nMemory-efficient scaling techniques that allow for high performance at lower compute costs. \nThe outcome: new standards in dense AI \nThis approach achieves state-of-the-art coherence and reasoning without requiring the extreme sparsity of MoE, proving that dense architectures can remain viable when optimized correctly. \nMixture of experts (MoE): the key to unlocking scalable AI\n \nThe problem: scaling beyond dense models \nAs AI models surpass billions of parameters, activating every neuron for every token becomes impractical—leading to massive compute costs and energy inefficiencies. \nSo how does MoE solve this? MoE introduces a novel approach where only a fraction of the model’s parameters are used at a time, thanks to: \nSpecialized routing mechanisms that direct inputs to the most relevant “experts.” \nDynamic sparsity, allowing the model to selectively activate only the necessary computational pathways instead of the full network. \nAnd what is the impact of MoE? By adopting this selective activation strategy, models can scale beyond dense architectures without a linear increase in compute cost. This results in: 
✔ Greater model capacity—They can handle more complex reasoning tasks. 
✔ Lower computational demands—Reduces cost per inference. 
✔ Specialization without redundancy—Experts specialize in different aspects of knowledge, improving response accuracy. \nArchitectural synergy: when to use dense vs. MoE\n \nThe tradeoff: reliability vs. efficiency \nOrganizations developing AI solutions typically face a crucial tradeoff: \nDense models provide predictable, uniform activation—ideal for tasks where reliability, interpretability, and deterministic responses are necessary. \nMoE models are optimized for efficiency and scalability—better suited for large-scale inference where compute costs are a primary concern. \nThe benefit of offering both architectures\n \nBy developing both dense and MoE models, researchers and organizations gain: \nFlexibility to choose based on task complexity and computational resources. \nOptimal performance scaling—dense for high-precision tasks, MoE for high-scale efficiency. \nThe best of both worlds—reliable, full-parameter models alongside scalable, cost-effective MoE systems. \nFuture-proofing AI: long-context and multimodal capabilities\n \nThe challenge: memory limitations and cross-modal understanding \nMost AI models struggle with: \nLong-context retention—forgetting key details in extended interactions. \nMultimodal processing—seamlessly integrating text, image, and video understanding. \nTo create an AI model that is future-ready, advancements must include: 
✔ Extended context length support—ensuring information retention across long-form content. 
✔ Multimodal alignment techniques—integrating multiple data types for a cohesive understanding of text, images, and beyond. \nOutcome: versatile AI-ready for complex applications\n \nThe combination of: \nDense model precision \nMoE scalability \nMultimodal readiness \n…ensures AI is prepared for enterprise-scale applications, research, and next-generation problem-solving. \nWhy this dual approach is the future\n \nThe debate between dense vs. MoE is not about one replacing the other, but rather about how they complement each other in modern AI development. By: \nImproving dense-model efficiency \nLeveraging MoE for scalable reasoning \nIntegrating long-context & multimodal readiness \n…AI models can deliver superior performance while maintaining computational efficiency. \nThis dual-track approach ensures AI remains adaptable for both research and enterprise applications, future-proofing AI capabilities for years to come. \nWANT MORE? PART 2 OF THIS BLOG WILL SPOTLIGHT STRATEGIES TO REALIZE THES GAINS. STAY TUNED…AND \nCLICK HERE\n TO CONNECT WITH US TO LEARN HOW YOUR UTILITY CAN BRIDGE AI SCALABILITY AND EFFICIENCY. \n 

Bridging AI Scalability And Efficiency: The Synergy of Dense and Mixture-of-Experts Architectures

