SenseCluster for Exploring Large Data Repositories

Exploring and making sense of large data repositories has become a daunting task. This is especially the case for end users who often have limited access to the data due to the complexity of the retrieval process and limited availability of IT support for developing custom queries and reports based on the data. Consequently, traditional interfaces are no longer meeting these requirements. Instead, novel interfaces are required to fully support the sense making process. In this paper, we followed a design science approach and introduced a query clustering system (Sense Cluster) that could serve as a quick exploration tool for making better sense of large data repositories. We also present an evaluation of the effectiveness of our artifact using cognitive walkthroughs.