Towards a Programmable Semantic Extract-Transform-Load Framework for Semantic Data Warehouses

This paper presents 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.


In order to create better decisions for business analytics, organizations increasingly use external data, structured, semistructured and unstructured, 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 process. Also, current ETL tools neither support processing semantic-aware data nor create a Semantic Data Warehouse (DW) as a semantic repository of semantically integrated data. This paper describes SETL: a (Pythonbased) programmable Semantic ETL 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-aware data sources, semantic integration, and creating a semantic DW, composed of an ontology and its instances. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more handcoding) on a concrete use case, shows that SETL provides better performance, knowledge base quality and porgrammer productivity.

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

SETL Architecture

setl framework

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