Dimensional Indexing for Targeted Case-Base Retrieval: The SMIRKS System

For a case-based application with a sizable case library, a common practice is to retrieve a set of cases and then reduce the set with a domain-specific similarity metric. This research investigates an alternative technique that constructs a number of two-dimensional (or multi-dimensional) indices using the principle of elaboration and specialization. By storing cases with these indices, we reduce the size of the retrieved candidate set and, in many instances, fetch a single case. This paper describes a case-based reasoner called SMIRKS. We investigate the retrieval performance by comparing linear search, 1-dimensional indexing, and 2dimensional indexing. The improvement in performance with dimensional indexing is found to be significant, especially in terms of the size of the retrieved candidate set. This paper describes the implementation of SMIRKS, presents the results of evaluation, and discusses some ideas on future applications that can utilize this technique.