NDLI: Cardinality estimation in ETL processes

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Content Provider	ACM Digital Library
Author	Lehner, Wolfgang Kiefer, Tim Thiele, Maik
Abstract	The cardinality estimation in ETL processes is particularly difficult. Aside from the well-known SQL operators, which are also used in ETL processes, there are a variety of operators without exact counterparts in the relational world. In addition to those, we find operators that support very specific data integration aspects. For such operators, there are no well-examined statistic approaches for cardinality estimations. Therefore, we propose a black-box approach and estimate the cardinality using a set of statistic models for each operator. We discuss different model granularities and develop an adaptive cardinality estimation framework for ETL processes. We map the abstract model operators to specific statistic learning approaches (regression, decision trees, support vector machines, etc.) and evaluate our cardinality estimations in an extensive experimental study.
Starting Page	57
Ending Page	64
Page Count	8
File Format	PDF
ISBN	9781605588018
DOI	10.1145/1651291.1651302
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cardinality estimation Real-time data warehouse Etl
Content Type	Text
Resource Type	Article

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Cardinality estimation in ETL processes