BDD-Based Combinatorial Keyword Query Processing under a Taxonomy Model

Abstract

Digital libraries are one of the key systems for an IT society, and supporting easy access to them is an important technical issue between a human and an intelligent system. Here we consider a publish/subscribe system for digital libraries which continuously evaluates queries over a large repository containing document descriptions. The subscriptions, the query expressions and the document descriptions, all rely on a taxonomy that is a hierarchically organized set of keywords, or terms. The digital library supports insertion, update and removal of a document. Each of these operations is seen as an event that must be notified only to those users whose subscriptions match the document‘s description. In this chapter, the authors present a novel method of processing such keyword queries. Our method is based on Binary Decision Diagram (BDD), an efficient data structure for manipulating large-scale Boolean functions. The authors compile the given keyword queries into a BDD under a taxonomy model. The number of possible keyword sets can be exponentially large, but the compiled BDD gives a compact representation, and enabling a highly efficient matching process. In addition, our method can deal with any Boolean combination of keywords from the taxonomy, while the previous result considered only a conjunctive keyword set. In this chapter, they describe the basic idea of their new method, and then the authors show their preliminary experimental result applying to a document set with large-scale keyword domain under a real-life taxonomy structure.