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Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis 1st ed. 2016 [Kõva köide]

  • Formaat: Hardback, 232 pages, kõrgus x laius: 210x148 mm, kaal: 4451 g, 8 Illustrations, color; 57 Illustrations, black and white; XXXI, 232 p. 65 illus., 8 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Sep-2016
  • Kirjastus: Springer Vieweg
  • ISBN-10: 3658157275
  • ISBN-13: 9783658157272
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Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 232 pages, kõrgus x laius: 210x148 mm, kaal: 4451 g, 8 Illustrations, color; 57 Illustrations, black and white; XXXI, 232 p. 65 illus., 8 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Sep-2016
  • Kirjastus: Springer Vieweg
  • ISBN-10: 3658157275
  • ISBN-13: 9783658157272
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783658157272.html
Märksõnad:
Philipp Meisen introduces a model, a query language, and a similarity measure enabling users to analyze time interval data. The introduced tools are combined to design and realize an information system. The presented system is capable of performing analytical tasks (avoiding any type of summarizability problems), providing insights, and visualizing results processing millions of intervals within milliseconds using an intuitive SQL-based query language. The heart of the solution is based on several bitmap-based indexes, which enable the system to handle huge amounts of time interval data.

Modeling Time Interval Data.- Querying for Time Interval Data.- Similarity of Time Interval Data.- An Information System for Time Interval Data Analysis.
Acknowledgments v
Abstract vii
Zusammenfassung ix
Table of Contents
xi
List of Abbreviations
xv
List of Figures
xix
List of Tables
xxv
List of Listings
xxvii
List of Definitions
xxxi
1 Introduction and Motivation
1(6)
2 Time Interval Data Analysis
7(38)
2.1 Time
7(22)
2.1.1 Time Intervals
7(3)
2.1.2 Time Interval Data Aggregation
10(4)
2.1.3 Temporal Models
14(6)
2.1.4 Temporal Operators
20(2)
2.1.5 Temporal Concepts
22(1)
2.1.6 Special Characteristics of Time
23(6)
2.2 Features of Time Interval Data Analysis Information System
29(14)
2.2.1 Analytical Capabilities
30(5)
2.2.2 Time Interval Data Analysis Process
35(7)
2.2.3 User Interface, Visualization, and User Interactions
42(1)
2.3 Summary
43(2)
3 State of the Art
45(28)
3.1 Analytical Information Systems
45(1)
3.2 Analyzing Time Interval Data: Different Approaches
46(10)
3.2.1 On-Line Analytical Processing
47(5)
3.2.2 Temporal Pattern Mining & Association Rule Mining
52(2)
3.2.3 Visual Analytics
54(2)
3.3 Performance Improvements
56(6)
3.3.1 Indexing Time Interval Data
56(4)
3.3.2 Aggregating Time Interval Data
60(1)
3.3.3 Caching Time Interval Data
61(1)
3.4 Analytical Query Languages for Temporal Data
62(5)
3.5 Similarity of Time Interval Data
67(3)
3.6 Summary
70(3)
4 TidaModel: Modeling Time Interval Data
73(18)
4.1 Time Axis τ
73(3)
4.2 Descriptors Σ
76(4)
4.3 Time Interval Database P
80(2)
4.4 Dimensional Modeling Δ
82(5)
4.5 Summary
87(4)
5 TidaQL: Querying for Time Interval Data
91(20)
5.1 Data Control Language
92(3)
5.2 Data Definition Language
95(1)
5.3 Data Manipulation Language
96(12)
5.3.1 Insert, Delete, & Update Statements
97(2)
5.3.2 Get & Alive Statements
99(1)
5.3.3 Select Statements
100(8)
5.4 Summary
108(3)
6 TidaDistance: Similarity of Time Interval Data
111(10)
6.1 Temporal Order Distance
113(2)
6.2 Temporal Relational Distance
115(2)
6.3 Temporal Measure Distance
117(1)
6.4 Temporal Similarity Measure
118(3)
7 TidaIS: An Information System for Time Interval Data
121(60)
7.1 System's Architecture, Components, and Implementation
121(8)
7.1.1 Data Repository
125(2)
7.1.2 Cache & Storage
127(2)
7.2 Configuration
129(20)
7.2.1 Model Configuration
130(15)
7.2.2 System Configuration
145(4)
7.3 Data Structures & Algorithms
149(27)
7.3.1 Model Handling
150(6)
7.3.2 Indexes
156(9)
7.3.3 Caching & Storage
165(2)
7.3.4 Aggregation Techniques
167(4)
7.3.5 Distance Calculation
171(5)
7.4 User Interfaces
176(2)
7.5 Summary
178(3)
8 Results & Evaluation
181(22)
8.1 Requirements & Features
181(6)
8.2 Performance
187(14)
8.2.1 High Performance Collections
188(1)
8.2.2 Load Performance
189(1)
8.2.3 Selection Performance
190(6)
8.2.4 Distance Performance
196(1)
8.2.5 Proprietary Solutions vs. TidaIS
197(4)
8.3 Summary
201(2)
9 Summary and Outlook
203(2)
Appendix
205(14)
Pipelined Table Functions (PL/SQL Oracle)
205(1)
A Complete Sample Model-Configuration-File
206(5)
A Complete Sample Configuration-File
211(4)
Detailed Overview of the Runtime Performance
215(2)
3-NN of the Temporal Relational Similarity
217(2)
Bibliography 219
Philipp Meisen holds a doctoral degree from RWTH Aachen, where he was a research group leader at the Chair of Information Management in Mechanical Engineering.