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.
Highlights
The establishment of electronic data management and business processes brought a number of improvements for enterprises, such as the automatic handling of purchasing and selling products.information about products and business processes are almost exclusively managed as data in enterprises’ information systems [1]
This paper pursues the question: how can state-of-the-art information systems be improved by the use of semantic technologies, and 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
We propose that the necessary data integration processes and information systems can be improved by employing methods from the field of semantic technologies
Summary
The establishment of electronic data management and business processes brought a number of improvements for enterprises, such as the automatic handling of purchasing and selling products.information about products and business processes are almost exclusively managed as data in enterprises’ information systems [1]. Enterprises are challenged by the increase of complexity required to handle more and more electronic data and processes [2]. This issue is discussed in context of big data. In 2011, Gartner identified three dimensions of big data, on which business and information technology leaders have to focus: information volume, variety and velocity [14]. Variety and velocity, the International Business Machines Cooperation (IBM) suggests “veracity” as another dimension to measure the reliability of data since data sets arrive from different sources and may not fully fulfill the required quality standards [15]
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