NSF III-Core-Small: MoveMine: Mining Sophisticated Patterns and Actionable Knowledge from Massive Moving Object Data

National Science Foundation Award Number: NSF IIS 10-17362 (09/01/2010-08/31/2016)

 Award Abstract Link @ NSF

 

 

Contact Information

 

Jiawei Han,  PI
Department of Computer Science
University of Illinois, Urbana-Champaign
1304 West Springfield Ave. , Urbana, Illinois 61801 U.S.A.
Office: (217) 333-6903,   Fax: (217) 265-6494

E-mail: hanj at cs.uiuc.edu, URL: http://www.cs.uiuc.edu/~hanj

 

List of Supported Students and Staff

 

§  Zhenhui Li, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign (duration working on this project: 2010-2012)

§  Lu An Tang, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign  (duration working on this project: 2010-2013)

§  Manish Gupta, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign (duration working on this project: 2010-2013)

§  Jingjing Wang, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign (duration working on this project: 2012-present)

§  Chao Zhang, Ph.D. student, Department of Computer Science, University of Illinois at Urbana-Champaign (duration working on this project: 2013-present)

Project Award Information

§  Award Number: NSF IIS 10-17362 (09/01/2010-08/31/2016) (with no-cost extension)

§  Duration: NSF IIS 10-17362 (09/01/2010-08/31/2016) (with no-cost extension)

§  Title: NSF III-Core-Small: MoveMine: Mining Sophisticated Patterns and Actionable Knowledge from Massive Moving Object Data

§  Keywords:  Moving object data mining; multidimensional data analysis; pattern discovery; spatiotemporal data analysis; traffic mining; efficiency and scalability

Project Summary

This research project is to investigate principles and methods for uncovering sophisticated patterns and actionable knowledge from massive moving object data.  Thanks to the rapid progress and broad adoption of sensor, GPS, wireless network, and other advanced technologies, moving object data have been accumulating in unprecedented scale. However, moving object data could be dynamic, sparse, scattered, and noisy, and patterns and knowledge to be mined could be deeply hidden, sophisticated, and subtle.  The MoveMine project investigates effective and scalable methods for mining various kinds of complex patterns from dynamic and noisy moving object data, finding multiple interleaved periodic patterns, and performing in-depth multidimensional analysis of moving object data.  It integrates and extends multiple disciplinary approaches derived from spatiotemporal data analysis, data mining, pattern recognition, statistics, and machine learning.  The study takes bird and animal movement data and traffic data as the major sources of data for investigation.  However, developed methods can be applied to the analysis of many other kinds of moving object data for environmental study, traffic control, law enforcement, and protection of homeland security.  The study also addresses the issue of ensuring privacy and security protection while developing powerful pattern and knowledge discovery mechanisms.  The research results are to be published in various research and application forums and be integrated into the educational programs at UIUC.  The research results are also disseminated via the project Web site: (http://www.cs.uiuc.edu/homes/hanj/projs/movemine.htm).

Publications and Products:  

Note:  Please search and download all the papers in PDF, if available, at our group’s publication website by following the link: Selected research publications.

Books (authored or edited)

 

1.      Ashok N. Srivastava and Jiawei Han (eds.), Machine Learning and Knowledge Discovery for Engineering Systems Health Management: Detection, Diagnostics, and Prognostics, Chapman & Hall, 2011.

2.      Yizhou Sun and Jiawei Han, Mining Heterogeneous Information Networks: Principles and Methodologies, Morgan &Claypool Publishers, July 2012

3.      Manish Gupta, Jing Gao, Charu Aggarwal, and Jiawei Han, Outlier Detection for Temporal Data, Morgan & Claypool Publishers, 2014.

4.      Chi Wang and Jiawei Han, Mining Latent Entity Structures, Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool Publishers, 2015.

