The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTER RESEARCH
Transactions ID Number: 32-225
Full Name: Saleh Abu-Soud
Position: Professor
Age: ON
Sex: Male
Address: P.O.Box (1202), Jubeiha 11941, Amman
Country: JORDAN
Tel: 777486610
Tel prefix: 00962
Fax: 0096265169091
E-mail address: abu-soud@psut.edu.jo
Other E-mails: sabusoud@nyit.edu, saleh@nyit.edu.jo
Title of the Paper: DRILA: A Distributed Relational Inductive Learning Algorithm
Authors as they appear in the Paper: Saleh Abu-Soud, Ali Al-Ibrahim
Email addresses of all the authors: sabusoud@nyit.edu,alikitim@yahoo.com
Number of paper pages: 12
Abstract: This paper describes a new rule discovery algorithm called Distributed Relational Inductive Learning DRILA, which has been developed as part of ongoing research of the Inductive Learning Algorithm (ILA) [11], and its extension ILA2 [12] which were built to learn from a single table, and the Relational Inductive Learning Algorithm (RILA) [13], [14] which was developed to learn from a group of interrelated tables, i.e. a centralized database. DRILA allows discovery of distributed relational rules using data from distributed relational databases. It consists of a collection of sites, each of which maintains a local database system, or a collection of multiple, logically interrelated databases distributed over a computer network. The basic assumption of the algorithm is that objects to be analyzed are stored in a set of tables that are distributed over many locations. Distributed relational rules discovered would either be used in predicting an unknown object attribute!
value, or they can be used to extract the hidden relationship between the objects' attribute values. The rule discovery algorithm, developed, was designed to use data available from many locations (sites), any possible 'connected' schema at each location where tables concerned are connected by foreign keys. In order to have a reasonable performance, the 'hypotheses search' algorithm was implemented to allow construction of new hypotheses by refining previously constructed hypotheses, thereby avoiding the work of re-computing. Unlike many other relational learning algorithms, the DRILA algorithm does not need its own copy of distributed relational data to process it. This is important in terms of the scalability and usability of the distributed relational data mining solution that has been developed. The architecture proposed can be used as a framework to upgrade other propositional learning algorithms to relational learning.
Keywords: Distributed Relational Rule Induction, Rule Selection Strategies, Inductive Learning, Ila, Ila2, Rila, Drila
EXTENSION of the file: .doc
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How Did you learn about congress: Mrs. Nawal Maraqa / Nawal@nyit.edu.jo
IP ADDRESS: 79.173.196.205
