Interactive data analysis and its applications on multi-structured datasets

Abstract

Data analytics in databases has received a lot of attention in the database community as it is an effective process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. However, as dataset cardinality increases dramatically nowadays, it remains a challenge to make the analytical process scalable as well as keep the process interactive, visual intuitive and user controllable. As such, it is important to provide a framework to support data interactive analytics in a scalable manner.

This thesis first addresses a user preference query on top of multi-dimensional datasets. To further extend the interactive data analytics, we propose to apply the hierarchical browsing approach in the application of keyword search in databases. Finally, we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs.