Efficiently Supporting Multiple Similarity Queries for Mining in Metric Databases

Metric databases are databases where a metric distance function is defined for pairs of database objects. In such databases, similarity queries in the form of range queries or k-nearest neighbor queries are the most important queries. In traditional query processing, single queries are issued independently by different users. In many data mining applications, however, the database is typically explored by iteratively asking similarity queries for answers of previous similarity queries. In this paper, we introduce a generic scheme for such data mining algorithms and we develop a method to transform such algorithms in a way that they can use multiple similarity queries, i.e. sets of queries issued simultaneously. We investigate two orthogonal approaches, reducing I/O cost as well as CPU cost, to speed-up the processing of multiple similarity queries. The proposed techniques apply to any type of similarity query and to an implementation based on an index or using a sequential scan. Parallelization yields an additional impressive speed-up. An extensive performance evaluation confirms the efficiency of our approach and we conclude that multiple similarity queries should be provided as a basic DBMS operation in order to support many data mining applications in metric databases.