Name
Title | ||
|---|---|---|
Architecture-Centric Data Mining Middleware Supporting Multiple Data Sources And Mining Techniques |
Abstract | ||
|---|---|---|
In today's market place, information stored in a consumer database is the most valuable asset of an organization. It houses important hidden information that can be extracted to solve real-world problems in engineering, science, and business. The possibility to extract hidden information to solve real-world problems has led to increasing application of knowledge discovery in databases, and hence the emergence of a variety of data mining tools in the market. These tools offer different strengths and capabilities, helping decision makers to improve business decisions. In this paper, we provide a high-level overview of a proposed data mining middleware whose architecture provides great flexibility for a wide spectrum of data mining techniques to support decision makers in generating useful knowledge to help in decision making. We describe features that we consider important to be supported by the middleware such as providing a wide spectrum of data mining algorithms and reports through plugins. We also briefly explain both the high-level architecture of the middleware and technologies that will be used to develop it. |
Year | Venue | Keywords |
|---|---|---|
2007 | ICSOFT (ISDM/EHST/DC) | knowledge discovery, data mining, middleware, data mining middleware |
Field | DocType | Citations |
Middleware,Data mining,Architecture,Multiple data,Data stream mining,World Wide Web,Middleware (distributed applications),Web mining,Computer science,Database | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Peck Lee | 1 | 142 | 22.55 |
| Lai Ee Hen | 2 | 0 | 0.34 |