SETL: A programmable semantic extract-transform-load framework for semantic data warehouses

作者：

Highlights：

• This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools.

• SETL provides a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks.

• SETL supports semantic and traditional data sources, semantic integration, and creating or publishing a (MD) semantic DW.

• Using SETL, we perform a comprehensive experimental evaluation by producing a MD semantic DW that integrates a semantic and non semantic data sources.

• The evaluation shows that SETL improves considerably over the competing solutions/tools in terms of productivity, KB quality, and performance.

摘要

•This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools.•SETL provides a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks.•SETL supports semantic and traditional data sources, semantic integration, and creating or publishing a (MD) semantic DW.•Using SETL, we perform a comprehensive experimental evaluation by producing a MD semantic DW that integrates a semantic and non semantic data sources.•The evaluation shows that SETL improves considerably over the competing solutions/tools in terms of productivity, KB quality, and performance.

论文关键词：ETL,RDF,Semantic integration,Data warehouse,Semantic-aware,Knowledge base

论文评审过程：Received 14 May 2016, Accepted 29 January 2017, Available online 4 March 2017, Version of Record 7 June 2017.

论文官网地址：https://doi.org/10.1016/j.is.2017.01.005