Spatiotemporal Compression Techniques for Moving Point Objects
Moving object data handling has received a fair share of attention over recent years in the spatial database community. This is understandable as positioning technology is rapidly making its way into the consumer market, not only through the already ubiquitous cell phone but soon also through small, on-board positioning devices in many means of transport and in other types of portable equipment. It is thus to be expected that all these devices will start to generate an unprecedented data stream of time-stamped positions. Sooner or later, such enormous volumes of data will lead to storage, transmission, computation, and display challenges. Hence, the need for compression techniques.
Although previously some work has been done in compression for time series data, this work mainly deals with one-dimensional time series. On the other hand, they are good for short time series and in absence of noise, two characteristics not met by moving objects.
We target applications in which present and past positions of objects are important, so focus on the compression of moving object trajectories. The paper applies some older techniques of line generalization, and compares their performance against algorithms that we specifically designed for compressing moving object trajectories.
Unable to display preview. Download preview PDF.
- 2.Abdelguerfi, M., Givaudan, J., Shaw, K., Ladner, R.: The 2-3TR-tree, a trajectoryoriented index structure for fully evolving valid-time spatio-temporal datasets. In: Proc. 10th ACM-GIS, pp. 29–34. ACM Press, New York (2002)Google Scholar
- 3.Zhu, H., Su, J., Ibarra, O.H.: Trajectory queries and octagons in moving object databases. In: Proc. 11th CIKM, pp. 413–421. ACM Press, New York (2002)Google Scholar
- 6.Agarwal, P.K., Guibas, L.J., Edelsbrunner, H., Erickson, J., Isard, M., Har- Peled, S., Hershberger, J., Jensen, C., Kavraki, L., Koehl, P., Lin, M., Manocha, D., Metaxas, D., Mirtich, B., Mount, D., Muthukrishnan, S., Pai, D., Sacks, E., Snoeyink, J., Suri, S., Wolfson, O.: Algorithmic issues in modeling motion. ACM Computing Surveys 34, 550–572 (2002)CrossRefGoogle Scholar
- 7.Meratnia, N., de By, R.A.: A new perspective on trajectory compression techniques. In: Proc. ISPRS DMGIS 2003, Québec, Canada, October 2-3 (2003) (s.p.)Google Scholar
- 8.Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer Graphics: Principles and Practice, 2nd edn. Addison-Wesley, Reading (1990)Google Scholar
- 9.Shatkay, H., Zdonik, S.B.: Approximate queries and representations for large data sequences. In: Su, S.Y.W. (ed.) Proc. 12th ICDE, New Orleans, Louisiana, USA, pp. 536–545. IEEE Computer Society, Los Alamitos (1996)Google Scholar
- 10.Keogh, E.J., Chu, S., Hart, D., Pazzani, M.J.: An online algorithm for segmenting time series. In: Proc. ICDM 2001, Silicon Valley, California, USA, pp. 289–296. IEEE Computer Society, Los Alamitos (2001)Google Scholar
- 11.Tobler, W.R.: Numerical map generalization. In: Nystuen, J.D. (ed.) IMaGe Discussion Papers. Michigan Interuniversity Community of Mathematical Geographers, University of Michigan, Ann Arbor (1966)Google Scholar
- 12.Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10, 112–122 (1973)Google Scholar
- 14.Jenks, G.F.: Linear simplification: How far can we go? Paper presented to the Tenth Annual Meeting, Canadian Cartographic Association (1985)Google Scholar
- 17.Hershberger, J., Snoeyink, J.: Speeding up the Douglas-Peucker line-simplification algorithm. In: Proc. 5th SDH, Charleston, South Carolina, USA, vol. 1, pp. 134–143. University of South Carolina (1992)Google Scholar
- 18.Nanni, M.: Distances for spatio-temporal clustering. In: Decimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD 2002), Portoferraio (Isola d’Elba), Italy, pp. 135–142 (2002)Google Scholar
- 19.Jasinski, M.: The compression of complexity measures for cartographic lines. Technical report 90–1, National Center for Geographic Information and Analysis, Department of Geography. State University of New York at Buffalo, New York, USA (1990)Google Scholar