An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
AJAX: an extensible data cleaning tool
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Exploiting statistics on query expressions for optimization
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Conceptual modeling for ETL processes
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
Continuous queries over data streams
ACM SIGMOD Record
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Optimizing ETL Processes in Data Warehouses
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Deciding the physical implementation of ETL workflows
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Partition-based workload scheduling in living data warehouse environments
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
E-ETL: framework for managing evolving etl processes
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
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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.