Efficient Processing of Expressive Node-Selecting Queries on XML Data in Secondary Storage: A Tree Automata-based Approach

Abstract

This chapter proposes a new, highly scalable, and efficient technique for evaluating node-selecting queries on XML trees, which is based on recent advances in the theory of tree automata. It discusses an efficient processing of expressive node-selecting queries on XML data in secondary storage. The experiments demonstrate the immediate practical usefulness of the approach of using tree automata for the evaluation of expressive node-selecting queries on trees in secondary storage, but more experiments are in place and under way. The approach has a number of interesting properties that need to be studied and possibly exploited in the future. Tree automata-based query processing lends itself to parallel query processing. Multiple query evaluation: Tree-Marking Normal Form (TMNF) programs can evaluate several queries (each one defined by one IDB predicate) in one program. It will be interesting to study how well Arb handles multiple queries. Precomputed information can be made use of through predicates available to the automata as part of the labeling information (each node may have any set of input predicates as label). The chapter suggests to work on ways of detecting, given a query, which parts of the data tree can be jumped over and do not have to be processed by the automata.