Templated Search over Relational Databases

Businesses and large organizations accumulate increasingly large amounts of customer interaction data. Analysis of such data holds great importance for tasks such as strategic planning and orchestration of sales/marketing campaigns. However, discovery and analysis over heterogeneous enterprise data can be challenging. Primary reasons for this are dispersed data repositories, requirements for schema knowledge, and difficulties in using complex user interfaces. As a solution to the above, we propose a TEmplated Search paradigm (TES) for exploring relational data that combines the advantages of keyword search interfaces with the expressive power of question-answering systems. The user starts typing a few keywords and TES proposes data exploration questions in real time. A key aspect of our approach is that the questions displayed are diverse to each other and optimally cover the space of possible questions for a given question-ranking framework. Efficient exact and provably approximate algorithms are presented. We show that the Templated Search paradigm renders the potentially complex underlying data sources intelligible and easily navigable. We support our claims with experimental results on real-world enterprise data.