High-level ETL for semantic data warehouses

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using RDF model. This growth poses new requirements Business Intelligence technologies enable On-Line Analytical Processing (OLAP)-like analysis over data. incorporation into a Data Warehouse (DW) is not supported by traditional Extract-Transform-Load (ETL) tools because they do consider issues in integration process. In this paper, we propose layer-based process set high-level RDF-based ETL constructs required define, map, extract, process, transform, integrate, update, load (multidimensional) Different other tools, automate flows creating metadata at schema level. Therefore, it relieves developers from burden manual mapping operation We create prototype, named Construct (SETLCONSTRUCT), based on innovative proposed here. To evaluate SETLCONSTRUCT, multidimensional DW integrating Danish dataset an EU Subsidy compare with previous programmable framework SETLPROG terms productivity, development time, performance. evaluation shows that 1) SETLCONSTRUCT uses 92% fewer Number Typed Characters (NOTC) than SETLPROG, SETLAUTO (the extension for generating execution automatically) further reduces Used Concepts (NOUC) another 25%; 2) time almost cut half compared 27% SETLAUTO; 3) scalable has similar performance SETLPROG. also our approach qualitatively interviewing two experts.