 

Journal articles

 

1.      Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Guofei Jiang, Alice Leung, and Thomas La Porta, "A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System", ACM Transactions on Knowledge Discovery from Data (TKDD), 9(3):16:1-16:35 (2015)

2.      Zhenhui Li, Jingjing Wang, and Jiawei Han, "e-Periodicity: Mining Event Periodicity from Incomplete Observations", IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(5): 1219-1232 (2015)

3.      Jae-Gil Lee, Jiawei Han, and Xiaolei Li, "A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness," IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(6): 1478-1490 (2015)

4.      Jun Li, Qiming Qin, Jiawei Han, Lu An Tang, Kin Hou Lei, "Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference", Transactions in GIS (T. GIS) 19(1): 1-18 (2015)

5.      Chao Zhang, Jiawei Han, Lidan Shou, Jiajun Lu, and Thomas F. La Porta, "Splitter: Mining Fine Grained Sequential Patterns in Semantic Trajectories", PVLDB 7(9): 769-780, 2014 (Also, Proc. 2014 Int. Conf. on Very Large Data Bases (VLDB'14), Hangzhou, China, Sept. 2014.)

6.      Manish Gupta, Jing Gao, Charu C. Aggarwal, and Jiawei Han, "Outlier Detection for Temporal Data: A Survey", IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(9): 2250-2267, 2014.

7.      Lu-An Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice Leung, Wen-Chih Peng, Thomas La Porta, “A Framework of Traveling Companion Discovery on Trajectory Data Streams", ACM Transactions on Intelligent Systems and Technology (ACM TIST), 5(1):3, 2013.

8.      Lu-An Tang, Xiao Yu, Sangkyum Kim, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta, “Trustworthiness Analysis of Sensor Data in Cyber-Physical Systems”, accepted by Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams, Journal of Computer and System Sciences (JCSS), 79(3): 383-401, 2013.

9.      Lu-An Tang, Xiao Yu, Sangkyum Kim, Jiawei Han, Wen-Chih Peng, Yizhou Sun, Alice Leung, Thomas La Porta, “Multidimensional Sensor Data Analysis in Cyber-Physical Systems: An Atypical Cube Approach”, International Journal of Distributed Sensor Networks, Vol. 2012, 2012.

10.  Zhenhui Li, Jiawei Han, Bolin Ding, and Roland Kays, “Mining Periodic Behaviors of Object Movements for Animal and Biological Sustainability Studies”, Data Mining and Knowledge Discovery, 24(2):355-386, 2012.

11.  Zhenhui Li, Jiawei Han, Ming Ji, Lu-An Tang, Yintao Yu, Bolin Ding, Jae-Gil Lee, and Roland Kays, "MoveMine: Mining Moving Object Data for Discovery of Animal Movement Patterns", ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2(4):37, 2011.

12.  Zhenhui Li, Jiawei Han, Ming Ji, Lu-An Tang, Yintao Yu, Bolin Ding, Jae-Gil Lee, and Roland Kays, “MoveMine: Mining Moving Object Data for Discovery of Animal Movement Patterns", ACM Transactions on Intelligent Systems and Technology (ACM TIST) (Special Issue on Computational Sustainability), 2(4):37, 2011.

13.  Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hong Cheng, “Mining Discriminative Patterns for Classifying Trajectories on Road Networks", IEEE Transactions on Knowledge and Data Engineering, 23(5):713-725, 2011.

14.  Zhenhui Li, Bolin Ding, Jiawei Han, and Roland Kays, “Swarm: Mining Relaxed Temporal Moving Object Clusters", PVLDB 3(1): 723-734, 2010. (Also, Proc. 2010 Int. Conf. on Very Large Data Bases (VLDB'10), Singapore, Sept. 2010.)

 

Book Chapters

 

1.      Zhenhui Li and Jiawei Han, "Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data", in Wesley W. Chu (ed.), Data Mining and Knowledge Discovery for Big Data, pp. 41-82, Springer, 2014.

 

Refereed Conference Publications

 

1.      Chao Zhang, Shan Jiang, Yucheng Chen, Yidan Sun, and Jiawei Han,"Fast Inbound Top-K Query for Random Walk with Restart", in Proc. of 2015 European Conf. on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECMLPKDD'15), Porto, Portugal, Sept. 2015.  (Received the Best Student Paper Runner-Up award at ECML/PKDD 2015)

2.      Chao Zhang, Yu Zheng, Xiuli Ma,  Jiawei Han, "Assembler: Efficient Discovery of Spatial Coevolving Patterns in Massive Geosensory Data", in Proc. of 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'15), Sydney, Australia, Aug. 2015

