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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.
This work presents techniques for the construction of a global data integrations system. Similar to distributed databases this system allows declarative queries in order to express user-specific information needs. Scalability towards global data integration systems and openness were major design goals for the architecture and techniques developed in this work. It is shown how service composition, extensibility and quality of service can be supported in an open system of providers for data, functionality for query processing operations, and computing power.
This research book presents key developments, directions, and challenges concerning advanced query processing for both traditional and non-traditional data. A special emphasis is devoted to approximation and adaptivity issues as well as to the integration of heterogeneous data sources. The book will prove useful as a reference book for senior undergraduate or graduate courses on advanced data management issues, which have a special focus on query processing and data integration. It is aimed for technologists, managers, and developers who want to know more about emerging trends in advanced query processing.
Data Integration aims to combine distributed information conforming to different data models and provide interfaces for accessing such information in an unfied view. The Mediator Environment for Multiple Information Sources (MOMIS), aims to construct synthesized, integrated descriptions of information coming from multiple heterogeneous sources. We show how the MOMIS data integration system can deal with all the twelve queries of the THALIA benchmark, a public available testbed and benchmark for information integration systems. We propose new techniques that consider the optimization of full outerjoin operation in MOMIS. Full outerjoin is used in data integration systems for merging multiple records representing the same real-world object into a single, consistent, and clean representation. A query manager that merge information using full outerjoin can benefit easily from this optimization technique. We made a study of different data integration systems which implement the notion of data quality. We propose a table that summarizes relationships between data anomalies and data quality dimensions. Finally, we focus on quality-driven query processing.
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 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.