Abstract: Financial investment decision making is a complex process, in which decision makers utilize specific techniques to analyze a large volume of noisy time-series data in order to arrive at a final decision. Collecting and managing the enormous amount of available financial data is an important task in this process, for both researchers and end-user investors. This paper proposes an ontology-based framework for effectively managing big financial data. It further describes the steps required to implement such a framework, and reports the results of a feasibility study into implementing the proposed framework. A Financial Statement Ontology (FSO) is created using the Web Ontology Language (OWL) in the Protégé knowledge framework together with a data acquisition driver written in Perl. The use of an ontology adds a layer of abstraction to Big Data, alleviating the need for end-users to concern themselves with added complexity. The framework thus allows researchers and investors to spend more time on problem-solving and less time managing Big Data. In addition to the described application to finance, the proposed framework has the potential to be applied to any other domain in which relevant data is distributed across multiple systems or is accessed using different formats or names, such as is common in medical research.
Keywords: big data, financial decision support systems, knowledge base, ontology
Download this article: JISAR - V8 N1 Page 31.pdf
Recommended Citation: Westrick, L., Du, J., Wolffe, G. (2015). Building a Better Stockbroker: Managing Big (Financial) Data by Constructing an Ontology-Based Framework . Journal of Information Systems Applied Research, 8(1) pp 31-41. http://jisar.org/2015-8/ ISSN: 1946-1836. (A preliminary version appears in The Proceedings of CONISAR 2014)