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Systems 2017, 5(3), 45; doi:10.3390/systems5030045

Ontology-Based Big Data Management

1
Department of Media Management, CAEBUS Center for Advanced E-Business Studies, RheinMain University of Applied Sciences, Unter den Eichen 5, 65195 Wiesbaden, Germany
2
Department of Applied Computer Science, RheinMain University of Applied Sciences, Unter den Eichen 5, 65195 Wiesbaden, Germany
*
Author to whom correspondence should be addressed.
Received: 15 May 2017 / Revised: 29 June 2017 / Accepted: 4 July 2017 / Published: 6 July 2017
(This article belongs to the Special Issue Systems Approaches and Tools for Managing Complexity)
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Abstract

Big data management is no longer an issue for large enterprises only; it has also become a challenge for small and middle-sized enterprises, too. Today, enterprises have to handle business data and processes of increasing complexity that are almost entirely electronic in nature, regardless of enterprises’ size. Enterprises’ information systems need functions based on specific technologies to be able to reduce and interpret the complexity of business data and processes. This paper pursues the question: how can state-of-the-art information systems be improved by the use of semantic technologies, and particularly ontologies? For this purpose, three use cases of information systems that could be improved are described, and approaches based on semantic technologies and ontologies are proposed. The selected use cases relate to data integration, data quality, and business process integration. View Full-Text
Keywords: ontologies; big data management; complexity management; systems tools ontologies; big data management; complexity management; systems tools
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Eine, B.; Jurisch, M.; Quint, W. Ontology-Based Big Data Management. Systems 2017, 5, 45.

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