Mining Traffic Stream and Vehicle/pedestrian Networks

Philip S. YuProfessor & Wexler Chair in Information Technology

Computer Science Department

University of Illinois at Chicago

Problem Statement and Motivation

• With the advancement on sensor, GPS and wireless technologies, transportation system transforms from data poor to data rich.

• Challenges:• Real-time requirement• Complexity of the data

• Spatio-temporal correlation • Noisy or uncertain data• Privacy preservation

3

Prediction of congested areas

GPS applications

- database compaction through object simplification- faster pattern matching

4

Collision Detectioncollision detection can be more efficient using segmentation

- approximate object movement

Technical Approach

• Develop real-time stream processing capability to address monitoring type applications

• Develop new scalable mining techniques to discover traffic and traversal patterns

• Explore graph OLAP technique to zoom in/out a huge graph for analysis on different granularities

• Explore learning from heterogeneous sources to address lacking of training examples

Key Achievements and Future Goals

• Real-time data stream mining algorithms with concept drifts, and uncertainty

• Indexing and similarity search methods for trajectories

• Online Analytical Processing paradigms for Information Network

• Privacy preservation techniques• Learning from heterogeneous examples• Explore green technology

Publications

• C. Aggarwal, P.S. Yu, "A Framework for Clustering Uncertain Data Streams", IEEE Intl. Conf. on Data Engineering, 2008.

• A. Anagnostopoulos, M. Vlachos, E. Keogh, P.S. Yu, "Global Distance-based Segmentation of Trajectories", ACM KDD 2006.

• C. Aggarwal, P.S. Yu, "Privacy-Preserving Data Mining: Models and Algorithms", Springer, 2008.

• B. Fung, K. Wang, P.S.Yu, "Anonymizing Classification Data for Privacy Preservation", IEEE Trans. Knowledge and Data Eng., Vol. 19, No. 5, May 2007.

• X. Shi, Q. Liu, W. Fan, Q. Yang, P.S. Yu, "Predictive Modeling with Heterogeneous Sources", SIAM Data Mining Conference, 2010.

• C. Chen, X. Yan, F. Zhu, J. Han, P.S. Yu, "Graph OLAP: A Multi-dimensional Framework for Graph Data Analysis", Knowledge and Information Systems, Vol. 21. No. 1, 2009.

Publications

• B. Gedik, L. Liu, P. S. Yu, "ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks", IEEE Trans. Parallel Distributed Systems, 2007.

• B. Gedik, K.L. Wu, P.S. Yu, L. Liu, "MobiQual: QoS-aware Load Shedding in Mobile CQ Systems", IEEE Intl. Conf. on Data Engingeering, 2008.

• K.L. Wu, S.K. Chen, P.S. Yu, "Incremental Processing of Continual Range Queries over Moving Objects", IEEE Trans. Knowledge and Data Eng., Vol. 18, No. 11, 2006.

• W. Li, W.K. Ng, X.H. Dang, K. Zhang, P.S. Yu, "Density-Based Clustering of Data Streams at Multiple Resolutions", ACM Trans. Knowledge Discovery from Data, Vol. 3, No. 3, 2009.

• X. Gu, S. Papadimitriou, P.S. Yu, S.P. Chang "Toward Learning-based Failure Management for Distributed Stream Processing Systems", IEEE Intl. Conf. on Distributed Computing Systems, 2008.