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Authors: Amit Rudra ; Raj P. Gopalan and N. R. Achuthan

Affiliation: Curtin University, Australia

Keyword(s): Sampling, Approximate Query Processing, Data Warehousing, Inverse Simple Random Sample without Replacement (SRSWOR).

Related Ontology Subjects/Areas/Topics: Data Warehouses and OLAP ; Databases and Information Systems Integration ; Enterprise Information Systems ; Query Languages and Query Processing

Abstract: For highly selective queries, a simple random sample of records drawn from a large data warehouse may not contain sufficient number of records that satisfy the query conditions. Efficient sampling schemes for such queries require innovative techniques that can access records that are relevant to each specific query. In drawing the sample, it is advantageous to know what would be an adequate sample size for a given query. This paper proposes methods for picking adequate samples that ensure approximate query results with a desired level of accuracy. A special index based on a structure known as the k-MDI Tree is used to draw samples. An unbiased estimator named inverse simple random sampling without replacement is adapted to estimate adequate sample sizes for queries. The methods are evaluated experimentally on a large real life data set. The results of evaluation show that adequate sample sizes can be determined such that errors in outputs of most queries are within the acceptable lim it of 5%. (More)

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Paper citation in several formats:
Rudra, A.; P. Gopalan, R. and R. Achuthan, N. (2013). Selecting Adequate Samples for Approximate Decision Support Queries. In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS; ISBN 978-989-8565-59-4; ISSN 2184-4992, SciTePress, pages 46-55. DOI: 10.5220/0004444200460055

@conference{iceis13,
author={Amit Rudra. and Raj {P. Gopalan}. and N. {R. Achuthan}.},
title={Selecting Adequate Samples for Approximate Decision Support Queries},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS},
year={2013},
pages={46-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004444200460055},
isbn={978-989-8565-59-4},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS
TI - Selecting Adequate Samples for Approximate Decision Support Queries
SN - 978-989-8565-59-4
IS - 2184-4992
AU - Rudra, A.
AU - P. Gopalan, R.
AU - R. Achuthan, N.
PY - 2013
SP - 46
EP - 55
DO - 10.5220/0004444200460055
PB - SciTePress

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