By Bharath Moola, HEXstream data solutions engineer\nIn today's data-driven world, industries like utilities, telecoms and service providers rely on analytics for operational efficiency, customer satisfaction, and decision-making. This you already know. One powerful tool for interactive data analysis is Power BI Q&A, which enables users to ask questions in natural language and receive visualized insights. This might be new to you. \nThis blog explores how to train Power BI Q&A for better results, using outage analytics as an example. \nWhat is Power BI Q&A? \nPower BI Q&A enables users to query data in plain English, generating charts, tables and graphs without complex DAX formulas. For instance, asking “What were sales last quarter?” returns relevant data instantly. However, to improve accuracy, especially with domain-specific data like outages, you need to train the Q&A model. \nExample: Outage Analytics Training \nImagine an outage dataset with control zones labeled as Levels. Power BI may not recognize this as a region or county, so training involves adding synonyms: 
Level1_name – Company, Level2_Name – Region, Level3_Name – Division, Level4_Name – County and Level5_Name – Substation\n\nWith these synonyms, a user could ask: "How many Outages occurred in Canton County in 2017?"\n\nOr "Show me all the outages by company in 2015."\n\nSimilarly, when configuring your data model, be sure to add synonyms (where relevant) for fields that have multiple references within your organization. For example, "Outage Data Set" might have multiple dates, but users want to filter by year or other time references, which is “Event Start Date,” so you may need to add synonyms. Power BI Q&A understands different ways of asking about dates.\nAdded Synonyms:\nEvent Start Date → \nYear, Start Date, Occurrence Date\n\nTips for Better Q&A Results\nUse Clear Field Names—Give your fields descriptive labels so Power BI Q&A can easily map user queries to the data.\nAdd More Synonyms Over Time—Monitor how users phrase questions; if Q&A struggles, add new synonyms that match how your team speaks.\nProvide Sample Questions—In Power BI Desktop, you can insert sample questions that guide users and improve Q&A accuracy.\nSet Up Default Aggregations—Assign default aggregations (e.g., sum, average) for numerical fields to ensure accurate calculations. \nEnable Suggestions—Allow Power BI to suggest questions, helping users frame queries correctly. \n\nTraining Power BI Q&A with synonyms and context-specific terms enhances its ability to deliver accurate insights. Regular updates ensure seamless, user-friendly data analysis, making it an invaluable tool for any industry. \n\nLEARN HOW OUR TEAM CAN OPTIMIZE YOUR POWER BI EFFORTS. CLICK HERE TO CONTACT WITH US.\n

Power BI Q&A: Training For Better Data Insights

By Satish Saini, HEXstream utilities industry specialist \nEarlier this month, US Secretary of Energy Chris Wright and few other senators participated in an artificial intelligence (AI) collaboration session involving Department of Energy (DOE) scientists and other professionals from AI technology field. They discussed the plans for the next four years to create America’s “New Golden Age,” win the AI race, and make US energy dominant again, noting that energy is essential to the nation’s security, the well-being of our citizens, and lives of people around the world.\nIn October 2024 last year, the DOE published “Artificial Intelligence and Transforming the Energy Landscape in US,” which detailed plans for applying AI to address key challenges across the energy sector through following key initiatives:\nModernizing the aging and complex electricity grid using AI-powered tools for enhancing grid resiliency, optimizing operations, efficient integration of renewable energy to the grid and improving load-forecasting and other operational data insights.\nAccelerating energy innovation through the development of new energy technologies in power generation and storage to meet energy goals, while using AI to automate processes and improving assets' lifecycle performance.\nImproving energy efficiency through AI-driven design, solutions and control systems for buildings, transportation, industrial processes and equipment through optimizing energy consumption.\n\nOur modern, complex energy and power system is triggering the need for automated tools and technologies with the rising electricity demand. It requires more penetration of distributed, renewable energy sources across various segments of the transmission and distribution grid, in reaction to the rising number of grid-connected devices stemming from the adoption of smart-grid technologies and behind-the-meter (BTM) systems like solar energy, storage and EV charging systems.\nFurther, climate changes and adverse-weather events are severely impacting the power-grid infrastructure, triggering the need for a more reliable and resilient grid. Operating this complex, vast and widely scattered grid with millions of devices and assets pose a big challenge for the utilities. You already know this; think planning activities for the complex grid, forecasting for load, predictive analysis and insights for assets performance and maintenance needs for these innovative tools and technologies. Also consider how regulatory compliance, safety standards, utility-business operations and sustainability requirements need efficient data-exchange and decision-making tools through the integration of various silos and platforms within the utility.\nSupporting all this in the energy and power system, AI and its applications are rapidly increasing in quick, accurate and automated data analysis and reporting. This is a good thing. AI algorithms and machine learning (ML) models are increasingly customized and advanced for power-system-specific applications and uses. These are becoming more effective and widely used in current utility transformation and grid-modernization efforts by utilities. \nIn short...they have to be. \nA massive amount of data is generated with the rising deployment of Distributed Energy Sources (DERs), Advanced Metering Infrastructure (AMI), Advanced Distribution Management System (ADMS), Smart Grid, Asset Performance Management (APM) and other technology platforms within T&D grids. This huge data from multiple platforms needs to be quickly and effectively integrated, processed and analyzed to help utilities in efficient decision making in operation and maintenance activities. \nManaging this much volume and integration cannot be done through manual processes. The use of AI and ML is providing a huge amount of support to utilities through advanced algorithms and machine-learning models.\nA report by International Energy Agency (IEA), in reference to an analysis from Indigo Advisory Group, suggests that AI is already serving more than 50 different uses in the energy system, from grid operation, assets management, load forecasting and others. The report further mentions that the market for AI in the energy sector could be worth up to USD $13 billion in the coming period. \nIncentives like that spur transformation and modernization efforts. \n\nContact us for further details as to how artificial intelligence can help your utility to effectively manage your critical assets and enhance your customers' experiences.\n\nUp next: Specific use cases for T&D utilities in relation to asset-performance insights, load and demand planning/forecasting, grid modernization and utility transformation. Stay tuned! 

