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Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data [electronic resource] / by L. Octavio Lerma, Vladik Kreinovich.

By: Lerma, L. Octavio [author.].
Contributor(s): Kreinovich, Vladik [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Big Data: 29Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: VIII, 141 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319613499.Subject(s): Computational intelligence | Data mining | Big data | Quantitative research | Artificial intelligence | Computational Intelligence | Data Mining and Knowledge Discovery | Big Data | Data Analysis and Big Data | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction --  Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing --  Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
In: Springer Nature eBookSummary: This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data—we mostly rely on experts’ opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.
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Introduction --  Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing --  Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.

This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data—we mostly rely on experts’ opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.

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