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Entity Information Life Cycle for Big Data

Entity Information Life Cycle for Big Data
  • Author : John R. Talburt,Yinle Zhou
  • Publisher :Unknown
  • Release Date :2015-04-20
  • Total pages :254
  • ISBN : 9780128006658
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Summary : Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions

Handbook of Research on Big Data Storage and Visualization Techniques

Handbook of Research on Big Data Storage and Visualization Techniques
  • Author : Segall, Richard S.,Cook, Jeffrey S.
  • Publisher :Unknown
  • Release Date :2018-01-05
  • Total pages :917
  • ISBN : 9781522531432
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Summary : The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Information Quality in Information Fusion and Decision Making

Information Quality in Information Fusion and Decision Making
  • Author : Éloi Bossé,Galina L. Rogova
  • Publisher :Unknown
  • Release Date :2019-04-02
  • Total pages :620
  • ISBN : 9783030036430
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Summary : This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.

Information Technology: New Generations

Information Technology: New Generations
  • Author : Shahram Latifi
  • Publisher :Unknown
  • Release Date :2016-03-28
  • Total pages :1306
  • ISBN : 9783319324678
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Summary : This book collects articles presented at the 13th International Conference on Information Technology- New Generations, April, 2016, in Las Vegas, NV USA. It includes over 100 chapters on critical areas of IT including Web Technology, Communications, Security, and Data Mining.

Analytic Methods in Systems and Software Testing

Analytic Methods in Systems and Software Testing
  • Author : Ron S. Kenett,Fabrizio Ruggeri,Frederick W. Faltin
  • Publisher :Unknown
  • Release Date :2018-09-04
  • Total pages :568
  • ISBN : 9781119271505
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Summary : A comprehensive treatment of systems and software testing using state of the art methods and tools This book provides valuable insights into state of the art software testing methods and explains, with examples, the statistical and analytic methods used in this field. Numerous examples are used to provide understanding in applying these methods to real-world problems. Leading authorities in applied statistics, computer science, and software engineering present state-of-the-art methods addressing challenges faced by practitioners and researchers involved in system and software testing. Methods include: machine learning, Bayesian methods, graphical models, experimental design, generalized regression, and reliability modeling. Analytic Methods in Systems and Software Testing presents its comprehensive collection of methods in four parts: Part I: Testing Concepts and Methods; Part II: Statistical Models; Part III: Testing Infrastructures; and Part IV: Testing Applications. It seeks to maintain a focus on analytic methods, while at the same time offering a contextual landscape of modern engineering, in order to introduce related statistical and probabilistic models used in this domain. This makes the book an incredibly useful tool, offering interesting insights on challenges in the field for researchers and practitioners alike. Compiles cutting-edge methods and examples of analytical approaches to systems and software testing from leading authorities in applied statistics, computer science, and software engineering Combines methods and examples focused on the analytic aspects of systems and software testing Covers logistic regression, machine learning, Bayesian methods, graphical models, experimental design, generalized regression, and reliability models Written by leading researchers and practitioners in the field, from diverse backgrounds including research, business, government, and consulting Stimulates research at the theoretical and practical level Analytic Methods in Systems and Software Testing is an excellent advanced reference directed toward industrial and academic readers whose work in systems and software development approaches or surpasses existing frontiers of testing and validation procedures. It will also be valuable to post-graduate students in computer science and mathematics.

Entity Resolution and Information Quality

Entity Resolution and Information Quality
  • Author : John R. Talburt
  • Publisher :Unknown
  • Release Date :2011-01-14
  • Total pages :256
  • ISBN : 0123819733
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Summary : Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality. It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ). The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology. It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the Algebraic Model for Entity Resolution, which are the major theoretical models that support Entity Resolution. In relation to this, the book briefly discusses entity-based data integration (EBDI) and its model, which serve as an extension of the Algebraic Model for Entity Resolution. There is also an explanation of how the three commercial ER systems operate and a description of the non-commercial open-source system known as OYSTER. The book concludes by discussing trends in entity resolution research and practice. Students taking IT courses and IT professionals will find this book invaluable. First authoritative reference explaining entity resolution and how to use it effectively Provides practical system design advice to help you get a competitive advantage Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.

