A general framework for time-aware decision support systems

• Published in: Expert systems with applications 2013-02, Vol.40 (2), p.399-407
• Main Authors: Milea, V., Frasincar, F., Kaymak, U.
• Format: Article
• Language: English
• Subjects: Agglomeration; Applied sciences; Artificial intelligence; Automated trading; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Decision support system; Decision support systems; Decision theory. Utility theory; Exact sciences and technology; Information systems. Data bases; Intelligence; Market recommendations; Markets; Materials handling; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; Reasoning; Software; Speech and sound recognition and synthesis. Linguistics; State of the art; Temporal knowledge; Temporal logic; Temporal reasoning; tOWL
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.08.001

► We present a general framework for time-aware decision support systems. ► Temporal knowledge is represented in the state-of-the-art tOWL language. ► tOWL enables temporal reasoning and interoperability across systems. ► The framework is applied as a market recommendations aggregation system. ► We implement multiple methods for the aggregation of market recommendations. In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.