ebook ebooks e-book e-books downloaden bei MyEbooks.ch downloaden

Spatio-Temporal Data Streams

:	Zdravko Galic
:	Spatio-Temporal Data Streams
:	Springer-Verlag
:	9781493965755
:	1
:	CHF 47.40
:

:	Informatik
:	English

:	116
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

< iv>

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.

& bsp;

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.

< div>

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.

< r>

	Preface	7
	Acknowledgements	9
	Contents	10
	Acronyms	12
	1 Introduction	14
	1.1 From Databases to Data Streams	14
	1.2 Data Stream Management Systems---An Overview	18
	1.3 Data Stream Mining and Knowledge Discovery---An Overview	21
	References	25
	2 Spatio-Temporal Continuous Queries	29
	2.1 Foundation of Continuous Query Processing	29
	2.1.1 Running Example	32
	2.2 Stream Windows	36
	2.2.1 Time-Based Window	37
	2.2.2 Tuple-Based Window	39
	2.2.3 Predicate-Based Window	40
	2.3 OCEANUS---A Prototype of Spatio-Temporal DSMS	41
	2.3.1 The Type System	44
	2.4 Operators	46
	2.4.1 Lifting Operations to Spatio-Temporal Streaming Data Types	46
	2.5 Implementation	48
	2.5.1 User-Defined Aggregate Functions	49
	2.5.2 SQL-Like Language Embedding: CSQL	52
	References	55
	3 Spatio-Temporal Data Streams and Big Data Paradigm	58
	3.1 Background	58
	3.2 MobyDick---A Prototype of Distributed Framework	61
	3.2.1 Data Model	61
	3.2.2 Apache Flink	67
	3.2.3 Spatio-Temporal Queries	69
	3.3 Related Work	72
	3.3.1 Distributed Spatial and Spatio-Temporal Batch Systems	73
	3.3.2 Centralized DSMS-Based Systems	74
	3.3.3 Distributed DSMS-Based Systems	75
	3.4 Final Remarks	76
	References	77
	4 Spatio-Temporal Data Stream Clustering	81
	4.1 Introduction	81
	4.1.1 Spatio-Temporal Clustering	82
	4.2 Data Stream Clustering	86
	4.3 Trajectory Stream Clustering	88
	4.3.1 Incremental Trajectory Clustering Using Micro- and Macro-Clustering	88
	4.3.2 CTraStream	94
	4.3.3 Spatial Quincunx Lattices Based Clustering	103
	4.4 Bibliographic Notes	109
	References	110
	Index	114