A Model-Forest Based Horizontal Fragmentation Approach for Disjunctive Deductive Databases
Disjunctive deductive databases (DDDBs) can capture indeenite information, i.e., imprecise or partial knowledge, of the real world. In this paper we present a method for horizontally fragmenting a DDDB based on the minimal-model forest approach. A minimal-model forest of a DDDB D is a collection of minimal-model trees of D such that each tree represents a set of facts that is disjoint from the set of facts represented in any other tree of D. Eventually, each tree T in the forest is assigned to a fragment along with the rules that utilize the facts represented in T to infer new facts. This approach minimizes the amount of data in D that needs to be processed for any query of D by taking the advantage of the natural partition of data that may appear in D.