The ACM SIGSPATIAL Cup 2012 is about map matching, a problem of correctly matching a sequence of GPS sampling points to the roads on a digital map. This paper describes one of the winning submissions of the competition. The approach applies multi-threading technology to map matching in order to reduce running time and we propose an improvement to the Hidden Markov Model (HMM) map matching algorithm.

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS ( <u>P</u>aralleled <u>R</u>oad-Network-Based Trajectory Compr<u>ess</u>ion ), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.

The ACM SIGSPATIAL Cup 2013 is about geo-fencing, a problem of determining whether sampled location points fall inside or within a certain distance from virtual perimeters for realworld geographic areas. This paper describes one of the winning submissions of the competition. The approach applies two novel techniques, namely Trajectory Partitioning (TP) and Boundary Simplification (BS). TP partitions the movement trajectory of an object into segments and processes the points within the same segment simultaneously. BS simplifies the polygons thus accelerates the geo-fencing process.

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