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 data procedure. The awarded procedure is able to acting analytical projects (avoiding any kind of summarizability problems), delivering insights, and visualizing effects processing hundreds of thousands of durations inside milliseconds utilizing an intuitive SQL-based question language. the guts of the answer is predicated on a number of bitmap-based indexes, which permit the process 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
In addition, the needed resources property is of special interest regarding aggregation. 4, this property can lead to summarizability problems if not aggregated correctly (Lenz, Shoshani 1997; Song et al. 2001; Mazón et al. 2008). , summarizability is not given). Within the next sections, the introduced examples are used to exemplify time interval data aggregation and are used to motivate the usage and exemplify the impact of temporal models, concepts and operators. 2 Time Interval Data Aggregation Data aggregation is the predominant operation in the field of data analysis (Zhang et al.
7: The item property using a discrete (left) and continuous (right) temporal model. Within the discrete chart, the diamonds mark the value of the item property and the triangles illustrate the indivisible delta between the previous and the current time point. In this example, the information provided by the continuous model is too precise. , if interpolation is used) it may even be invalid6. From an analytical point of view, one may argue that: ‘as long as the granularity of a discrete time-axis is selected correctly, the discrete temporal model is at least as good as the continuous one’.
Nevertheless, from an information system point of view the process is to generic and wide. Discussions during the different workshops have shown that from an analyst point of view several steps should be redefined or narrowed. f. feature request PD01). 17 illustrates the time interval data analysis process based on the results of the workshops. The figure differentiates between steps which should be supported by an information system (colored boxes) and steps performed by other systems, an analyst or a user (white boxes).
Analyzing Time Interval Data : Introducing an Information System for Time Interval Data Analysis by Philipp Meisen