Big Data Revolution

Big Data Revolution
  • Author : Rob Thomas,Patrick McSharry
  • Publisher :Unknown
  • Release Date :2015-01-05
  • Total pages :288
  • ISBN : 9781118943724
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Summary : Exploit the power and potential of Big Data to revolutionizebusiness outcomes Big Data Revolution is a guide to improving performance,making better decisions, and transforming business through theeffective use of Big Data. In this collaborative work by an IBMVice President of Big Data Products and an Oxford Research Fellow,this book presents inside stories that demonstrate the power andpotential of Big Data within the business realm. Readers are guidedthrough tried-and-true methodologies for getting more out of data,and using it to the utmost advantage. This book describes the majortrends emerging in the field, the pitfalls and triumphs beingexperienced, and the many considerations surrounding Big Data, allwhile guiding readers toward better decision making from theperspective of a data scientist. Companies are generating data faster than ever before, andmanaging that data has become a major challenge. With the rightstrategy, Big Data can be a powerful tool for creating effectivebusiness solutions – but deep understanding is key whenapplying it to individual business needs. Big DataRevolution provides the insight executives need to incorporateBig Data into a better business strategy, improving outcomes withinnovation and efficient use of technology. Examine the major emerging patterns in Big Data Consider the debate surrounding the ethical use of data Recognize patterns and improve personal and organizationalperformance Make more informed decisions with quantifiable results In an information society, it is becoming increasingly importantto make sense of data in an economically viable way. It can drivenew revenue streams and give companies a competitive advantage,providing a way forward for businesses navigating an increasinglycomplex marketplace. Big Data Revolution provides expertinsight on the tool that can revolutionize industries.

Beyond Big Data

Beyond Big Data
  • Author : Martin Oberhofer,Eberhard Hechler,Ivan Milman,Scott Schumacher,Dan Wolfson
  • Publisher :Unknown
  • Release Date :2014-10-17
  • Total pages :272
  • ISBN : 9780133509816
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Summary : Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends

New Horizons for a Data-Driven Economy

New Horizons for a Data-Driven Economy
  • Author : José María Cavanillas,Edward Curry,Wolfgang Wahlster
  • Publisher :Unknown
  • Release Date :2016-04-04
  • Total pages :303
  • ISBN : 9783319215693
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Summary : In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Big Data

Big Data
  • Author : Min Chen,Shiwen Mao,Yin Zhang,Victor C.M. Leung
  • Publisher :Unknown
  • Release Date :2014-05-05
  • Total pages :89
  • ISBN : 9783319062457
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Summary : This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.

C2 Re-envisioned

C2 Re-envisioned
  • Author : Marius S. Vassiliou,David S. Alberts,Jonathan Russell Agre
  • Publisher :Unknown
  • Release Date :2014-12-08
  • Total pages :316
  • ISBN : 9781466595804
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Summary : Command and Control (C2) is the set of organizational and technical attributes and processes by which an enterprise marshals and employs human, physical, and information resources to solve problems and accomplish missions.C2 Re-envisioned: The Future of the Enterprise identifies four interrelated megatrends that are individually and collectively shaping the state of the art and practice of C2 as well as the mission challenges we face. These megatrends the book examines are: Big Problems—manifested in part as increasing complexity of both endeavors and enterprises, as military establishments form coalitions with each other, and partnerships with various civilian agencies and non-governmental organizations Robustly Networked Environments—enabled by the extremely broad availability of advanced information and communications technologies (ICT) that place unprecedented powers of information creation, processing, and distribution in the hands of almost anyone who wants them—friend and foe alike Ubiquitous Data—the unprecedented volumes of raw and processed information with which human actors and C2 systems must contend Organizational alternatives—as decentralized, net-enabled approaches to C2 have been made more feasible by technology. The book analyzes historical examples and experimental evidence to determine the critical factors that make C2 go wrong and how to get it right. Successful enterprises in the future will be those that can reconfigure their approaches in an agile manner. Offering fresh perspectives on this subject of critical importance, this book provides the understanding you will need to choose your organizational approaches to suit the mission and the conditions at hand.

Big Data Integration

Big Data Integration
  • Author : Xin Luna Dong,Divesh Srivastava
  • Publisher :Unknown
  • Release Date :2015-02-01
  • Total pages :198
  • ISBN : 9781627052245
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Summary : The big data era is upon us: data are being generated, analyzed, and used at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of big data. BDI differs from traditional data integration along the dimensions of volume, velocity, variety, and veracity. First, not only can data sources contain a huge volume of data, but also the number of data sources is now in the millions. Second, because of the rate at which newly collected data are made available, many of the data sources are very dynamic, and the number of data sources is also rapidly exploding. Third, data sources are extremely heterogeneous in their structure and content, exhibiting considerable variety even for substantially similar entities. Fourth, the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This book explores the progress that has been made by the data integration community on the topics of schema alignment, record linkage and data fusion in addressing these novel challenges faced by big data integration. Each of these topics is covered in a systematic way: first starting with a quick tour of the topic in the context of traditional data integration, followed by a detailed, example-driven exposition of recent innovative techniques that have been proposed to address the BDI challenges of volume, velocity, variety, and veracity. Finally, it presents merging topics and opportunities that are specific to BDI, identifying promising directions for the data integration community.

The Analytics Lifecycle Toolkit

The Analytics Lifecycle Toolkit
  • Author : Gregory S. Nelson
  • Publisher :Unknown
  • Release Date :2018-03-07
  • Total pages :464
  • ISBN : 9781119425090
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Summary : An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.

