Designing intelligent database systems through argumentation

Information systems play an ever-increasing key role in our society. In particular, data intensive applications are in constant demand and there is need of computing environments with much more intelligent capabilities than those present in today’s Data-base Management Systems (DBMS). Nowadays intelligent applications require better reasoning capabilities than those present in older data processing systems[7]. Argumentation frameworks appear to be an excellent starting-point for building such systems. Research in argumentation has provided important results while striving to obtain tools for common sense reasoning. As a result, argumentation systems have substantially evolved in the past few years, and this resulted in a new set of argument-based applications in diverse areas where knowledge representation issues play a major role. Clustering algorithms [6], intelligent web search [3], recommender systems [4, 3], and natural language assessment [2] are the outcome of this evolution. We claim that massive data processing systems can be combined with argumentation to obtain systems that administer and reason with large databases. These would result in a system that can extract and process information from massive databases and exhibit intelligent behavior and common sense reasoning as a by-product of the argumentation system used for the reasoning process. In this work, we present a specialization of the DeLP [5] system, called Database Defeasible Logic Programming (DB DeLP). This framework could be easily integrated with a relational database component to achieve a system capable of massive data processing and intelligent behavior. Next, we formally define this system. Eje: Agentes y Sistemas Inteligentes Red de Universidades con Carreras en Informática (RedUNCI)