Semantic ETL - State-of-the-art and open research challenges

There has been an exponential growth and availability of data, both structured and unstructured. Massive amounts of data are available to be harvested for competitive business advantage, sound government policies, and new insights in a broad array of applications (including healthcare, biomedicine, energy, smart cities, genomics, transportation, etc.). Yet, most of this data is inaccessible for users, as we need technology and tools to find, transform, analyze, and visualize data in order to make it consumable for decision-making. Meaningful data integration in a schema-less, and complex Big Data world of databases is a big open challenge. This survey paper presents a holistic view of literature in data integration and Extract-Transform-Load (ETL) techniques. Limitations and gaps in existing approaches are identified and open research challenges are discussed.