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

This paper presents Semantic ETL (SETL), a unified framework for processing and integrating data semantically by bridging SW and DW technologies. SETL uses and extends SW tools and standards to overcome the limitations of the traditional ETL tools. Using SETL, the BI community can benefit by including semantic annotated data in their analytical processes and the SW community can benefit by having an MD view over semantic data for enabling OLAP-like analysis. Hence, it supports publishing better quality RDF datasets as well.


In order to create better decisions for business analytics, organizations increasingly use external structured, semi-structured, and unstructured data in addition to the (mostly structured) internal data. Current Extract-Transform-Load (ETL) tools are not suitable for this “open world scenario” because they do not consider semantic issues in the integration processing. Current ETL tools neither support processing semantic data nor create a semantic Data Warehouse (DW), a repository of semantically integrated data. This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools and supports developers by offering a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks. Thus it supports semantic data sources in addition to traditional data sources, semantic integration, and creating or publishing a semantic (multidimensional) DW in terms of a knowledge base. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more hand-coding) on a concrete use case, shows that SETL provides better programmer productivity, knowledge base quality, and performance.

Authors: Rudra Pratap Deb Nath, Katja Hose, Torben Bach Pedersen, and Oscar Romero

SETL Architecture

setl framework

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