Mining Dense Regions from Vehicular Mobility in Streaming Setting
The detection of congested areas can play an important role in the development of systems of traffic management. Usually, the problem is investigated under two main perspectives which concern the representation of space and the shape of the dense regions respectively. However, the adoption of movement tracking technologies enables the generation of mobility data in a streaming style, which adds an aspect of complexity not yet addressed in the literature. We propose a computational solution to mine dense regions in the urban space from mobility data streams. Our proposal adopts a stream data mining strategy which enables the detection of two types of dense regions, one based on spatial closeness, the other one based on temporal proximity. We prove the viability of the approach on vehicular data streams in the urban space.
KeywordsDense Region Urban Space Dense Road Temporal Proximity Entrepreneurship Ecosystem
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- 2.Gama, J., Gaber, M.M.: Learning from Data Streams: Processing Techniques in Sensor Networks. Springer (November 2007)Google Scholar
- 4.Jensen, C.S., Lin, D., Ooi, B.C., Zhang, R.: Effective density queries on continuously moving objects. In: Liu, L., Reuter, A., Whang, K.-Y., Zhang, J. (eds.) ICDE, p. 71. IEEE Computer Society (2006)Google Scholar
- 6.Wang, W., Yang, J., Muntz, R.R.: Sting: A statistical information grid approach to spatial data mining. In: Jarke, M., Carey, M.J., Dittrich, K.R., Lochovsky, F.H., Loucopoulos, P., Jeusfeld, M.A. (eds.) VLDB, pp. 186–195 (1997)Google Scholar