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Computing Optimised Result Matrices for the Processing of Objects from Knowledge Resources

Authors:
Claus-Peter Rückemann

Keywords: Knowledge Processing; Result Matrix; Optimisation; Computing; Statistics; Classification; UDC; Big Data; High End Computing; Knowledge Resources; Knowledge Discovery.

Abstract:
The aim of this paper is to discuss and summarise the main results on computing optimised result matrices from the practical creation of long-term multi-disciplinary and multi-lingual knowledge resources. Structuring big data is the essential process, which has to preceed creating and implementing algorithms. The knowledge resources implement structure and features and can be integrated most flexibly into information and computing system components. Main elements are so called knowledge objects, which can consist of any content and context documentation and can employ a multitude of means for description and referencing of objects used with computational workflows. Core attributes are a facetted universal classification and various content views and attributes. Developing workflow implementations for various purposes requires to compute result matrices from the objects and referred knowledge, e.g., from geosciences, archaeology, physics, and information technology. The purposes can require individual processing means, complex algorithms, and a base of big data collections. Advanced discovery workflows can easily demand large computational requirements for High End Computing (HEC) resources supporting an efficient implementation. This paper presents some major methodologies and statistics instruments, which have been developed and successfully integrated. The combination of instruments and resources allows to flexibly compute optimised result matrices for discovery processes in information systems, expert and decision making components, search engine algorithms, and fosters the further development of the long-term knowledge resources.

Pages: 156 to 162

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2308-3484

ISBN: 978-1-61208-365-0

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014