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Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis de Philipp Meisen

Analyzing Time Interval Data: Introducing an Information System for Time Interval Data Analysis

Autor Philipp Meisen
en Limba Engleză Hardback – 26 sep 2016
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. 
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Specificații

ISBN-13: 9783658157272
ISBN-10: 3658157275
Pagini: 264
Ilustrații: XXXI, 232 p. 65 illus., 8 illus. in color.
Dimensiuni: 148 x 210 x 22 mm
Greutate: 0.58 kg
Ediția:1st ed. 2016
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Vieweg
Locul publicării:Wiesbaden, Germany

Cuprins

Modeling Time Interval Data.- Querying for Time Interval Data.- Similarity of Time Interval Data.- An Information System for Time Interval Data Analysis.

Notă biografică

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.

Textul de pe ultima copertă

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.

Contents
  • Modeling Time Interval Data
  • Querying for Time Interval Data
  • Similarity of Time Interval Data
  • An Information System for Time Interval Data Analysis
Target Groups
  • Researchers and students in the field of information management 
  • Business analysts and dispatchers in the fields of online analytical processing (OLAP), data warehousing (DW), business intelligence (BI), workforce management, and data science
The Author
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.

Caracteristici

Publication in the field of technical sciences Includes supplementary material: sn.pub/extras