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Analyzing time interval data[electronic resource] :introducing an information system for time interval data analysis /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.5
書名/作者:	Analyzing time interval data : introducing an information system for time interval data analysis // by Philipp Meisen.
作者:	Meisen, Philipp.
出版者:	Wiesbaden : : Springer Fachmedien Wiesbaden :, 2016.
面頁冊數:	xxxi, 232 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	System analysis - Data processing.
標題:	Time.
標題:	Computer Science.
標題:	Information Systems and Communication Service.
標題:	Data Structures, Cryptology and Information Theory.
標題:	Software Engineering/Programming and Operating Systems.
ISBN:	9783658157289
ISBN:	9783658157272
內容註:	Modeling Time Interval Data -- Querying for Time Interval Data -- Similarity of Time Interval Data -- An Information System for Time Interval Data Analysis.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-658-15728-9

Analyzing time interval data[electronic resource] :introducing an information system for time interval data analysis /
Meisen, Philipp.

Analyzing time interval dataintroducing an information system for time interval data analysis /[electronic resource] :by Philipp Meisen. - Wiesbaden :Springer Fachmedien Wiesbaden :2016. - xxxi, 232 p. :ill., digital ;24 cm.

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

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.

ISBN: 9783658157289

Standard No.: 10.1007/978-3-658-15728-9doiSubjects--Topical Terms:

616144
System analysis
--Data processing.

LC Class. No.: QA402

Dewey Class. No.: 519.5

Analyzing time interval data[electronic resource] :introducing an information system for time interval data analysis /
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