Abstract—In the regime of “Big Data”, data compression
techniques take crucial part in preparation phase of data
analysis. It is challenging because statistical properties and
other characteristics need to be preserved while the size of data
need to be reduced. In particular, to compress trajectory data,
movement status (such as position, direction, and speed etc.)
need to be retained. Moreover, for the increasing demand of
real-time processing capability, “online” algorithms are
becoming more desirable in data analysis. In this paper, we introduce an On-Line Data Compression Algorithms for
Trajectories (OLDCAT), which is an elegant, fast algorithm to
effectively compress trajectory data to desirable volume. It is
able to deal with real-time data, and scalable to adapt to
different sensitivity, accuracy, and compression requirements.
An evaluation of its parameter settings and a case study are also
discussed in this paper.
Index Terms—Data compression, trajectory data, online algorithm.
Wang Ting is with SAP Asia Pte Ltd, Singapore (e-mail: firstname.lastname@example.org).
Cite:Ting Wang, "An Online Data Compression Algorithm for Trajectories (An OLDCAT)," International Journal of Information and Education Technology vol. 3, no. 4, pp. 480-487, 2013.