The multi-criteria evaluation of research efforts based on ETL software: from business intelligence approach to big data and semantic approaches.

Many industries and academia have devoted a lot of effort and money to creating and/or using good extract-transform-load (ETL) software suitable for their data analysis purposes since it is considered a key to their success. As a result, we find the valuable interventions of research efforts based on ETL software are divided according to well-known approaches such as Business Intelligence, Big Data, and/or Semantic. As a result, problems arise in keeping up with changes and handling the significant diversity in features across these approaches. Which results in disorientation in the finding, evaluation, and choice of an ETL for industries and academia facing their approaches needs. These problems inspire us to provide a contribution that uses the systematic-literature-review (SLR) method to collect 207 papers from three databases, namely, ScienceDirect, Springer, and IEEE, dated from 2010 to 2022, grouped based on both ETL approaches and their commonly used criteria, afterwards using an existing method that automatically identifies the adequate multicriteria method for this study, which gives us the analytical-hierarchy-process method to provide the best research paper according to the requirements of scientific literature. The result implies the great significance of this study in multiple ways, providing a global idea of research papers about ETL approaches, allowing customers to eliminate uncertainty from selecting an ETL according to their specific approach needs, preferences, and interests, and also enabling future researchers and developers of ETL to decide when to focus and how to make innovative contributions to fill gaps in the literature. [ABSTRACT FROM AUTHOR]