By Philipp Meisen
Philipp Meisen introduces a version, a question language, and a similarity degree permitting clients to investigate time period facts. The brought instruments are mixed to layout and become aware of a data procedure. The awarded process is in a position to acting analytical initiatives (avoiding any kind of summarizability problems), supplying insights, and visualizing effects processing thousands of durations inside of milliseconds utilizing an intuitive SQL-based question language. the center 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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Additional resources for Analyzing Time Interval Data : Introducing an Information System for Time Interval Data Analysis
The machine only produced 16 items between 09:00 and 12:28, even though it could have produced 25. The translator typed the word ‘treasure’ and looked up the word – ‘Schatzinsel’ within two minutes. Looking at these sentences reveals some peculiarities to be considered when working with time intervals. , "within two minutes"). , 09:00 uses a minute granularity, whereas the granularity of 09:45:12 is seconds). , "red apple" as categorization vs. "16 items" as fact). 1 illustrates a first example of a time interval and different types of associated information.
In contrast to the data science process, the depicted time interval data process described the steps from an information system or data point of view instead of the perspective of an analyst. The analyst uses the information system to query, interact, or understand the time interval dataset and additionally configure and model the system (which is a cross-sectional task, and therefore not illustrated). 17: The result of the workshops regarding the time interval data analysis process. The process starts with the collection of time interval data from an available and configured source.
The realization of these features is addressed in the context of modeling the time axis (cf. 1) and dimensional modeling (cf. 4). 4. 10 The problems occur when using available proprietary software (cf. Mazón et al. (2008)) or algorithms presented in the field of temporal databases (cf. 2). Lately, several proprietary tools like icCube, Microsoft Analysis Services, or IBM Cognos presented features to support many-to-many relationship (cf. Russo, Ferrari (2011)). 15: Overview of selected features defined in the category descriptive analytics in the context of time interval data analysis (cf.