Titlebook: Analyzing Time Interval Data ; Introducing an Infor Philipp Meisen Book 2016 Springer Fachmedien Wiesbaden GmbH 2016 Aggregation.On-Line An

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Doctoral Education for the Knowledge SocietyThis chapter presents the answer to RQ2 “Which aspects must be covered by a time interval data analysis model and how can it be defined”. This is achieved by defining a model based on the terms time interval, time interval record, time interval dataset, descriptive value, descriptor, time axis, dimensions, descriptor hierarchy, and time hierarchy.

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State of the Art,Time interval data is in the focus of research over the past years and decades. Several aspects, dealing with (time) interval data, have been addressed and are introduced in this chapter.

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TIDAIS: An Information System for Time Interval Data,In this chapter, an information system to analyze time interval data is presented. The system realizes the previously introduced TIDAMODEL, TIDAQL, and TIDADISTANCE. The heart of the system is a bitmap-based data structure, which ensures a high performance when filtering and aggregating.

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Introduction and Motivation,t the outcome of elections, or understand causes. Over the last years, discussions with industrial partners and feedback from several companies showed that the analysis of time interval data created various problems, across different domains.

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TIDADISTANCE: Similarity of Time Interval Data,asure. Thus, a flexible distance measure is needed to determine the similarity between two sets of time interval data. So far, three similarity measures have been introduced, i.e., DTW and ARTEMIS (Kostakis et al. 2011), as well as IBSM (Kotsifakos et al. 2013).

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Results & Evaluation,he information system. In addition, the user acceptance and usability are criteria to be validated. In the first section of this chapter, i.e. section 8.1, the fulfillment of the features requested is validated.