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 notice a knowledge process. The offered approach is in a position to appearing analytical projects (avoiding any form of summarizability problems), delivering insights, and visualizing effects processing hundreds of thousands of periods inside milliseconds utilizing an intuitive SQL-based question language. the guts of the answer is predicated on numerous bitmap-based indexes, which permit the approach to deal with large quantities of time period data.
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Extra resources for Analyzing Time Interval Data : Introducing an Information System for Time Interval Data Analysis
Analyze the different months, detect anomalies, and understand the reasons for the anomalies by looking at the days of the months. , detect frequent patterns occurring on Mondays. Using the time dimension in the context of analytics reveals several problems. One of the problems to deal with is the fact that a calendar week does not neatly fit into a month nor a year. Thus, a time hierarchy like day → calendar week → month → year risks summarizability and comparison problems (Hutchison et al. 2006; Mansmann, Scholl 2006; Mazón et al.
Kimball, Ross (2002), Mazón et al. (2008)). As an analyst, I want to be able to validate the descriptive values (properties) associated to the time interval. , add it, use null, or fail). , intervals which have no start, end, or neither defined) are handled. , create a time point), or fail. As an analyst, I want to be able to write scripts applied to the raw data prior to any processing or cleansing. Thus, I am able to manipulate the incoming data without pre-processing it using integration tools.
When analyzing temporal data the time zone information is of great importance to ensure the validity of the analytical results (cf. Kimball, Ross 2002, p. 240; Carmel 1999; Espinosa et al. 2007). 13 illustrates an example which exemplifies the importance. , UTC+1, UTC-8, and UTC-5). The example implies that data collected in the time zones UTC+1 and UTC-8 represent tasks performed at different airports. , 09/11, a stock market crash, or the moon landing). , if the dataset of an airport is analyzed separately from the other.
Analyzing Time Interval Data : Introducing an Information System for Time Interval Data Analysis by Philipp Meisen