Galic Spatio-Temporal Data Streams

1. Auflage 2016
ISBN: 978-1-4939-6575-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 116 Seiten

Reihe: SpringerBriefs in Computer Science

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.

Autoren/Hrsg.

Galic, Zdravko

Inhaltsverzeichnis

1;Preface;7
2;Acknowledgements;9
3;Contents;10
4;Acronyms;12
5;1 Introduction;14
5.1;1.1 From Databases to Data Streams;14
5.2;1.2 Data Stream Management Systems---An Overview;18
5.3;1.3 Data Stream Mining and Knowledge Discovery---An Overview;21
5.4;References;25
6;2 Spatio-Temporal Continuous Queries;29
6.1;2.1 Foundation of Continuous Query Processing;29
6.1.1;2.1.1 Running Example;32
6.2;2.2 Stream Windows;36
6.2.1;2.2.1 Time-Based Window;37
6.2.2;2.2.2 Tuple-Based Window;39
6.2.3;2.2.3 Predicate-Based Window;40
6.3;2.3 OCEANUS---A Prototype of Spatio-Temporal DSMS;41
6.3.1;2.3.1 The Type System;44
6.4;2.4 Operators;46
6.4.1;2.4.1 Lifting Operations to Spatio-Temporal Streaming Data Types;46
6.5;2.5 Implementation;48
6.5.1;2.5.1 User-Defined Aggregate Functions;49
6.5.2;2.5.2 SQL-Like Language Embedding: CSQL;52
6.6;References;55
7;3 Spatio-Temporal Data Streams and Big Data Paradigm;58
7.1;3.1 Background;58
7.2;3.2 MobyDick---A Prototype of Distributed Framework ƒ;61
7.2.1;3.2.1 Data Model;61
7.2.2;3.2.2 Apache Flink;67
7.2.3;3.2.3 Spatio-Temporal Queries;69
7.3;3.3 Related Work;72
7.3.1;3.3.1 Distributed Spatial and Spatio-Temporal Batch Systems;73
7.3.2;3.3.2 Centralized DSMS-Based Systems;74
7.3.3;3.3.3 Distributed DSMS-Based Systems;75
7.4;3.4 Final Remarks;76
7.5;References;77
8;4 Spatio-Temporal Data Stream Clustering;81
8.1;4.1 Introduction;81
8.1.1;4.1.1 Spatio-Temporal Clustering;82
8.2;4.2 Data Stream Clustering;86
8.3;4.3 Trajectory Stream Clustering;88
8.3.1;4.3.1 Incremental Trajectory Clustering Using Micro- and Macro-Clustering;88
8.3.2;4.3.2 CTraStream;94
8.3.3;4.3.3 Spatial Quincunx Lattices Based Clustering;103
8.4;4.4 Bibliographic Notes;109
8.5;References;110
9;Index;114