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Index Selection for in-Memory Databases

Pratham L. Bajaj1 , Archana Ghotkar2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-7 , Page no. 129-132, Jul-2015

Online published on Jul 30, 2015

Copyright © Pratham L. Bajaj , Archana Ghotkar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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How to Cite this Paper

IEEE Style Citation: Pratham L. Bajaj , Archana Ghotkar, “Index Selection for in-Memory Databases,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.129-132, 2015.

MLA Style Citation: Pratham L. Bajaj , Archana Ghotkar "Index Selection for in-Memory Databases." International Journal of Computer Sciences and Engineering 3.7 (2015): 129-132.

APA Style Citation: Pratham L. Bajaj , Archana Ghotkar, (2015). Index Selection for in-Memory Databases. International Journal of Computer Sciences and Engineering, 3(7), 129-132.

BibTex Style Citation:
@article{Bajaj_2015,
author = {Pratham L. Bajaj , Archana Ghotkar},
title = {Index Selection for in-Memory Databases},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2015},
volume = {3},
Issue = {7},
month = {7},
year = {2015},
issn = {2347-2693},
pages = {129-132},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=588},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=588
TI - Index Selection for in-Memory Databases
T2 - International Journal of Computer Sciences and Engineering
AU - Pratham L. Bajaj , Archana Ghotkar
PY - 2015
DA - 2015/07/30
PB - IJCSE, Indore, INDIA
SP - 129-132
IS - 7
VL - 3
SN - 2347-2693
ER -

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Abstract

Index recommendation is an active research area in query performance tuning and optimization. Designing efficient indexes is paramount to achieving good database and application performance. In association with database engine, index recommendation technique need to adopt for optimal results. Different searching methods used for Index Selection Problem (ISP) on various databases and resemble knapsack problem and traveling salesman. The query optimizer reliably chooses the most effective indexes in the vast majority of cases. Loss function calculated for every column and probability is assign to column. Experimental results presented to evidence of our contributions.

Key-Words / Index Term

Query Performance, NPH Analysis, Index Selection Problem

References

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