Analyzing Time Interval Data : Introducing an Information by Philipp Meisen PDF

By Philipp Meisen

ISBN-10: 3658157275

ISBN-13: 9783658157272

ISBN-10: 3658157283

ISBN-13: 9783658157289

Philipp Meisen introduces a version, a question language, and a similarity degree permitting clients to research time period facts. The brought instruments are mixed to layout and notice a data procedure. The provided approach is able to acting analytical projects (avoiding any kind of summarizability problems), supplying insights, and visualizing effects processing hundreds of thousands of durations inside milliseconds utilizing an intuitive SQL-based question language. the center of the answer relies on numerous bitmap-based indexes, which allow the approach to address large quantities of time period data. 

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In addition, the analyst might retrieve and visualize results created by defined exploratory data analysis tasks, data mining algorithm, or machine learning concepts (step: "Apply Algorithms & Models"). Depending on the configuration of the information system, the defined algorithms and models are applied automatically used to determine if an alert has to be generated (step: "Data Observer") or report results to a decision maker (step: "Communicate, Visualize, Report"). 2 Features of Time Interval Data Analysis Information System 39 In the following, the requested features for the steps: "Raw Time Interval Dataset" (Data Linkage & Collection), "Processed Data and Clean Dataset" (Data Integration & Cleansing), and "Apply Algorithms & Models" (Application of Models & Algorithms) are introduced and discussed.

The issues arising when using these tools are the definition of the rule-sets or the scenario’s parameters. To formulate such a rule-set or determine the parameters, a better understanding of current and past situations is required which support the necessity of descriptive analytics. 2. 2: Overview of the features requested in the category predictive analytics. Identifier PD-01 PD-02 Description As a manager/supervisor, I want to be able to observe specified measures and be alerted if a defined threshold may be reached in the near future.

55)). Linear, Branching, and Circular Temporal Models Another aspect of temporal models addresses the future. Within a linear temporal model only one future is assumed, whereby a branching temporal model allows the existence of at least one but also multiple futures (paths). Moreover, a circular temporal model defines the future to be recurring. In the majority of cases regarding temporal data analysis, a linear temporal model is used. This is plausible because of the temporal concepts and operators mostly used within the field.

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Analyzing Time Interval Data : Introducing an Information System for Time Interval Data Analysis by Philipp Meisen

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