The Role Of Artificial Intelligence (AI) In Utility Transformation And Grid Modernization

By Bala Sai Tilak.Thalla, HEXstream data integrations engineer\nOracle Integration Cloud (OIC) is a powerful Platform as a Service (PaaS) solution that simplifies applications and data integration across cloud and on-premises environments. It seamlessly connects homogeneous and heterogeneous applications, enabling organizations to streamline business processes and improve efficiency. \nHowever, organizations often face challenges in bridging gaps between disparate systems while ensuring scalability, security and performance. These challenges can be overcome. \nTo build a robust integration, it is essential to focus on three key aspects:\nSource system—The system from which data originates.\nTarget system—The destination where the data is delivered.\nMiddleware platform—OIC, which facilitates the integration process.\nKey considerations for effective OIC integrations\n1. Establishing secure connectivity with the connectivity agent\nMany enterprise applications still rely on on-premises systems and legacy applications. To securely integrate these with cloud-based applications, Oracle Connectivity Agent is used. This agent establishes a secure bridge between cloud and on-premises applications without
exposing internal systems directly to the internet.\n2. Handling large data volumes efficiently\nWhen integrating applications that handle bulk data, performance bottlenecks can arise. To optimize performance, enterprises should evaluate the system's processing capacity before integrating bulk data, then monitor throughput and response times to detect slowdowns.\n3. Optimizing performance with batch processing\nBulk data transfers can harm system performance. Instead of pushing large data loads at once, leverage batch processing by:\nSegmenting data internally within OIC.\nProcessing data in manageable batches instead of overwhelming the system.\nEnhancing efficiency by ensuring smooth data ingestion.\n4. Improving scalability with asynchronous integrations\nSynchronous integrations wait for an immediate response, which can result in failures if the response isn't received within 300 seconds. Instead, using asynchronous integrations (such as decouple processes) reduces system dependencies.\n5. Leveraging parallel processing for faster execution\nWith OIC Gen 3, parallel processing is now available, enabling actions to be executed simultaneously instead of sequentially. This reduces processing time by executing multiple tasks concurrently and ensures faster response times for the target system.\n6. Enhancing efficiency with scheduled integrations\nScheduling integrations is a best practice for automating routine data transfers. Use child integrations to consume and store data internally before triggering the scheduled integration;  this method ensures controlled execution and reduces system overload.\n7. Implementing a robust error-handling mechanism\nError handling is a crucial component of any integration architecture. Consider: \nRetry mechanisms: Automatically reattempt failed integration executions.\nNotification actions: Send error details via email or logs for prompt resolution.\nGlobal fault handlers: Define standardized error-handling logic across integrations.\n8. Managing changes with versioning\nDirectly modifying active integrations can lead to failures. Instead, adopt versioning to create a new version of an integration without deactivating the existing one. Allow rollback in case of unexpected failures.\n9. Ensuring security and compliance\nFor organizations that handle sensitive data, security is a top priority. Consider: \nAuthentication mechanisms: Use OAuth for APIs to secure access.\nEncrypted communication: Implement SASL PLAIN over SSL for secure message-transmission.\nBuilding a scalable, secure and efficient integration in Oracle Integration Cloud requires careful planning and adherence to best practices. By leveraging batch processing, asynchronous execution, parallel processing, scheduled integrations, error handling,
versioning, and security measures, organizations can ensure high-performance and resilient integrations.\nFollowing these methods will enable businesses to seamlessly integrate applications, improve data flow, and drive operational excellence in the ever-evolving digital landscape.\n\nCLICK HERE TO CONNECT WITH US ABOUT BUILDING SCALABLE INTEGRATIONS AT YOUR ENTERPRISE. \n\n


