Plan Selection based on Query Clustering

Ghosh, Antara; Parikh, Jignashu; Sengar, Vibhuti; Haritsa, Jayant R.

doi:10.1016/b978-155860869-6/50024-x

Cited by 41 publications

(33 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A closely related piece of work is PLASTIC [6]. Like PPQO, PLASTIC incrementally maintains clusters of incoming queries and avoids optimizing a new query if it is "close enough" to a previously seen cluster.…”

Section: Related Workmentioning

confidence: 99%

Progressive Parametric Query Optimization

Bizarro

Bruno

DeWitt

2009

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Abstract-Commercial applications usually rely on precompiled parameterized procedures to interact with a database. Unfortunately, executing a procedure with a set of parameters different from those used at compilation time may be arbitrarily suboptimal. Parametric query optimization (PQO) attempts to solve this problem by exhaustively determining the optimal plans at each point of the parameter space at compile time. However, PQO is likely not cost-effective if the query is executed infrequently or if it is executed with values only within a subset of the parameter space. In this paper, we propose instead to progressively explore the parameter space and build a parametric plan during several executions of the same query. We introduce algorithms that, as parametric plans are populated, are able to frequently bypass the optimizer but still execute optimal or near-optimal plans.

show abstract

Section: Related Workmentioning

confidence: 99%

Progressive Parametric Query Optimization

Bizarro

Bruno

DeWitt

2009

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…The work in [5] talks about attribute similarity but focuses on numeric data and on conclusions about similarity that can be deduced from the workload. Furthermore, in [15] queries are classified according to their structural similarity; yet, the authors focus on features that differentiate queries with respect to optimization plans. The only work relevant to ours is that of [6], where overall semantic similarity of queries is explored.…”

Section: Query Similaritymentioning

confidence: 99%

GrouPeer: Dynamic clustering of P2P databases

Kantere¹,

Tsoumakos²,

Sellis³

et al. 2009

Information Systems

View full text Add to dashboard Cite

Abstract. Sharing structured data in a P2P network is a challenging problem, especially in the absence of a mediated schema. The standard practice of answering a consecutively rewritten query along the propagation path often results in significant loss of information. On the opposite, the use of mediated schemas requires human interaction and global agreement, both during creation and maintenance. In this paper we present GrouPeer, an adaptive, automated approach to both issues in the context of unstructured P2P database overlays. By allowing peers to individually choose which rewritten version of a query to answer and evaluate the received answers, information-rich sources left hidden otherwise are discovered. Gradually, the overlay is restructured as semantically similar peers are clustered together. Experimental results show that our technique produces very accurate answers and builds clusters that are very close to the optimal ones by contacting a very small number of nodes in the overlay.

show abstract

“…Queries in this group also exhibit high sequential and random IO usage. Cluster 4 represents shortrunning, trivial complexity queries with a varying amount of tables being joined (3)(4)(5)(6)(7)(8). Finally, Cluster 1 appears to represent moderate-complexity queries with a smaller number of tables being joined (1-5) and exhibiting high CPU utilization.…”

Section: Identifying Class Characteristicsmentioning

confidence: 99%

“…al. [7] use clustering to reduce the complexity of selecting an optimal access plan for a query. In their approach, similar queries are grouped together into clusters and the optimizer-generated plan for the cluster representative is used to execute all future queries assigned to the cluster, resulting in significantly improved query optimization times.…”

Section: Related Workmentioning

confidence: 99%

Developing a characterization of business intelligence workloads for sizing new database systems

Wasserman

Martin

Skillicorn

et al. 2004

Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP

View full text Add to dashboard Cite

Computer system sizing involves estimating the amount of hardware resources needed to support a new workload not yet deployed in a production environment. In order to determine the type and quantity of resources required, a methodology is required for describing the new workload. In this paper, we discuss the sizing process for database management systems and describe an analysis for characterizing business intelligence (BI) workloads, using the TPC-H benchmark as our workload basis. The characterization yields four general classes of queries, each with different characteristics. Our approach for sizing a BI application's database tier quantifies a new BI workload in terms of the response time goals and mix of the different query classes obtained from the characterization analysis.

show abstract

Plan Selection based on Query Clustering

Cited by 41 publications

References 8 publications

Progressive Parametric Query Optimization

Progressive Parametric Query Optimization

GrouPeer: Dynamic clustering of P2P databases

Developing a characterization of business intelligence workloads for sizing new database systems

Contact Info

Product

Resources

About