Usability analysis of compression algorithms for position data streams

With the increasing use of sensor technology, the compression of sensor data streams is getting more and more important to reduce both the costs of further processing as well as the data volume for persistent storage. A popular method for sensor data compression is to smooth the original measurement curve by an approximated curve, which is bounded by a given maximum error value. Measurement values from positioning systems like GPS are an interesting special case, because they consist of two spatial and one temporal dimension. Therefore various standard techniques for approximation calculations like regression or line simplification algorithms cannot be directly applied. In this paper, we portray our stream data management system NexusDS and an operator for compressing sensor data. For the operator, we implemented various compression algorithms for position data streams. We present the required adaptations and the different characteristics of the compression algorithms as well as the results of our evaluation experiments, and compare them with a map matching approach, specifically developed for position data.

[1]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[2]  Dimitrios Gunopulos,et al.  Online amnesic approximation of streaming time series , 2004, Proceedings. 20th International Conference on Data Engineering.

[3]  H. Alt.  Discrete Geometric Shapes Matching Interpolation and Approximation A Survey , 2009 .

[4]  Christian S. Jensen,et al.  Tracking of Moving Objects with Accuracy Guarantees , 2007, Spatial Data on the Web.

[5]  Lukasz Golab,et al.  Issues in data stream management , 2003, SGMD.

[6]  Bernhard Mitschang,et al.  Preprocessing Position Data of Mobile Objects , 2008, The Ninth International Conference on Mobile Data Management (mdm 2008).

[7]  Ling Liu,et al.  MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries , 2006, IEEE Transactions on Mobile Computing.

[8]  Nabil H. Mustafa,et al.  Near-Linear Time Approximation Algorithms for Curve Simplification , 2005, Algorithmica.

[9]  Ouri Wolfson,et al.  Spatio-temporal data reduction with deterministic error bounds , 2003, DIALM-POMC.

[10]  Bernhard Mitschang,et al.  NexusDS: a flexible and extensible middleware for distributed stream processing , 2009, IDEAS '09.

[11]  Laurence Boxer,et al.  Polygonal approximation by boundary reduction , 1993, Pattern Recognit. Lett..

[12]  Elena Ferrari,et al.  Spatial Data on the Web, Modeling and Management , 2007, Spatial Data on the Web.

[13]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[14]  Christian S. Jensen,et al.  Efficient tracking of moving objects with precision guarantees , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[15]  David Luebke,et al.  A Survey of Polygonal Simplification Algorithms , 1997 .

[16]  Ouri Wolfson,et al.  Nonmaterialized Motion Information in Transport Networks , 2005, ICDT.

[17]  Frank Dürr,et al.  Online trajectory data reduction using connection-preserving dead reckoning , 2008, MobiQuitous.

[18]  Paul S. Heckbert,et al.  Survey of Polygonal Surface Simplification Algorithms , 1997 .

[19]  Jia-Guu Leu,et al.  Polygonal approximation of 2-D shapes through boundary merging , 1988, Pattern Recognit. Lett..

[20]  Jian Pei,et al.  Fast and quality-guaranteed data streaming in resource-constrained sensor networks , 2008, MobiHoc '08.

[21]  Christian S. Jensen,et al.  The Infati Data , 2004, ArXiv.

[22]  Robert B McMaster,et al.  Automated Line Generalization , 1987 .