Tips For Building Scalable Integrations In Oracle Integration Cloud (OIC)

By Satish Saini, HEXstream utility industry specialist \nThere are many factors that propel utilities to adopt smarter asset-managmenet strategies informed by performance insights from relevant analytics. Consider just some of these factors: \nAging T&D grid infrastructure leading to equipment failures\nClimate change causing adverse weather events and impacting power infrastructure reliability\nCustomer needs for reliable power\nUtilities' needs for sustainable, efficient and cost-effective business operations\nRegulatory pressure to maintain operational and safety standards\nRenewable energy sources penetration to the T&D grid\n\nThere are many challenges, unfortunately, but solutions to overcome these challenges get smarter every day. Let's explore...\nAging infrastructure, grid reliability and resiliency:\n The majority of domestic T&D infrastructure is already aging and reaching the end of life, which is leading to frequent failures, blackouts, power-quality issues and increased maintenance costs. Extreme weather events and natural disasters pose further risks to grid reliability and stability and cause heavy expenses due to damages.\nAccording to the US Department of Energy (DOE), much of the US electric grid was built in the 1960s and 1970s, and 70% of the infrastructure is approaching the end of their typical 50–80-year lifecycle. This has major consequences on our communities—power
outages, susceptibility to cyberattacks, emergencies caused by faulty grid infrastructure. \nIts very hard to replace and upgrade all of this infrastructure, as numerous assets along high-voltage transmission lines and substations are quite expensive and involve huge investments.\nHowever, effective asset-management plans and automated performance insights\n \ncan help defer some of the replacement costs through predictive analysis regarding asset-health conditions and through critical condition-based monitoring and maintenance
of critical and expensive assets (think transformers, circuit breakers, etc.). \nThese insights help maximize the lifecycle of assets.\nAsset operational efficiency & performance affecting utility financial sustainability:\n Inefficient operation and performance of assets due to aging and other factors leads to higher system losses, grid/asset failures, voltage fluctuations, power-quality issues and system overloads. These things derail utility operational efficiency, of course, and hurt financial sustainability and cost recovery.\nHuge capital investment on replacement and increased operations & maintenance (O&M) costs of inefficient assets push utilities toward developing and implementing asset-management strategies to efficiently manage the grid through strategic investments, optimizing O&M costs, enhancing equipment lifecycles and improving financial sustainability.\nDigital transformation, real-time monitoring and AI-driven, automated asset-performance insights inform effective data-driven decision-making, as traditional manual inspections and maintenance methodologies lead to delays and inefficient decisions. And as you know, data-driven analytics on proactive and predictive condition-based maintenance more efficient than reactive maintenance.\nRegulatory compliance & safety standards:\n There is a greater public dependency on power. There is increased customer expectation on power quality and reliability. There are more complex requirements to comply with myriad standards and government regulations related to reliability, safety and environmental targets. Failure to meet these goals results in heavy penalties for utilities.  \nA critical part of the strategy to meet these goals is effective asset-management strategies and procedures, which help utilities ensure compliance with grid codes and safety standards (like NERC, FERC, IEEE). Further monitoring of emission levels from grid operations (substations and other assets) helps monitor and meet environmental regulations. Technologies and solutions like real-time monitoring and asset-performance insights help reduce the risks of equipment failures and outages through proactive actions. Automated analytics and reporting mechanisms enhance accuracy in compliance and reducing errors and operational costs. Win win win. \nRenewable energy resources and EV penetration impacting grid assets:\n Increasing penetration of Distributed Energy Resources (DERs) and electric-vehicle charging infrastructure at various segments along the T&D grid is creating new operational challenges for the grid due to reverse and two-way power flow and increased fault levels. This affects a large number of grid assets at transmission lines, substation and distribution feeders, all while triggering the need for heavy replacements and upgrades.\nFor efficient integration and utilization of DERs and the efficient operation of EV-charging infrastructure, efficient and optimized asset-management strategies and investment prioritization is required to maintain operational efficiency and peak performance of the grid.\nSo how do we do this? \nT&D utilities must develop and implement strong asset =-management strategies, programs and frameworks using digital technologies to ensure grid reliability, support renewables, comply with regulations, and enhance financial sustainability. A modern asset-management plan using digital technologies and data-driven, predictive, and proactive asset-performance
monitoring enables utilities to future-proof their infrastructure and improve customer service.\n\nWant more? Click here to contact us for details on asset-management strategies and asset-performance insights.\n\nNext up: A closer look at key assets along the T&D grid, pillars, strategies and roadmap for effective utility-asset management.\n


