A Model-Forest Based Horizontal Fragmentation Approach for Disjunctive Deductive Databases

نویسندگان

  • Aparna Seetharaman
  • Yiu-Kai Ng
چکیده

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.

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تاریخ انتشار 1998