Towards a Data Warehouse Fed with Web Services

The role of data warehouse for business analytics cannot be undermined for any enterprise, irrespective of its size. But the growing dependence on web services has resulted in a situation where the enterprise data is managed by multiple, autonomous service providers. The goal of our work is to investigate and devise an approach to address the trade-off between scalability and adaptability in large scale integration with numerous ever-evolving web services. We present our prototype DaWeS (Data warehouse fed with Web Services) and explore how ETL using the mediation approach benefits this trade-off for enterprises with complex data warehousing requirements. The semantic web research community has proposed various standards like WSDL, WADL, hRESTS, SAWSDL for describing web service interface (API) the usage of which could have solved our requirement of automated integration. DaWeS looks to fill the current gap between the industry and research community by taking into account the key characteristics of the aforementioned description languages and using a declarative approach in order to reduce the manual effort. We also present to the semantic web research community the optimization heuristics (to reduce the API operation calls) and semantic challenges (auto-adaptability especially in the wake of an API change) devised while building DaWeS.