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Author Galić, Zdravko, author

Title Spatio-temporal data streams / Zdravko Galić
Published New York : Springer, 2016
Online access available from:
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Description 1 online resource (xiv, 107 pages) : illustrations
Series SpringerBriefs in computer science, 2191-5768
SpringerBriefs in computer science.
Contents Preface; Acknowledgements; Contents; Acronyms; 1 Introduction; 1.1 From Databases to Data Streams; 1.2 Data Stream Management Systems -- An Overview; 1.3 Data Stream Mining and Knowledge Discovery -- An Overview; References; 2 Spatio-Temporal Continuous Queries; 2.1 Foundation of Continuous Query Processing; 2.1.1 Running Example; 2.2 Stream Windows; 2.2.1 Time-Based Window; 2.2.2 Tuple-Based Window; 2.2.3 Predicate-Based Window; 2.3 OCEANUS -- A Prototype of Spatio-Temporal DSMS; 2.3.1 The Type System; 2.4 Operators; 2.4.1 Lifting Operations to Spatio-Temporal Streaming Data Types
2.5 Implementation2.5.1 User-Defined Aggregate Functions; 2.5.2 SQL-Like Language Embedding: CSQL; References; 3 Spatio-Temporal Data Streams and Big Data Paradigm; 3.1 Background; 3.2 MobyDick -- A Prototype of Distributed Framework #x83;; 3.2.1 Data Model; 3.2.2 Apache Flink; 3.2.3 Spatio-Temporal Queries; 3.3 Related Work; 3.3.1 Distributed Spatial and Spatio-Temporal Batch Systems; 3.3.2 Centralized DSMS-Based Systems; 3.3.3 Distributed DSMS-Based Systems; 3.4 Final Remarks; References; 4 Spatio-Temporal Data Stream Clustering; 4.1 Introduction; 4.1.1 Spatio-Temporal Clustering
4.2 Data Stream Clustering4.3 Trajectory Stream Clustering; 4.3.1 Incremental Trajectory Clustering Using Micro- and Macro-Clustering; 4.3.2 CTraStream; 4.3.3 Spatial Quincunx Lattices Based Clustering; 4.4 Bibliographic Notes; References; Index
Summary This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and platforms. It explores a consistent framework which facilitates a thorough understanding of all different facets of the technology, from basic definitions to state-of-the-art techniques. Key topics include spatio-temporal continuous queries, distributed stream processing, SQL-like language embedding, and trajectory stream clustering. Over the course of the book, the reader will become familiar with spatio-temporal data streams management and data flow processing, which enables the analysis of huge volumes of location-aware continuous data streams. Applications range from mobile object tracking and real-time intelligent transportation systems to traffic monitoring and complex event processing. Spatio-Temporal Data Streams is a valuable resource for researchers studying spatio-temporal data streams and Big Data analytics, as well as data engineers and data scientists solving data management and analytics problems associated with this class of data
Bibliography Includes bibliographical references and index
Notes Online resource; title from PDF title page (SpringerLink, viewed September 1, 2016)
Subject Streaming technology (Telecommunications) -- Management.
Data mining.
Form Electronic book
ISBN 9781493965755 (electronic bk.)
1493965751 (electronic bk.)