Business intelligence (BI) applications within an enterprise range over enterprise reporting, cube and ad hoc query analysis, statistical analysis, data mining, and proactive report delivery and alerting. The most sophisticated applications of BI are statistical analysis and data mining, which involve mathematical and statistical treatment of data for correlation analysis, trend analysis, hypothesis testing, and predictive analysis. They are used by relatively small groups of users consisting of information analysts and power users, for whom data and analysis are their primary jobs. We present an ontology-based approach for BI applications, specifically in statistical analysis and data mining. We implemented our approach in financial knowledge management system (FKMS), which is able to do: (i) data extraction, transformation and loading, (ii) data cubes creation and retrieval, (iii) statistical analysis and data mining, (iv) experiment metadata management, (v) experiment retrieval for new problem solving. The resulting knowledge from each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports are stored, shared, disseminated, and thus helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification.
Knowledge is power. Today’s business environment has been tougher than ever. Enterprises experience global competitions. Customers demand more on product features and services. Corporate expenses are continuously increasing. To survive in the harsh environment, high-level management needs business intelligent information to efficiently manage corporate operations and support their making of decisions. Support-level staffs need knowledge information to provide better customer services for gaining satisfaction and retaining loyalty. Vast operating data is staggered into various corporate databases and needs consolidating. It has become more important than ever to access and generate valuable knowledge and share information among authorized users within a corporation and/or business partners. Thus, a system of integrating knowledge management and decision support processes is in great demand. As mentioned in (Bolloju, Khalifa, & Turban, 2002), a synergy can be created by the integration of decision support and knowledge management, since these two processes involve activities that complement each other. The knowledge retrieval, storage, and dissemination activities in knowledge management functionality enhance the dynamic creation and maintenance of decision support models, subsequently, enhancing the decision support process. From the system design’s point of view, what we need is a new generation of knowledge-enabled system that provides enterprise an infrastructure to capture, cleanse, store, organize, leverage, and disseminate not only source data and information but also the knowledge or value-added information of the firm (Nemati, Steiger, Iyer, & Herschel, 2002).
We present the concept of financial knowledge management system (FKMS), which is a prototype of KM environment specifically for financial research purposes. What the environment generates is groups of knowledge set with strings of data, models, parameters, and reports. Ontology of knowledge management and knowledge sharing is presented. Finally, a realization of decision support and knowledge sharing processes to a corporate bond classification is illustrated. With FKMS, knowledge workers can freely extract sets of financial and economic data, analyze data with different decision support modules, rerun experiments with different sets of parameters, and finally disseminate value-added information (knowledge) through middleware or Internet to remote clients. Not to mention that the knowledge generated is being collected, classified, and shared with colleagues, and thus well archived into corporate business intelligence databank.
The remainder of this paper proceeds as follows. Section 2 reasons our motivation for developing FKMS. Section 3 introduces system architecture of FKMS. Section 4 presents the ontology of knowledge management and knowledge sharing, and demonstrates with a case of corporate bond classification problem. Section 5 concludes this paper.
Domain experts need a flexible system environment where they can freely select data and models and run different settings of parameters for decision support purposes. Knowledge sets of each research experiment containing data, models, parameters, and results essentially provide great value to business intelligence generation. The integration of decision support and knowledge management processes is crucial for enterprises to create their niche business intelligence and to maintain global competitive advantages.
We present the concept of financial knowledge management system, FKMS, which is a prototype of KM environment specifically for financial research purposes. What FKMS environment generates are groups of knowledge sets containing strings of data, models, parameters, and reports for each analytical study. Ontology of knowledge management and knowledge sharing is presented. Finally, we demonstrate a business intelligence generation process, where corporate bonds are classified by domain experts using selected data mining approach, finally learned cluster data of bond features are being saved for prediction of rating changes or determining interest rates of newly issuing bonds.
As thousands of knowledge sets gradually piled up in the knowledge database, intelligent screening of ontology, spontaneous push-and-pull knowledge dissemination, and performance ranking will essentially and inevitably lead the way to more powerful knowledge generation.
