Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Ontology learning from biomedical natural language documents using UMLS
Expert Systems with Applications: An International Journal
Journal of Information Science
Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: Recent developments in the organization goals conformance using ontology
Expert Systems with Applications: An International Journal
Hi-index | 12.08 |
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.