Impact of Index Structures on Temporal Database Performance

Kvet, Michal

doi:10.1109/ems.2016.012

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Furthermore, the study has shown figures regarding sole access to the disk. In the future, it could be interesting applying ML techniques to the traditional spatial and temporal locality algorithms [23], that run to place the expected data pages in the buffer pool. Nevertheless, an automated ML and dynamic approach seem to be a promising technique to reduce access time and overall latency when dealing with indexed tables accessed by any data mining algorithms.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Intelligent Indexing—Boosting Performance in Database Applications by Recognizing Index Patterns

2020

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An issue that most databases face is the static and manual character of indexing operations. This old-fashioned way of indexing database objects is proven to affect the database performance to some degree, creating downtime and a possible impact in the performance that is usually solved by manually running index rebuild or defrag operations. Many data mining algorithms can speed up by using appropriate index structures. Choosing the proper index largely depends on the type of query that the algorithm performs against the database. The statistical analyzers embedded in the Database Management System are neither always accurate enough to automatically determine when to use an index nor to change its inner structure. This paper provides an algorithm that targets those indexes that are causing performance issues on the databases and then performs an automatic operation (defrag, recreation, or modification) that can boost the overall performance of the Database System. The effectiveness of proposed algorithm has been evaluated with several experiments developed and show that this approach consistently leads to a better resulting index configuration. The downtime of having a damaged, fragmented, or inefficient index is reduced by increasing the chances for the optimizer to be using the proper index structure.

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Section: Conclusion and Further Workmentioning

confidence: 99%