Spatio-Temporal Data Streams (eBook)

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 ƒ 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