A Hybrid Index Structure for Set-Valued Attributes Using Itemset Tree and Inverted List

Abstract

The use of set-valued objects is becoming increasingly commonplace in modern application domains, multimedia, genetics, the stock market, etc. Recent research on set indexing has focused mainly on containment joins and data mining without considering basic set operations on set-valued attributes. In this paper, we propose a novel indexing scheme for processing superset, subset and equality queries on set-valued attributes. The proposed index structure is a hybrid of itemset-transaction set tree of “frequent items” and an inverted list of “infrequent items” that take advantage of the developments in itemset research in data mining. In this hybrid scheme, the expectation is that basic set operations with frequent low cardinality sets will yield superior retrieval performance and avoid the high costs of construction and maintenance of item-set tree for infrequent large item-sets. We demonstrate, through extensive experiments, that the proposed method performs as expected, and yields superior overall performance compared to the state of the art indexing scheme for set-valued attributes, i.e., inverted lists.