Clustering a DAG for CAD Databases
IEEE Transactions on Software Engineering
Cactis: a self-adaptive, concurrent implementation of an object-oriented database management system
ACM Transactions on Database Systems (TODS)
The performance and utility of the Cactis implementation algorithms
Proceedings of the sixteenth international conference on Very large databases
Principles of static clustering for object-oriented databases
Principles of static clustering for object-oriented databases
Self-adaptive, on-line reclustering of complex object data
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
On the Optimality of the Probability Ranking Scheme in Storage Applications
Journal of the ACM (JACM)
On-line reorganization in object databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
OCB: A Generic Benchmark to Evaluate the Performances of Object-Oriented Database Systems
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Partition-Based Clustering in Object Bases: From Theory to Practice
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
VOODB: A Generic Discrete-Event Random Simulation Model To Evaluate the Performances of OODBs
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
DBMSs on a Modern Processor: Where Does Time Go?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Dynamic Reorganization of Object Databases
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
TMOS: A Transactional Garbage Collector
POS-9 Revised Papers from the 9th International Workshop on Persistent Object Systems
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
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Ever since the 'early days' of database management systems, clustering has proven to be one of the most effective performance enhancement techniques for object oriented database management systems. The bulk of the work in the area has been on static clustering algorithms which re-cluster the object base when the database is static. However, this type of re-clustering cannot be used when 24-hour database access is required. In such situations dynamic clustering is required, which allows the object base to be reclustered while the database is in operation. We believe that most existing dynamic clustering algorithms lack three important properties. These include: the use of opportunism to imposes the smallest I/O footprint for re-organisation; the re-use of prior research on static clustering algorithms; and the prioritisation of re-clustering so that the worst clustered pages are re-clustered first. In this paper, we present OPCF, a framework in which any existing static clustering algorithm can be made dynamic and given the desired properties of I/O opportunism and clustering prioritisation. In addition, this paper presents a performance evaluation of the ideas suggested above.The main contribution of this paper is the observation that existing static clustering algorithms, when transformed via a simple transformation framework such as OPCF, can produce dynamic clustering algorithms that out-perform complex existing dynamic algorithms, in a variety of situations. This makes the solution presented in this paper particularly attractive to real OODBMS system implementers who often prefer to opt for simpler solutions.