In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphereTM Streams (V1). See: http://www.redbooks.ibm.com/abstracts/sg247970.html for the newer InfoSphere Streams (V2) release. Stream computing is a new paradigm. In traditional processing, queries are typically run against relatively static sources of data to provide a query result set for analysis. With stream computing, a process that can be thought of as a continuous query, that is, the results are continuously updated as the data sources are refreshed. So, traditional queries seek and access static data, but with stream computing, a continuous stream of data flows to the application and is continuously evaluated by static queries. However, with IBM InfoSphere Streams, those queries can be modified over time as requirements change. IBM InfoSphere Streams takes a fundamentally different approach to continuous processing and differentiates itself with its distributed runtime platform, programming model, and tools for developing continuous processing applications. The data streams consumable by IBM InfoSphere Streams can originate from sensors, cameras, news feeds, stock tickers, and a variety of other sources, including traditional databases. It provides an execution platform and services for applications that ingest, filter, analyze, and correlate potentially massive volumes of continuous data streams.

There are multiple uses for big data in every industry—from analyzing larger volumes of data than was previously possible to driving more precise answers, to analyzing data at rest and data in motion to capture opportunities that were previously lost. A big data platform will enable your organization to tackle complex problems that previously could not be solved using traditional infrastructure. As the amount of data available to enterprises and other organizations dramatically increases, more and more companies are looking to turn this data into actionable information and intelligence in real time. Addressing these requirements requires applications that are able to analyze potentially enormous volumes and varieties of continuous data streams to provide decision makers with critical information almost instantaneously. IBM® InfoSphere® Streams provides a development platform and runtime environment where you can develop applications that ingest, filter, analyze, and correlate potentially massive volumes of continuous data streams based on defined, proven, and analytical rules that alert you to take appropriate action, all within an appropriate time frame for your organization. This IBM Redbooks® publication is written for decision-makers, consultants, IT architects, and IT professionals who will be implementing a solution with IBM InfoSphere Streams.

In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphereTM Streams (V2), a new paradigm and key component of IBM Big Data platform. Data has traditionally been stored in files or databases, and then analyzed by queries and applications. With stream computing, analysis is performed moment by moment as the data is in motion. In fact, the data might never be stored (perhaps only the analytic results). The ability to analyze data in motion is called real-time analytic processing (RTAP). IBM InfoSphere Streams takes a fundamentally different approach to Big Data analytics and differentiates itself with its distributed runtime platform, programming model, and tools for developing and debugging analytic applications that have a high volume and variety of data types. Using in-memory techniques and analyzing record by record enables high velocity. Volume, variety and velocity are the key attributes of Big Data. The data streams that are consumable by IBM InfoSphere Streams can originate from sensors, cameras, news feeds, stock tickers, and a variety of other sources, including traditional databases. It provides an execution platform and services for applications that ingest, filter, analyze, and correlate potentially massive volumes of continuous data streams. This book is intended for professionals that require an understanding of how to process high volumes of streaming data or need information about how to implement systems to satisfy those requirements. See: http://www.redbooks.ibm.com/abstracts/sg247865.html for the IBM InfoSphere Streams (V1) release.

This IBM® Redbooks® publication describes visual development, visualization, adapters, analytics, and accelerators for IBM InfoSphere® Streams (V3), a key component of the IBM Big Data platform. Streams was designed to analyze data in motion, and can perform analysis on incredibly high volumes with high velocity, using a wide variety of analytic functions and data types. The Visual Development environment extends Streams Studio with drag-and-drop development, provides round tripping with existing text editors, and is ideal for rapid prototyping. Adapters facilitate getting data in and out of Streams, and V3 supports WebSphere MQ, Apache Hadoop Distributed File System, and IBM InfoSphere DataStage. Significant analytics include the native Streams Processing Language, SPSS Modeler analytics, Complex Event Processing, TimeSeries Toolkit for machine learning and predictive analytics, Geospatial Toolkit for location-based applications, and Annotation Query Language for natural language processing applications. Accelerators for Social Media Analysis and Telecommunications Event Data Analysis sample programs can be modified to build production level applications. Want to learn how to analyze high volumes of streaming data or implement systems requiring high performance across nodes in a cluster? Then this book is for you.

As world activities become more integrated, the rate of data growth has been increasing exponentially. And as a result of this data explosion, current data management methods can become inadequate. People are using the term big data (sometimes referred to as Big Data) to describe this latest industry trend. IBM® is preparing the next generation of technology to meet these data management challenges. To provide the capability of incorporating big data sources and analytics of these sources, IBM developed a stream-computing product that is based on the open source computing framework Apache Hadoop. Each product in the framework provides unique capabilities to the data management environment, and further enhances the value of your data warehouse investment. In this IBM Redbooks® publication, we describe the need for big data in an organization. We then introduce IBM InfoSphere® BigInsightsTM and explain how it differs from standard Hadoop. BigInsights provides a packaged Hadoop distribution, a greatly simplified installation of Hadoop and corresponding open source tools for application development, data movement, and cluster management. BigInsights also brings more options for data security, and as a component of the IBM big data platform, it provides potential integration points with the other components of the platform. A new chapter has been added to this edition. Chapter 11 describes IBM Platform Symphony®, which is a new scheduling product that works with IBM Insights, bringing low-latency scheduling and multi-tenancy to IBM InfoSphere BigInsights. The book is designed for clients, consultants, and other technical professionals.

This book covers important aspects of fundamental research in data provenance and data management(DPDM), including provenance representation and querying, as well as practical applications in such domains as clinical trials, bioinformatics and radio astronomy.

This book is a collection of peer-reviewed best selected research papers presented at the Second International Conference on Machine Intelligence and Smart Systems (MISS 2021), organized during September 24–25, 2021, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation. Scientists, engineers, academicians, technology developers, researchers, students and government officials will find this book useful in handling their complicated real-world issues by using machine intelligence methodologies.