Programming Collective Intelligence

Programming Collective Intelligence
  • Author : Toby Segaran
  • Publisher :Unknown
  • Release Date :2007-08-16
  • Total pages :362
  • ISBN : 9780596550684
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Summary : Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Data Protection and Information Lifecycle Management

Data Protection and Information Lifecycle Management
  • Author : Thomas D. Petrocelli
  • Publisher :Unknown
  • Release Date :2006
  • Total pages :256
  • ISBN : UOM:39015062617181
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Summary : This book introduces Information Lifecycle Management (ILM), a powerful new strategy for managing enterprise information based on its value over time. The author explains emerging techniques for protecting storage systems and storage networks, and for integrating storage security into your overall security plan. He also presents new technical advances and opportunities to improve existing data-protection processes, including backup/restore, replication, and remote copy.

Managing Data in Motion

Managing Data in Motion
  • Author : April Reeve
  • Publisher :Unknown
  • Release Date :2013-02-26
  • Total pages :204
  • ISBN : 9780123977915
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Summary : Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

The Enterprise Big Data Lake

The Enterprise Big Data Lake
  • Author : Alex Gorelik
  • Publisher :Unknown
  • Release Date :2019-02-21
  • Total pages :224
  • ISBN : 9781491931509
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Summary : The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries

Java EE 7: The Big Picture

Java EE 7: The Big Picture
  • Author : Danny Coward
  • Publisher :Unknown
  • Release Date :2014-10-11
  • Total pages :512
  • ISBN : 9780071837330
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Summary : The Definitive Guide to Java Platform, Enterprise Edition 7 Java EE 7: The Big Picture uniquely explores the entire Java EE 7 platform in an all-encompassing style while examining each tier of the platform in enough detail so that you can select the right technologies for specific project needs. In this authoritative guide, Java expert Danny Coward walks you through the code, applications, and frameworks that power the platform. Take full advantage of the robust capabilities of Java EE 7, increase your productivity, and meet enterprise demands with help from this Oracle Press resource. Explore the features of the Java servlet model and Java servlet API Create dynamic web content with JavaServer Pages and JavaServer Faces Build websites for nonbrowser clients with JAX-RS Push data to web clients using Java WebSockets Secure web applications Work with web component APIs Maximize enterprise beans for multithreading, asynchronous processes, transactions, and more Access relational databases with the Java Database Connectivity APIs and the Java Persistence API Understand the packaging and deployment mechanisms of Java EE applications Work with Java EE Contexts and Dependency Injection Secure enterprise beans in a Java EE application Enable parallel processing with Java EE concurrency APIs

Harness the Power of Big Data The IBM Big Data Platform

Harness the Power of Big Data The IBM Big Data Platform
  • Author : Paul Zikopoulos,Dirk deRoos,Krishnan Parasuraman,Thomas Deutsch,James Giles,David Corrigan
  • Publisher :Unknown
  • Release Date :2012-10-18
  • Total pages :280
  • ISBN : 9780071808170
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Summary : Boost your Big Data IQ! Gain insight into how to govern and consume IBM’s unique in-motion and at-rest Big Data analytic capabilities Big Data represents a new era of computing—an inflection point of opportunity where data in any format may be explored and utilized for breakthrough insights—whether that data is in-place, in-motion, or at-rest. IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is infusing open source Big Data technologies with IBM innovation that manifest in a platform capable of "changing the game." The four defining characteristics of Big Data—volume, variety, velocity, and veracity—are discussed. You’ll understand how IBM is fully committed to Hadoop and integrating it into the enterprise. Hear about how organizations are taking inventories of their existing Big Data assets, with search capabilities that help organizations discover what they could already know, and extend their reach into new data territories for unprecedented model accuracy and discovery. In this book you will also learn not just about the technologies that make up the IBM Big Data platform, but when to leverage its purpose-built engines for analytics on data in-motion and data at-rest. And you’ll gain an understanding of how and when to govern Big Data, and how IBM’s industry-leading InfoSphere integration and governance portfolio helps you understand, govern, and effectively utilize Big Data. Industry use cases are also included in this practical guide.

The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement
  • Author : David Loshin
  • Publisher :Unknown
  • Release Date :2010-11-22
  • Total pages :432
  • ISBN : 0080920349
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Summary : The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

Mastering Enterprise JavaBeans and the Java 2 Platform, Enterprise Edition

Mastering Enterprise JavaBeans and the Java 2 Platform, Enterprise Edition
  • Author : Ed Roman
  • Publisher :Unknown
  • Release Date :1999-10-08
  • Total pages :722
  • ISBN : CORNELL:31924089001931
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Summary : A guide to server-side application development showcases the strengths of Enterprise JavaBeans while demonstrating the coding of transactional, scalable, and secure multi-user applications