Principles of Distributed Database Systems

by M. Tamer Özsu, Patrick Valduriez

2011 · Springer Science & Business Media

This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.

Process-Aware Information Systems

by Marlon Dumas, Wil M. van der Aalst, Arthur H. ter Hofstede

2005 · John Wiley & Sons

A unifying foundation to design and implement process-aware information systems This publication takes on the formidable task of establishing a unifying foundation and set of common underlying principles to effectively model, design, and implement process-aware information systems. Authored by leading authorities and pioneers in the field, Process-Aware Information Systems helps readers gain a thorough understanding of major concepts, languages, and techniques for building process-aware applications, including: * UML and EPCs: two of the most widely used notations for business process modeling * Concrete techniques for process design and analysis * Process execution standards: WfMC and BPEL * Representative commercial tools: ARIS, TIBCO Staffware, and FLOWer Each chapter begins with a description of the problem domain and then progressively unveils relevant concepts and techniques. Examples and illustrations are used extensively to clarify and simplify complex material. Each chapter ends with a set of exercises, ranging from simple questions to thought-provoking assignments. Sample solutions for many of the exercises are available on the companion Web site. Armed with a new and deeper understanding, readers are better positioned to make their own contributions to the field and evaluate various approaches to a particular task or problem. This publication is recommended as a textbook for graduate and advanced undergraduate students in computer science and information systems, as well as for professionals involved in workflow and business process management, groupware and teamwork, enterprise application integration, and business-to-business integration. A Solution's Manual is available online. An Instructor Support FTP site is also available.

Knowledge Graphs

by Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmermann

2021 · Morgan & Claypool Publishers

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.