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