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This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Adaptive Query Processing surveys the fundamental issues, techniques, costs, and benefits of adaptive query processing. It begins with a broad overview of the field, identifying the dimensions of adaptive techniques. It then looks at the spectrum of approaches available to adapt query execution at runtime - primarily in a non-streaming context. The emphasis is on simplifying and abstracting the key concepts of each technique, rather than reproducing the full details available in the papers. The authors identify the strengths and limitations of the different techniques, demonstrate when they are most useful, and suggest possible avenues of future research. Adaptive Query Processing serves as a valuable reference for students of databases, providing a thorough survey of the area. Database researchers will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications
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The chapters of this book provide an excellent snapshot of current research and development activities in the area of query processing and optimization. They supply potential answers to many questions that have been raised for new types of database systems and at the same time reflect the variety of the different approaches taken. The book acts both as a reference for the state of the art in query processing for the "next generation" of database systems, and as a good starting point for anybody interested in understanding the challenging questions in the area. Furthermore, the book will help the reader to gain an in-depth understanding of why efficient query processing is needed for future database systems.
The Internet and the World Wide Web (WWW) are becoming more and more important in our highly interconnected world as more and more data and information is made available for online access. Many individuals and governmental, commercial, cultural, and scientific organizations increasingly depend on information sources that can be accessed and queried over the Web. For example, accessing flight schedules or retrieving stock information has become common practice in todays world. When accessing this data, many people assume that the information accessed is accurate and that the data source can be accessed reliably. These two examples clearly demonstrate that not only the information content is important, the information about the quality of the data becomes an even more crucial and critical aspect for individuals and organizations when they make plans or take decisions based on the results of their queries. More precisely, having access to information of known quality becomes critical for the well-being and indeed for the functioning of modern industrialized societies. Surprisingly, despite the urgent need for clear concepts and techniques to judge and value quality and for technology to use such (meta) information, very few scientific results are known and available. Few approaches are known to use quality measures for accessing and querying information over the Web. Only a limited number of products on the IT market address this burning problem.
This book constitutes the refereed proceedings of the Third International Workshop on Databases in Networked Information Systems, DNIS 2003, held in Aizu, Japan in September 2003. The 11 revised full papers presented together with 9 invited papers were carefully reviewed and selected for presentation. The papers are organized in topical sections on Web intelligence, information interchange and management systems, information interchange among cyber communities, knowledge annotation and visualization.
This 4-Volume-Set, CCIS 0251 - CCIS 0254, constitutes the refereed proceedings of the International Conference on Informatics Engineering and Information Science, ICIEIS 2011, held in Kuala Lumpur, Malaysia, in November 2011. The 210 revised full papers presented together with invited papers in the 4 volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on e-learning, information security, software engineering, image processing, algorithms, artificial intelligence and soft computing, e-commerce, data mining, neural networks, social networks, grid computing, biometric technologies, networks, distributed and parallel computing, wireless networks, information and data management, web applications and software systems, multimedia, ad hoc networks, mobile computing, as well as miscellaneous topics in digital information and communications.
This two-volume set constitutes the post-conference proceedings of the 4th EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2020, held in Binzhou, China, in September 2020. Due to COVID-19 the conference was held virtually. The 89 papers presented were selected from 190 submissions and focus on theory and application of hybrid information processing technology for smarter and more effective research and application. The theme of ADHIP 2020 was “Industrial applications of aspects with big data”. The papers are named in topical sections as follows: Industrial application of multi-modal information processing; Industrialized big data processing; Industrial automation and intelligent control; Visual information processing.