Creating Smarter Asset-Management Strategies For T&D Utilities

By Swathi Ambati, HEXstream data solutions engineer\n \nIn a \nprevious article\n, we explored how Oracle APEX can use an Excel spreadsheet to quickly create an application that enables data entry, reporting and dashboard functionality. \nHowever, we are in the AI era where artificial intelligence can further accelerate development.\nThis article will focus on the AI features available in APEX, how to configure them, and includes a short demo on creating an application based on existing database tables.\nAI-powered capabilities in APEX\nOracle APEX offers several AI-powered features to improve development efficiency, including:\n·      AI-assisted features in APEX\n·      Integration with OpenAI Assistant via Dynamic Actions\n·      Oracle AI services: Vision, Oracle Digital Assistant (ODA), Document Understanding, Generative AI, and Select AI\n·      Retrieval Augmented Generation (RAG) capabilities in AI-supported applications with APEX\nOracle APEX simplifies AI integration by abstracting popular AI providers such as OpenAI and Cohere. This is achieved through a REST infrastructure where authentication is managed via web credentials. The AI service interacts seamlessly with the Oracle database and APEX, while relying on AI providers for backend inferencing.\nUse cases of AI in APEX\n1. Prompt-based app creation—Developers can create APEX applications using natural language descriptions. This significantly reduces development time and improves
accessibility.\n2. Natural language to SQL (NL2SQL)—Users can enter queries in natural language, which are automatically converted into SQL statements. This simplifies the creation of reports and interaction with the database, especially for non-SQL users.\n3. AI assistant for real-time support—An AI-powered chat widget can assist users with app navigation, query creation and troubleshooting.\nLet's look at a simple example of how AI-powered functions can create an application based on existing data.\nActivate AI services in APEX\nConfigure the generative AI services in APEX by setting up the AI provider (e.g. OpenAI, Cohere).\nTo leverage AI features in APEX 24.1, you first need to configure a Generative AI service within your workspace. This can be done declaratively in just a few steps.\nStep-by-Step Guide to Configuring a Gen AI Service\nNavigate to the Generative AI Settings\nFrom your APEX workspace homepage, go to App Builder.\n\nClick on Workspace Utilities.\n\nSelect Generative AI from the available options.\n\nCreate a New Generative AI Service\nClick Create to set up a new AI service.\n\nChoose an AI provider. APEX 24.1 supports:\nOpenAI\nOCI Generative AI Service\nCohere\nExample: Configuring OpenAI as the AI Provider\nSelect OpenAI from the provider list.\nEnter a service name for easy identification.\n(Optional) Provide a static ID, which will be referenced when using the APEX_AI package.\nEnable the toggle button for App Builder access, allowing you to use the AI service within your applications.\nSet Up API Credentials\nThe base URL is auto-generated—leave it as default.\nEnter your OpenAI API key. (If you don’t have one, visit \nplatform.openai.com\n to sign up for an API key.)\nThe default AI model is set to GPT-4.0.\nFinalize the Configuration\nClick Create to save your AI service configuration.\n\nYour Generative AI service is now ready to use APEX!\nEnter a query in natural language:\n\nUse the APEX wizard to describe the application in natural language.\nThe AI generates a functional APEX application with corresponding tables, forms and reports.\nWe can see that in the demo video the AI Assistant generates a basic application as a starting point based on a two-line request. You can customize the application as needed, and the APEX AI Assistant can assist in creating and modifying SQL queries throughout the customization process.\nThis video demonstrates the process detailed above:\n\nConclusion\nAI-powered functions in Oracle APEX significantly speed up development by automating SQL generation, app creation and chatbot integration. With AI-driven features, developers can focus on building smarter applications faster and more efficiently.\n\nCLICK HERE TO CONNECT WITH HEXSTREAM TO LEARN HOW TO ACCELERATE YOUR APEX DEVELOPMENT. \n

