Analyzing Time Interval Data

1. Front Matter

   Pages I-XXXI

   PDF

2. Introduction and Motivation

   • Philipp Meisen

   Pages 1-5

3. Time Interval Data Analysis

   • Philipp Meisen

   Pages 7-44

4. State of the Art

   • Philipp Meisen

   Pages 45-71

5. TIDAMODEL: Modeling Time Interval Data

   • Philipp Meisen

   Pages 73-89

6. TIDAQL: Querying for Time Interval Data

   • Philipp Meisen

   Pages 91-110

7. TIDADISTANCE: Similarity of Time Interval Data

   • Philipp Meisen

   Pages 111-119

8. TIDAIS: An Information System for Time Interval Data

   • Philipp Meisen

   Pages 121-179

9. Results & Evaluation

   • Philipp Meisen

   Pages 181-201

10. Summary and Outlook

    • Philipp Meisen

    Pages 203-204

11. Back Matter

    Pages 205-232

    PDF

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. 

• Aggregation
• On-Line Analytical Processing
• Bitmap Index
• Query Language
• Similarity

• IMA, RWTH Aachen University IMA, Aachen, Germany

  Philipp Meisen

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.

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