3.      Xiang Ren, Ahmed El-Kishky, Chi Wang, Fangbo Tao, Clare R. Voss, Heng Ji, Jiawei Han, "ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering", in Proc. of 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'15), Sydney, Australia, Aug. 2015

4.      Younghoon Kim, Jiawei Han, Cangzhou Yuan, "TOPTRAC: Topical Trajectory Pattern Mining", in Proc. of 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'15), Sydney, Australia, Aug. 2015

5.      Wei Feng, Chao Zhang, Wei Zhang, Jiawei Han, Jianyong Wang, Charu Aggarwal, Jianbin Huang, " STREAMCUBE: Hierarchical Spatio-temporal Hashtag Clustering for Event Exploration over the Twitter Stream", in Proc of 2015 IEEE Int. Conf on Data Engineering (ICDE'15), Seoul, Korea, Apr. 2015

6.      Honglei Zhuang, Jing Zhang, George Brova, Jie Tang,  Hasan Cam, Xifeng Yan and Jiawei Han, "Mining Query-Based Subnetwork Outliers in Heterogeneous Information Networks", in Proc. of 2014 IEEE Int. Conf. on Data Mining (ICDM'14), Shenzhen, China, Dec. 2014.

7.      Manish Gupta, Arun Mallya, Subhro Roy, Jason H. D. Cho, and Jiawei Han,"Local Learning for Mining Outlier Subgraphs from Network Datasets", in Proc. of 2014 SIAM Int. Conf.on Data Mining (SDM'14), Philadelphia, PA, April 2014.

8.      Manish Gupta, Jing Gao, Xifeng Yan, Hasan Cam, and Jiawei Han, "Top-K Interesting Subgraph Discovery in Information Networks", Proc. 2014 IEEE Int. Conf. on Data Engineering (ICDE'14), Chicago, IL, Mar. 2014.

9.      Manish Gupta, Jing Gao, Xifeng Yan, Hasan Cam, and Jiawei Han, “On Detecting Association-Based Clique Outliers in Heterogeneous Information Networks", Proc. of 2013 IEEE/ACM Int. Conf. on Social Networks Analysis and Mining (ASONAM'13), Niagara Falls, Canada, Aug. 2013

10.  Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, and Thomas La Porta, “Mining Lines in the Sand: On Trajectory Discovery From Untrustworthy Data in Cyber-Physical System", Proc. of 2013 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'13), Chicago, IL, Aug. 2013.

11.  Zhenhui Li, Jingjing Wang, and Jiawei Han, "Mining Periodicity for Sparse and Incomplete Event Data", Proc. of 2012 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, Aug. 2012

12.  Jingjing Wang and Bhaskar Prabhala, "Periodicity Based Next Place Prediction", Proc. of Workshop on Mobile Data Challenge by Nokia, Newcastle, UK, June 2012

13.  Lu-An Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice Leung, Chih-Chieh Hung, and Wen-Chih Peng, "On Discovery of Traveling Companions from Streaming Trajectories", Proc. 2012 IEEE Int. Conf. on Data Engineering (ICDE'12), Arlington, VA, Apr. 2012.

14.  Lu-An Tang, Xiao Yu, Sangkyum Kim, Jiawei Han, Yizhou Sun, Wen-Chih Peng, Hector Gonzalez, Sebastian Seith, "Multidimensional Analysis of Atypical Events in Cyber-Physical Data", Proc. 2012 IEEE Int. Conf. on Data Engineering (ICDE'12), Arlington, VA, Apr. 2012.

15.  Zhenhui Li, Cindy Xide Lin, Bolin Ding, and Jiawei Han, “Mining Significant Time Intervals for Relationship Detection”, Proc. of 2011 Int. Symp. on Spatial and Temporal Databases (SSTD'11), Minneapolis, MN, Aug. 2011.

16.  Lu-An Tang, Yu Zheng, Xing Xie, Jing Yuan, Xiao Yu, Jiawei Han, “Retrieving k-Nearest Neighboring Trajectories by a Set of Point Locations”, Proc. of 2011 Int. Symp. on Spatial and Temporal Databases (SSTD'11), Minneapolis, MN, Aug. 2011.

