DSA Thrust Seminar: Similarity Query Processing of High-dimensional Data

High-dimensional data of large volumes are becoming increasing popular, partly

due to the exponential growth of Machine/Deep Learning applications (such as

multimedia databases and recommendation systems).

In this talk, we will provide an overview of our recent works on this topic. Firstly, we will present various applications that generate and rely on high-dimensional data and their operations. Then, we will consider exact range query processing in a Hamming space. Prior approaches mainly adopt pruning based on the pigeonhole principle. We observed several inefficiencies including the non-tightness of the pruning, and inability to handle data skews. We propose a general form of the pigeonhole principle which allows variable partition size and threshold. Based on the new principle, we first develop efficient query processing and optimization algorithms that exploits data skewness to improve the query performance. Next, we will introduce several techniques to answer k-NN queries approximately, based on Locality Sensitive Hashing and Learned Hashing.