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Prediction-based Online Trajectory Compression by Silva, Arlei; Raghavendra, Ramya; Srivatsa, Mudhakar; Singh, Ambuj K. is a scholarly article available to read on EtoBox.

What is Prediction-based Online Trajectory Compression about?

Recent spatio-temporal data applications, such as car-shar\-ing and smart cities, impose new challenges regarding the scalability and timeliness of data processing systems. Trajectory compression is a promising approach for scaling up spatio-temporal databases. However, existing techniques fail to address the online setting, in which a compressed version of a trajectory stream has to be maintained over time. In this paper, we introduce ONTRAC, a new framework for map-matched online trajectory compression. ONTRAC learns prediction models for suppressing updates to a trajectory database using training data. Two prediction schemes are proposed, one for road segments via a Markov model and another for travel-times by combining Quadratic Programming and Expectation Maximization. Experiments show that ONTRAC outperforms the state-of-the-art offline technique even when long update delays (4 mininutes) are allowed and achieves up to 21 times higher compression ratio for travel-times. Moreover, our approach increases database scalability by up to one order of magnitude.

Author

Silva, Arlei; Raghavendra, Ramya; Srivatsa, Mudhakar; Singh, Ambuj K.

Published

2016

Language

EN