Neural network-based decision support for incomplete database systems: Knowledge acquisition and performance analysis

Abstract

This paper investigates the design and implementation of a knowledge acquisition module as a part of a decision support system for handling large incomplete databases. Maybe algebra and attribute maybe algebra operations were introduced and implemented as extensions to the relational algebra operations. These operations give a user the opportunity to investigate the set of data containing null values (i.e. incomplete tuples) to draw his/her own conclusions. However, some of these operations may generate enormous and/or erroneous data. Moreover, under the maybe operations, some of the resultant data may not provide useful information for the user. A decision support system based on an artificial neural network is proposed to increase data qufllty in the presence of missing/incomplete information. Based on the learned knowledge, the neural network can filter out the undesirable data. In the proposed decision support system, a knowledge acquisition module plays a vital role in generating training data (training pairs). Data semantics of the underlying databases (e.g. data dependency conditions) is the main source from which such a knowledge can be acquired. It is demonstrated that the knowledge acquisition can be accomplished by using a two-level hierarchical structured model. Finally, some simulation results are presented to demonstrate the feasibility and performance of the proposed knowledge acquisition medule. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the titte of the publication and its date appear, and notice is given that eo ying is by ! permission of the Association for Computing Machinery. ocopy otherwise, or to republish requires a fee and/or specific pemussion. ** Dep~ent of Computer Science Iowa State University Ames, IA 50011