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FOOD Index: A Multidimensional Index Structure for Similarity-Based Fuzzy Object Oriented Database Models

Yazıcı, Adnan
Ince, Cagri
A fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.