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This book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained.Data errors can cause big problems in any process plant or refinery. Process measurements can be correupted by power supply flucutations, network transmission and signla conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of sen

Chemical process control --- Automatic data collection systems. --- Error analysis (Mathematics) --- Data collection systems, Automatic --- Factory data acquisition systems, Automatic --- Factory monitoring systems, Automatic --- In-plant data collection systems, Automatic --- Automation --- Communication in management --- Data transmission systems --- Errors, Theory of --- Instrumental variables (Statistics) --- Mathematical statistics --- Numerical analysis --- Statistics --- Automation. --- Data processing --- Measurement. --- Data processing. --- Error --- Process control

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Robotics --- Human-machine systems --- Supervisory control systems. --- 681.3*I29 --- Control systems, Supervisory --- SCADA systems --- Supervisory control and data acquisition systems --- Process control --- Remote control --- Human operators (Systems engineering) --- Human subsystems (Systems engineering) --- Man-machine control systems --- Man-machine systems --- Operator-machine systems --- Engineering systems --- Human engineering --- Automation --- Machine theory --- Robotics: manipulators; propelling mechanisms; sensors (Artificial intelli- gence) --- Human-machine systems. --- Robotics. --- 681.3*I29 Robotics: manipulators; propelling mechanisms; sensors (Artificial intelli- gence) --- Supervisory control systems

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Over 70 practical recipes to gain operational data intelligence with Splunk Enterprise About This Book This is the most up-to-date book on Splunk 6.3 and teaches you how to tackle real-world operational intelligence scenarios efficiently Get business insights using machine data using this easy-to-follow guide Search, monitor, and analyze your operational data skillfully using this recipe-based, practical guide Who This Book Is For This book is intended for users of all levels who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of business, IT, security, product, marketing, and many more! Also, existing users of Splunk who want to upgrade and get up and running with Splunk 6.3 will find this book invaluable. What You Will Learn Use Splunk to gather, analyze, and report on data Create dashboards and visualizations that make data meaningful Build an operational intelligence application with extensive features and functionality Enrich operational data with lookups and workflows Model and accelerate data and perform pivot-based reporting Build real-time, scripted, and other intelligence-driven alerts Summarize data for longer term trending, reporting, and analysis Integrate advanced JavaScript charts and leverage Splunk's API In Detail Splunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 70 recipes that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You'll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You'll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organization. Throughout the book, you'll dive deeper into Splunk, explore data models and pivots to extend your intel...

Big data. --- Data mining. --- Automatic data collection systems. --- Data collection systems, Automatic --- Factory data acquisition systems, Automatic --- Factory monitoring systems, Automatic --- In-plant data collection systems, Automatic --- Automation --- Communication in management --- Data transmission systems --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data sets, Large --- Large data sets --- Data sets --- Big data --- Data mining --- Automatic data collection systems --- E-books

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Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn Apply artificial neural networks when modelling medical data Know the standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a clinical trial Understand medical record categorization Measure personality differences using psychological models Who This Book Is For Machine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting.

Artificial intelligence. --- Automatic data collection systems. --- Data collection systems, Automatic --- Factory data acquisition systems, Automatic --- Factory monitoring systems, Automatic --- In-plant data collection systems, Automatic --- Automation --- Communication in management --- Data transmission systems --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

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Use this practical guide to the Splunk operational data intelligence platform to search, visualize, and analyze petabyte-scale, unstructured machine data. Get to the heart of the platform and use the Search Processing Language (SPL) tool to query the platform to find the answers you need. With more than 140 commands, SPL gives you the power to ask any question of machine data. However, many users (both newbies and experienced users) find the language difficult to grasp and complex. This book takes you through the basics of SPL using plenty of hands-on examples and emphasizes the most impactful SPL commands (such as eval, stats, and timechart). You will understand the most efficient ways to query Splunk (such as learning the drawbacks of subsearches and join, and why it makes sense to use tstats). You will be introduced to lesser-known commands that can be very useful, such as using the command rex to extract fields and erex to generate regular expressions automatically. In addition, you will learn how to create basic visualizations (such as charts and tables) and use prescriptive guidance on search optimization. For those ready to take it to the next level, the author introduces advanced commands such as predict, kmeans, and cluster. What You Will Learn Use real-world scenarios (such as analyzing a web access log) to search, group, correlate, and create reports using SPL commands Enhance your search results using lookups and create new lookup tables using SPL commands Extract fields from your search results Compare data from multiple time frames in one chart (such as comparing your current day application performance to the average of the past 30 days) Analyze the performance of your search using Job Inspector and identify execution costs of various components of your search This book is for application developers, architects, DevOps engineers, application support engineers, network operations center analysts, security operations center (SOC) analysts, and cyber security professionals who use Splunk to search and analyze their machine data. Karun Subramanian is an IT operations expert and a Splunk certified architect. He is committed to helping IT organizations implement world-class observability by making use of machine-generated data. His IT career has spanned more than two decades, ranging from systems administrator to software engineer to IT director. Possessing deep expertise of the Splunk platform, he has assisted teams to solve complex problems in the area of DevOps, security, and business analytics. He has worked in engineering roles for firms including Wells Fargo Bank, Express Scripts, Federal Reserve Bank, and Optum.

Big data. --- Information technology. --- Business—Data processing. --- Statistics . --- Big Data. --- IT in Business. --- Statistics, general. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Data sets, Large --- Large data sets --- Data sets --- Data mining. --- Automatic data collection systems. --- Data collection systems, Automatic --- Factory data acquisition systems, Automatic --- Factory monitoring systems, Automatic --- In-plant data collection systems, Automatic --- Automation --- Communication in management --- Data transmission systems --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching