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

Abstract

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.

Efficient Alphabetic Text-based Tree Indexing

“…Experimental results show that BM+-tree has a meaningfully better query processing efficiency than M-tree. Alberto Arteta Albert et al [11] have provided machine learning based an algorithm which achieves an automatic operation of indexing. This artificial neural network-based algorithm boosts the overall performance of the Database System.…”