Accelerating APEX Development With AI-driven Functions

By Satish Saini, HEXstream utilities industry specialist \nIn \nthe first installment of this series\n, we discussed that among the latest innovations, \nArtificial Intelligence (AI)\n and its applications are rapidly increasing in supporting operations and management of the energy & power system. \nAI-powered algorithms\n and \nmachine learning (ML) models\n are more and more innovated, customized and advanced to be widely used in current utility transformation and grid modernization efforts.\nIn this segment, some specific use cases of \nAI-Powered application \nin utilities\n \nusing\n Advanced Metering Infrastructure (AMI) / smart metering \ndata will be discussed.\n\nUtility Supply & Demand, Load Forecasting & Planning, Energy Losses\nUtility Demand and Load Forecasting: AI can provide better insights about load and demand patterns across the power grid and help with future demand forecasting by analyzing historical and real time demand and consumption patterns, weather data, and other factors for various communities and segments of the power grid. This can help utilities to better plan their assets upgrades and grid expansions based on forecasted demand.\nUse Case:\nDemand and Load Forecasting\nGrid Expansion Planning\nPeak Load Management\nGrid Supply & Demand Balancing - AI can help utilities in balancing supply and demand by analyzing available supply from grid resources and predicting demand and consumption across the grid and help effectively manage energy distribution. This improves the grid’s reliability by reducing the likelihood of blackouts, especially during peak load times. Combining with further utility initiatives and programs on Demand Response (DR) / Demand Side management (DSM), this can be of big relief to utilities when the grid is constrained. Utilities can use AI driven communication to issue alerts to customers on incentives for participating in demand response programs, and subsequently manage demand.\nUse Case:\n·       Supply & Demand Balance\n·       Demand Response (DR) and Demand Side Management (DSM)\nEnergy Losses, Consumption Anomalies, and Fraud Detection- By analyzing smart metering data, AI models can detect unusual patterns or anomalies in energy consumption for specific consumers, communities and grid segments, such as significant spikes or drops in usage. This can help identify potential problems like faulty meters, power theft / pilferage, frauds, or service lines / equipment malfunctions. These AI algorithms on energy losses by spotting suspicious patterns can help prevent revenue losses and ensuring the accuracy of billing by taking corrective actions.\nUse Case:\nFrauds & Energy Losses detection\nFaulty Meter Detection\nConsumption Anomalies & Billing Errors\nUtility Assets Management and Performance Insights\nAssets Loading: Using and analyzing the historical and real-time AMI data on energy consumption and rolling up to upstream grid assets, AI-powered algorithms can help utilities with meaningful insights on assets loading conditions (like transformers and feeders) and issue alerts on abnormal loading / anomalies. This can help monitor critical assets load and predict future overloading situations, helping utilities take proactive steps in load shifting or assets upgrades.\nUse Case:\nAssets Loading Analysis, Overloading alerts, Upgrade Forecasting\nPower Quality: AMI data on power quality parameters like voltage, active and reactive power, power factor, frequency and others can provide useful information and prediction on key operational and performance parameters of critical grid assets. Relevant alerts can be issued triggered by anomalies, significant spikes and dips which can help utilities plan
proactive, preventive and corrective inspections and maintenance activities. This can help prevent significant failures, expensive breakdowns and downtime and saving operational costs\nUse Case:\nAssets Power Quality monitoring\nAssets Performance insights\nAssets Predictive & Condition-based Maintenance\nCustomer Services, Experience and Engagement:\nEnhanced Customer Experience (CX/CIS) with Dynamic Pricing and Energy Efficiency – Using the historical and real-time metering data, AI-driven algorithms can provide energy consumers with insights on their energy usage patterns and can suggest energy-saving tips by adjusting consumption, taking energy efficiency measures and leading to costs savings. This offers a big benefit when the utility has Dynamic Price structure / Time-of-Use rates. Customer can receive alerts from AI driven communication on energy efficiency and incentives for participating in demand response programs as offered by the utility. It can suggest optimal times for high-energy-consuming tasks (like heating or cooling) with minimum applicable rates (wherever applicable).\nUse Case:\nEnergy Management, Conservation & Efficiency\nDynamic Pricing, Time-of-Use rates and Load Management\nConsumer Engagement and Energy Management – By analyzing individual customer load and energy consumption data, AI systems can provide personalized feedback and recommendations to customers for efficiently managing energy at their premises. This can be achieved through AI driven alerts and communication on energy usage, saving tips, financial and cost saving benefits and feedback.\nUse Case:\nCustomer Engagement and Cost savings\nIntegration with Building and Home Automation System and IoT Devices - AI can integrate AMI data along with data from IoT (Internet of Things) devices deployed at consumer premises, such as smart appliances, thermostats, and sensors and make relevant and efficient forecasts and predictions. This allows for even greater optimization of energy usage, as AI can control devices in real-time to optimize energy consumption and efficiency.\nUse Case:\nAMI Integration with Home Automation and IoT\nTo conclude, utility AMI data can provide a lot more valuable insights, forecasts and predictions regarding load and demand, assets performance, customer services and energy management. All this can help utilitites toward efficient transformation and grid modernization initiatives.\n\nContact us for further details as to how artificial intelligence can help your utility to effectively manage the grid, critical assets and enhancing customer experiences.\n\nUp next: We will discuss more about further use cases for T&D utilities in relation to Advanced Distribution Management System (ADMS) towards grid modernization and utility transformation. \n\n


