H-trees: a dynamic associative search index for OODB
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
OODB indexing by class-division
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Dynamic maintenance of data distribution for selectivity estimation
The VLDB Journal — The International Journal on Very Large Data Bases
The Indispensability of Dispensable Indexes
IEEE Transactions on Knowledge and Data Engineering
Indexing OODB Instances based on Access Proximity
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Region Splitting Strategy for Physical Database Design of Multidimensional File Organizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The hcC-tree: An Efficient Index Structure for Object Oriented Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A configurable type hierarchy index for OODB
The VLDB Journal — The International Journal on Very Large Data Bases
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This paper presents a tunable two-dimensional class hierarchy indexing technique (2D-CHI) for object-oriented databases. We use a two-dimensional file organization as the index structure. 2D-CHI deals with the problem of clustering objects in a two-dimensional domain space consisting of the key attribute domain and the class identifier domain. In conventional class indexing techniques using one-dimensional index structures such as the B+-tree, the clustering property is exclusively owned by one attribute. These indexing techniques do not efficiently handle the queries that address both the attribute keys and the class identifiers. 2D-CHI enhances query performance by adjusting the degree of clustering between the key value domain and the class identifier domain based on the pre-collected usage pattern. For performance evaluation, we first compare 2D-CHI with the conventional class indexing techniques using an analytic cost model based on the assumption of uniform object distribution, and then, verify the cost model through experiments using the multilevel grid file as the two-dimensional index. We further perform experiments with non-uniform object distributions. Our experiments show that our proposed method does indeed build optimal class index structures regardless of query types and object distributions. We strongly believe that our paper significantly contributes to building a self-tunable database system by supporting automatically tunable index structure.