In this project, we explore diversity in the context of large-scale and numeric multi-dimensional databases. We develop query processing methods and tools for browsing and exploratory queries. Such queries should return diverse results, each contributing to the novelty and diversity of the result set. We propose the idea of diverse matching that utilize subspace similarities to avoid the curse of dimensionality. Our approach is based on optimizing the type of partial proximity each result possesses. In particular, we maximize the coverage of attributes that are found similar in the result set, while minimizing the overlap of these sets attributes. For large-scale databases, we study a browsing method over metric indices based on angular pruning.

We also have another project on multi-dimensional big data. Finally, our past research is here.