Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
Optimal Selection of Secondary Indexes
IEEE Transactions on Software Engineering
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
On the automation of physical database design
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Microsoft index turning wizard for SQL Server 7.0
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Query processing in a system for distributed databases (SDD-1)
ACM Transactions on Database Systems (TODS)
The difficulty of optimum index selection
ACM Transactions on Database Systems (TODS)
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Self-managing technology in IBM DB2 universal database
Proceedings of the tenth international conference on Information and knowledge management
Toward autonomic computing with DB2 universal database
ACM SIGMOD Record
Exact and Approximate Algorithms for the Index Selection Problem in Physical Database Design
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
On Index Selection Schemes for Nested Object Hierarchies
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Framework for Automating Physical Database Design
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Index Selection in Relational Databases
MFDBS '89 Proceedings of the 2nd Symposium on Mathematical Fundamentals of Database Systems
On the Selection of an Optimal Set of Indexes
IEEE Transactions on Software Engineering
An efficient algorithm for the collapsing knapsack problem
Information Sciences: an International Journal
Selection and pruning algorithms for bitmap index selection problem using data mining
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses
Information Sciences: an International Journal
PARINDA: an interactive physical designer for PostgreSQL
Proceedings of the 13th International Conference on Extending Database Technology
An automated, yet interactive and portable DB designer
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
| Hi-index | 0.07 |
The index selection problem (ISP) concerns the selection of an appropriate index set to minimize the total cost for a given workload containing read and update queries. Since the ISP has been proven to be an NP-hard problem, most studies focus on heuristic algorithms to obtain approximate solutions. However, even approximate algorithms still consume a large amount of computing time and disk space because these systems must record all query statements and frequently request from the database optimizers the cost estimation of each query in each considered index. This study proposes a novel algorithm without repeated optimizer estimations. When a query is delivered to a database system, the optimizer evaluates the costs of various query plans and chooses an access path for the query. The information from the evaluation stage is aggregated and recorded with limited space. The proposed algorithm can recommend indexes according to the readily available information without querying the optimizer again. The proposed algorithm was tested in a PostgreSQL database system using TPC-H data. Experimental results show the effectiveness of the proposed approach.