Scheduling the allocation of data fragments in a distributed database environment: a machine learning approach

Different database fragmentation and allocation strategies have been proposed to partially replicate data in a partitioned, distributed database (DDB) environment. The replication strategies include database snapshots, materialized views, and quasi-copies. These strategies are 'static' and...

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Bibliographic Details
Published in:IEEE transactions on engineering management 1994-05, Vol.41 (2), p.194-207
Main Authors: Chaturvedi, A.R., Choubey, A.K., Jinsheng Roan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer network reliability
Costs
Data communication
Database systems
Delay
Distributed databases
Environmental economics
History
Information systems
Machine learning
Scheduling
Telecommunication network reliability
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Summary:Different database fragmentation and allocation strategies have been proposed to partially replicate data in a partitioned, distributed database (DDB) environment. The replication strategies include database snapshots, materialized views, and quasi-copies. These strategies are 'static' and do not adapt to the changes in the data usage patterns. Furthermore, they often require expensive update synchronizations to maintain data consistency and do not exploit the knowledge embedded in the query history. This paper describes a machine learning based time invariant fragmentation method (MLTIF) that acquires knowledge about the data usage patterns for each node. Based on this knowledge, MLTIF designs time invariant fragments and schedules its allocation and selective update for a specified time period. Simulation is used to compare the effectiveness of the MLTIF approach with that of full replication, materialized views, and nonreplication strategies. Initial results indicate that for most normal operating conditions, the MLTIF approach can be effective.< >
ISSN:0018-9391
1558-0040
DOI:10.1109/17.293386