AI-Powered Use Cases In Utility Transformation And Grid Modernization

By Naveen Madishetti, HEXstream data analyst\nIn the world of data analytics, Power BI has become a prominent tool for transforming raw data into meaningful insights. One of its most powerful features is drill-through functionality, which enables users to go deeper into data points without losing context. In this blog we will explore what drill-through is, how to implement it, and then spotlight benefits of using drill-through.\nWhat is drill-through in Power BI\nDrill-through is a feature in Power BI that enables users to navigate from a high-level summary report to a detailed data view of specific data point. Unlike drilldown, which explores hierarchical data within the same visual, drill-through takes you to a separate page focused on the selected item.\nImplementation of drill-through in Power BI\nCreate a summary page \n·      In Power BI we need to create a new page and rename it as the summary page.\n·      Add a visualization that displays summarized data.\n·      Make sure the visual contains fields relevant to the analysis, such as sales, region, etc.\n\n\nCreate a detailed-view page \n·     Create a second page and rename it as detailed-view page.\n·     Add visualization that contains detailed information about a particular data point such as order ID, product, sales person and unit price.\n\n\nAdd a drill-through field\n·     Drag the field that you want to use as the drill-through filter into the drill-through section in the visualizations page. This field acts as the key that connects the summary page to the detailed-view page.\n·     Power BI automatically creates a back button that allows users to return to the main report page.\n\nHow to use drill-through functionality\n·     Go to the summary page. \n·      Right click on a data point such as region or sales field.\n·      Choose detailed view by selecting drill-through.\n·      The data on the detailed-view page will be filtered according to the value that was chosen.\n·      To reset the selected filter on the detailed view page, click the reset button. \n·      To go back to the summary page, click the back button.\n\n\n\n\nBenefits of using drill-through\nEnhanced data exploration—Drill-through enables users to navigate into specific subsets of data without overcrowding the main report, offering a focused view of the details that matter most.\nImproved user experience—Seamless navigation between summary and detailed views creates a smooth workflow.\nBetter decision-making—By enabling a dynamic approach to data exploration, drill-through
provides deeper insights that help stakeholders make more informed decisions.\nStreamlined reports—Drill-through helps keep reports clean and structured by reducing the need for excessive visuals on a single page.\nConclusion\nDrill-through is a powerful feature that bridges the gap between high-level summaries and detailed analysis by enabling users to explore data with precision and clarity. This functionality not only improves user experience by keeping reports clean and structured, but also empowers decision-makers with deeper, more actionable insights. \nBy leveraging drill-through, businesses can uncover hidden patterns and trends effectively. Implementing this functionality ensures clear and insightful reporting for better strategic outcomes.\n\nCLICK HERE TO CONNECT WITH US ABOUT WAYS DRILL-THROUGH CAN BENEFIT YOUR UTILITY.\n

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