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Rough Sets Based Knowledge Discovery with a Case Study
M. Ambika1, C.Gomathi2
1P.G. student, Dept. of Computer Science and Engg, Anna University Tiruchirappalli2Lecturer, Dept. of Computer Science and Engg, Anna University Tiruchirappalli
Email: [email protected] , [email protected]
Main issues to be faced by the existing system are management and exploitation of the overwhelming amount of data produced by applications. To achieve these very ambitious goals, there is need to include knowledge discovery and knowledge management functionalities, for both applications and system management. We provide a solution to solve the existing problem.
In the proposed system we aimed at the development of an environment for geographically distributed high-performance knowledge discovery applications. This Knowledge discovery services allow professionals and scientists to extract knowledge from the data stored inside the geographically distributed Database.
In the proposed system the user can perform both publishing and discovering knowledge using WSDL interface. The published data are registered in the Knowledge database. Knowledge database consist of UDDI registry. Ontology defined in OWL document is interpreted as a set of "individuals" and a set of "property assertions" which relate these individuals to each other. OWL ontology consists of a set of axioms which place constraints on sets of individuals and the types of relationships permitted between them.The OWL ontologies are then parsed using OWL parser and then they are stored in the Ontology Repository.
When user query is given, it is then processed in both knowledge database and ontology repository .In order to have efficient and accurate retrieval of information Rough set methodology has been used. It perform knowledge discovery on very large data sets .This discovered knowledge can be used to make scientific discoveries, improve industrial processes and organization models, and uncover business valuable information.
Rough Set theory, is a Mathematical technique to deal with uncertainty in knowledge discovery. It discovers redundancies and dependencies between the given features of a problem. A rough set is a formal approximation of a crisp set in terms of a pair of sets which give the lowers and the upper approximation of the original set. More over it dynamically identifies and reduce dependent properties that may be an uncertain property when matching.
In this paper we have taken a case study about “HEALTH CARE SYSTEM”. The mission is to provide with the most relevant, accurate and up to date information available regarding diseases and conditions and its symptoms, Information about Hospital, Specialty of the hospital, Information about the doctors, Infrastructure facility, Medicine details, Patients history, Availability of doctor, Doctors Appointment and so on
by integrating geographically distributed hospital database of different hospitals. Thus huge amount of information will be available and are processed using Rough Set Methodology.
Thus it is used to solve many problems in existing health care system. These interconnections of hospital provide more benefits to both patients and the hospitals. It allows physicians easy access to massive amounts of up-to-date medical knowledge and to engage with queries or recommendations.
Keywords: Distributed Data Mining, Knowledge discovery, Rough set, Web Services Description Language (WSDL), Universal Description, Discovery and Integration (UDDI), Web Ontology Language (OWL)