Static grouping of small objects to enhance performance of a paged virtual memory
ACM Transactions on Computer Systems (TOCS)
Clustering a DAG for CAD Databases
IEEE Transactions on Software Engineering
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
A study of three alternative workstation server architectures for object-oriented database systems
Proceedings of the sixteenth international conference on Very large databases
The performance and utility of the Cactis implementation algorithms
Proceedings of the sixteenth international conference on Very large databases
A stochastic approach for clustering in object bases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Effective clustering of complex objects in object-oriented databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
On the performance of object clustering techniques
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
The knowledge base partitioning problem: mathematical formulation and heuristic clustering
Data & Knowledge Engineering
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Principles of static clustering for object-oriented databases
Principles of static clustering for object-oriented databases
Approximating block accesses in database organizations
Communications of the ACM
Building knowledge base management systems
The VLDB Journal — The International Journal on Very Large Data Bases
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In this paper, we address the problem of clustering graphs in object-oriented databases. Unlike previous studies which focused only on a workload consisting of a single operation, this study tackles the problem when the workload is a set of operations (method and queries) that occur with a certain probability. Thus, the goal is to minimize the expected cost of an operation in the workload, while maintaining a similarly low cost for each individual operation class.To this end, we present a new clustering policy based on the nearest-neighbor graph partitioning algorithm. We then demonstrate that this policy provides considerable gains when compared to a suite of well-known clustering policies proposed in the literature. Our results are based on two widely referenced object-oriented database benchmarks; namely, the Tektronix HyperModel and OO7.