An Overview of Moving Object Trajectory Compression Algorithms <s> Semantic Trajectory Compression. <s> In the light of rapidly growing repositories capturing the movement trajectories of people in spacetime, the need for trajectory compression becomes obvious. This paper argues for semantic trajectory compression (STC) as a means of substantially compressing the movement trajectories in an urban environment with acceptable information loss. STC exploits that human urban movement and its large---scale use (LBS, navigation) is embedded in some geographic context, typically defined by transportation networks. STC achieves its compression rate by replacing raw, highly redundant position information from, for example, GPS sensors with a semantic representation of the trajectory consisting of a sequence of events . The paper explains the underlying principles of STC and presents an example use case. <s> BIB001 </s> An Overview of Moving Object Trajectory Compression Algorithms <s> Semantic Trajectory Compression. <s> There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an ob- vious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the move- ment. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illus- trates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories. <s> BIB002 </s> An Overview of Moving Object Trajectory Compression Algorithms <s> Semantic Trajectory Compression. <s> Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS ( P aralleled R oad-Network-Based Trajectory Compr ess 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. <s> BIB003
Semantic information in road network has more practical significance in representing moving object trajectories that are collected from limited moving objects. Semantic trajectory compression stores trajectories in the form of semantic information in road network instead of trajectory points, compresses spatial information in trajectory data by spatial compression methods, and compresses temporal information in trajectory data by temporal compression techniques, until some halting condition is met. The new and novel representation for trajectories that replaces trajectory data by the form of semantic information in road network was proposed by Schmid et al. BIB001 in 2009. Many researchers have paid their attention to semantic trajectory compression since then. Semantic trajectory compression was applied to human motion dataset in urban area by Richter et al. BIB002 which identifies the relevant reference points along the trajectory, determines all possible descriptions of how movement continues from here, and exploits motion feature description of reference points to compress trajectory data. Song et al. BIB003 proposed a new framework, namely, paralleled road-network-based trajectory compression, to effectively compress trajectory data under road network constraints. Different from existing works, PRESS proposed 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. Semantic trajectory compression is only suitable for limited moving objects, such as movement in road network, urban movement, and orbital trajectory, which will get a more realistic significance result in compressing trajectories of limited moving objects.