17.  Xiao Yu, Ang Pan, Lu-An Tang, Zhenhui Li and Jiawei Han, "Geo-Friends Recommendation in GPS-based Cyber-Physical Social Network", Proc. of 2011 Int. Conf. on Advances in Social Network Analysis and Mining (ASONAM'11), Kaohsiung, Taiwan, July 2011.

18.  Zhijun Yin, Liangliang Cao, Jiawei Han, Jiebo Luo, and Thomas Huang, “Diversified Trajectory Pattern Ranking in Geo-tagged Social Media”, Proc. of 2011 SIAM Conf. on Data Mining (SDM'11), Phoenix, AZ, Apr. 2011.

19.  Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang, “Geographical Topic Discovery and Comparison”, Proc. of 2011 Int. World Wide Web Conf. (WWW'11), Hyderabad, India, Mar. 2011 (Full paper).

 

Ph.D. Dissertations

 

1.      Lu An Tang, Ph.D., August 2013, thesis title: “Mining Sensor and Mobility Data in Cyber Physical Systems”, link to Ph.D. dissertation

2.      Manish Gupta, Ph.D., March 2013, thesis title: “Outlier Detection for Information Networks", link to Ph.D. dissertation

3.      Zhenhui Li, Ph.D., Sept. 2012, thesis title: “Mining periodicity and object relationship in spatial and temporal data", link to Ph.D. dissertation

 

Project Impact

 

§  Education:  Parts of the new research results are used in Data Mining courses (CS412, CS512) for both undergraduate and graduate students being taught in the Department of Computer Science, the University of Illinois at Urbana-Champaign.   Moreover, the research results have been and will continuously be published timely in international conferences and journals and be distributed world-wide for education and research.  The new progress will also be integrated into the new edition of our data mining textbook and other research collections.

§  Collaborations: For this project we have established collaborations with Boeing, ARL, NASA, HP Labs, IBM T.J. Watson Research Center, Yahoo! Research, Microsoft Research, and NCSA (National Center of Supercomputer Applications).  Through such collaborations we expect to have access to real datasets and applications and produce more research results.

 

Current and Future Activities

The following are some of the highlights of our ongoing work.  Please refer to the section: Publications and Products section for related references.

1.      Study object movement mining in the context of cyber-physical networks

2.      Study efficient methods for mining more sophisticated movement patterns than the state-of-the-art

3.      Study methods for anomaly detection for moving objects in sensor network environment

Area Background

 

This project is based on the previous research on data mining, spatiotemporal data analysis, and data cube and multidimensional analysis.   There have been many research papers published on these themes. Several textbooks on data mining, information retrieval and information network analysis provide good overviews of the principles and algorithms, including (Han, Kamber, and Pei, Data Mining, 3rd ed. 2011), and (Miller and Han, Geographical Data Mining and Knowledge Discovery, 2nd ed. 2009).

 

Area References

·         Ralf Hartmut Güting and Markus Schneider, Moving objects databases, Morgan Kaufmann, 2005.

·         Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011.

·         Hillol Kargupta, Jiawei Han, Philip Yu, Rajeev Motwani, and Vipin Kumar (eds.), Next Generation of Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series), Taylor & Francis, 2008.

·         Harvey Miller and Jiawei Han (eds.), Geographical Data Mining and Knowledge Discovery, 2nd edition, Taylor & Francis, 2009.

·         Philip S. Yu, Jiawei Han, and Christos Faloutsos (eds), Link Mining: Models, Algorithms, and Applications, Springer, 2010.

 

Potential Related Projects

·         Information Network Analysis and Discovery (Information Network Academic Research Center: Network Science-Collaborative Technology Alliance) (NSF IIS Infonet Project)

·         Knowledge Discovery in Cyberphysical Systems (NSF/CPS)

·         Sequential and Structured Pattern Discovery: Classification, Clustering and Outlier Analysis

·         Discovery of the Dynamics of Data Streams in Multi-Dimensional Space

·         Multidimensional Analysis and Ranking in Databases, Web, and Other Information Repositories

Project Web site URL:  http://www.cs.uiuc.edu/~hanj/projs/movemine.htm

Online software:  Online software can be downloaded at http://illimine.cs.uiuc.edu, and online system demo is at http://dm.cs.uiuc.edu/movemine

Online resources:  Research publications related to this project can be downloaded at Selected Publications