The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 32-111
Full Name: Zakaria Zubi
Position: Assistant Professor
Age: ON
Sex: Male
Address: Computer Science Department,Al-Tahadi University,Serit Post Office,P.O. Box 727,Serit Libya
Country: LIBYA
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E-mail address: zszubi@yahoo.com
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Title of the Paper: Knowledge Discovery Query Language (KDQL)
Authors as they appear in the Paper: Zakaria Suliman Zubi
Email addresses of all the authors: zszubi@yahoo.com
Number of paper pages: 23
Abstract: Abstract: KDD is a rapidly expanding field with promise for great applicability. Knowledge discovery became the new database technology for the incoming years. The need for automated discovery tools caused an explosion in the number and type of tools available commercially and in the public domain. These requirements encouraged us to propose a new KDD model so called ODBC_KDD(2) described in [39] ."One of the ODBC_KDD(2) model requirements is the implementation of a query language that could handle DM rules"[40]. This query language called Knowledge Discovery Query Language (KDQL). KDQL is a companion of two major tasks in KDD such as DM and Data Visualization. These requirements motivates us to think for the possibility of joining the two tasks of KDD commonly known as Data Mining (DM) and Data Visualization (DV) together in one single KDD process. Integrating DM and DV requires a new database concept. This database concept is called "i-extended database". I-e!
xtended database will be retrieved by the use of KDQL. This I-extended database described in details in [42]. KDQL RULES operations were also theoretically proposed in this paper and some examples were given as well. KDQL RULES are used only to find out the association rules in i-extended database we have. The development and results of this paper would contribute to the data mining and visualization fields in several ways. The formulation of a set of heuristics for algorithms selection will help to clarify the matching between a specific problem and the set of best-suited algorithms or techniques (i.e. association rules) for solving it. These guidelines are expected to be useful and applicable to real DM projects.
Keywords: Key-words: Data Mining (DM), Data Mining Query Language (DMQL), Knowledge Discovery in Databases (KDD),Query Optimization (QO), Rule Mining(RM),Association Rules (AR).
EXTENSION of the file: .doc
Special (Invited) Session: Computer
Organizer of the Session: Data Mining Query Language Developments
How Did you learn about congress: WSEAS
IP ADDRESS: 41.252.102.125
