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