With the advent of ubiquitous computing, we can easily collect large-scale trajectory data, say, from moving vehicles. This paper studies pattern-matching problems for trajectory data over road networks, which complements existing efforts focusing on (1) a spatiotemporal window query for location-based service or (2) euclidean space with no restriction. In contrast, we first identify some desirable properties for pattern-matching queries to the road network trajectories. As the existing work does not fully satisfy these properties, we develop (1) trajectory representation and (2) distance metric that satisfy all the desirable properties we identified. Based on this representation and metric, we develop efficient algorithms for three types of pattern-matching queries-whole, subpattern, and reverse subpattern matching. We analytically validate the correctness of our algorithms and also empirically validate their scalability over large-scale, real-life, and synthetic trajectory data sets.

In this paper, we propose a scalable instant code clone search engine for large-scale software repositories. While there are commercial code search engines available, they treat software as text and often fail to find semantically related code. Meanwhile, existing tools for semantic code clone searches take a "post-mortem" approach involving the detection of clones "after" the code development is completed, and hence, fail to return the results instantly. In clear contrast, we combine the strength of these two lines of existing research, by supporting instant code clone detection. To achieve this goal, we propose scalable indexing structures on vector abstractions of code. Our proposed algorithms allow developers to detect clones of a given code segment among the 1.7 million code segments from 492 open source projects in sub-second response times, without compromising the accuracy obtained by a state-of-the-art tool.