Analyzing Time Interval Data : Introducing an Information by Philipp Meisen

By Philipp Meisen

Philipp Meisen introduces a version, a question language, and a similarity degree permitting clients to investigate time period information. The brought instruments are mixed to layout and become aware of a knowledge process. The offered approach is able to appearing analytical projects (avoiding any form of summarizability problems), offering insights, and visualizing effects processing hundreds of thousands of periods inside of milliseconds utilizing an intuitive SQL-based question language. the guts of the answer relies on a number of bitmap-based indexes, which allow the method to deal with large quantities of time period data. 

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Chapter 5 introduces a query language supporting the usage of temporal aggregations. 3 Temporal Models In literature about time, various temporal models have been proposed to represent physical time. Generally it can be stated that physical time can be modeled as discrete, dense, or continuous (Dyreson et al. 1994; Hudry 2004). In addition, literature introduces other aspects namely linear, branching, or circular temporal models, as well as bounded or unbounded temporal models (Frühwirth 1996).

4. 2 Features of Time Interval Data Analysis Information System As noted in the introduction of this chapter, several workshops with analysts from different domains were organized addressing the issues occurring when analyzing time interval data. " was held with 64 international companies (mainly aviation industry, logistics providers, and ground-handling service providers) during the "Inform Users Conference 2012". , regarding the query language or special visualizations). , aviation, logistic, groundhandling, call-center, hospitals, temporary employment, and linguistic.

26–27). , one when the time must be adjusted back one hour, the other one when it is forwarded). These days have 23 or 25 hours which makes it difficult to compare these days to any others. The problem can be exemplified when assuming a company utilizing an app to measure the employees’ performed tasks during a day. Analyzing the average amount of performed tasks within an hour may lead to false results and therefore to erroneous decisions. 14 illustrates the problem regarding DST and statistical values.

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