Paper Info
Title
View and index selection for query-performance improvement: quality-centered algorithms and heuristics
Abstract
Selecting and precomputing indexes and materialized views, with the goal of improving query-processing performance, is an important part of database-performance tuning. The significant complexity of the view- and index-selection problem may result in high total cost of ownership for database systems. In this paper, we develop efficient methods that deliver user-specified quality of the set of selected views and indexes when given view- and index-based plans as problem inputs. Here, quality means proximity to the globally optimum performance for the input query workload given the input query plans. Our experimental results and comparisons on synthetic and benchmark instances demonstrate the competitiveness of our approach and show that it provides a winning combination with end-to-end view- and index-selection frameworks such as those of [1, 2].
Year
DOI
Venue
2008
10.1145/1458082.1458261
CIKM
Keywords
Field
DocType
problem input,index-selection problem,index selection,end-to-end view,query-processing performance,query-performance improvement,input query workload,user-specified quality,quality-centered algorithm,optimum performance,input query plan,index-selection framework,selected view,integer linear programming,materialized views,indexation,database system
Data mining,Mathematical optimization,Workload,Computer science,Total cost of ownership,Lagrangian heuristic,Heuristics,Integer programming,Index selection,Materialized view,Performance improvement
Conference
Citations 
PageRank 
References 
3
0.38
4
Authors
4
Name
Order
Citations
PageRank
Maxim Kormilitsin1251.90
Rada Chirkova245036.53
Y. Fathi313719.10
Matthias F. M. Stallmann416619.38