| Description |
1 online resource (xiv, 107 pages) : illustrations |
| Series |
SpringerBriefs in computer science, 2191-5768 |
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SpringerBriefs in computer science.
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| 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 |
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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 |
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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
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Data mining.
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Data Mining
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Geographical information systems (GIS) & remote sensing.
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Network hardware.
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Combinatorics & graph theory.
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Databases.
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COMPUTERS -- General.
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Data mining.
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| Form |
Electronic book
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| ISBN |
9781493965755 |
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1493965751 |
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