Analyzing Time Interval Data  [electronic resource] : Introducing an Information System for Time Interval Data Analysis /
Meisen, Philipp.author.
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.
"Research."
Includes bibliographical references.
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.
Computers.
Data structures (Computer science).
Software engineering.
Information Systems and Communication Service.
Data Structures and Information Theory.
Software Engineering/Programming and Operating Systems.