Event Cube: An Organized Approach for Mining and Understanding Anomalous Aviation Events

NASA NNX08AC35A (IVHM: Integrated Vehicle Health Management) (01/01/08-12/31/10, Extended to 05/15/2012)

 

Contact Information

 

Jiawei Han, PI
Department of Computer Science
University of Illinois, Urbana-Champaign
201 N. Goodwin 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 Co-PIs and Collaborator

·         ChengXiang Zhai (co-PI): Department of Computer Science, University of Illinois at Urbana-Champaign

·         Latifur Khan (co-PI): Department of Computer Science, University of Texas at Dallas

·         Vicent Ng (co-PI): Department of Computer Science, University of Texas at Dallas

·         Bhavani Thuraisingham (co-PI): Department of Computer Science, University of Texas at Dallas

·         Anne Kao (Collaborator):  Boeing

List of Supported Students and Staff

 

·         University of Illinois at Urbana-Champaign

o   Bolin Ding, Yanen Li, Cindy Xide Lin, Jialu Liu, Yinyao Yu, Duo Zhang, Bo Zhao

·         University of Texas at Dallas

o   Md. Arshad Ul Abedin, Salim Ahmed, Greg Hellings, Qing Chen

Project Award Information

  • Award Number: NSF CNS 09-31975
  • Duration: 09/01/2009—08/31/2012
  • Title: NSF CPS: Small: Collaborative Research: Foundations of Cyber-Physical Networks
  • Keywords:  cyber-physical networks; sensor network analysis; information network analysis; data mining; pattern discovery; spatiotemporal data analysis; efficiency and scalability; applications

Project Summary

Cyber-Physical Systems (CPS) is a joint initiative between the Directorate for Computer and Information Science and Engineering (CISE) and the Directorate for Engineering (ENG). By abstracting from the particulars of specific application domains, the CPS program aims to reveal cross-cutting fundamental scientific and engineering principles that underpin the integration of cyber and physical elements across all application sectors. The CPS program also supports the development of methods and tools as well as hardware and software components, run-time substrates, and systems based upon these principles to expedite and accelerate the realization of cyber-physical systems in a wide range of applications. Furthermore, the program aims to create a new research and education community committed to the study and application of cyber-physical system innovations, through the establishment of a CPS Virtual Organization (CPS-VO) and regular PI meetings.

This project represents a joint effort of two highly active and experienced researchers in the fields of (i) data mining and information network analysis, and (ii) physical/sensor networking and medical care network applications, respectively. Combining our expertise and joining our research efforts, we propose to take care-net, i.e., medical care cyber-physical network, as its key application and investigate the foundations, methodologies, algorithms and implementations of cyber-physical networks. In particular, we propose to study three critical issues in the design and construction of cyber-physical networks. (1) Rare event detection and analysis in cyber-physical data streams, including rare event detection, rare event analysis, and multidimensional analysis for cyber-physical streams, (2) Reliable and trusted data analysis with cyber-physical networks, including veracity analysis for object consolidation and redundancy elimination, entity resolution and information integration, and feedback interaction between cyber- and physical- networks, and (3) Spatiotemporal data analysis in cyber-physical networks, including spatiotemporal cluster analysis and sequential pattern mining in cyber-physical networks and evolution of cyber-physical networks. The research results are to be published in various research and application forums and be integrated into the educational programs at UIUC.  The progress of the project and the research results are also disseminated via the project Web site (http://www.cs.uiuc.edu/homes/hanj/projs/cps.htm).

Publications and Products:

Journal articles

1.      Hector Gonzalez, Jiawei Han, Hong Cheng, Xiaolei Li, Diego Klabjan, and Tianyi Wu, “Modeling Massive RFID Datasets: A Gateway-Based Movement-Graph Approach", IEEE Transactions on Knowledge and Data Engineering, 22(1):90-104, 2010.

2.      TianyiWu, Yuguo Chen, and Jiawei Han, “Re-Examination of Interestingness Measures in Pattern Mining: A Unified Framework", Data Mining and Knowledge Discovery, Jan. 2010.

3.      Hongyan Liu, Yuan Lin, and Jiawei Han, “Methods for Mining Frequent Items in Data Streams: An Overview", Knowledge and Information Systems, (Online: Nov 11, 2009) (DOI 10.1007/s10115-009-0267-2)

4.      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, 2010.

5.      Xiaofei He, Deng Cai, Yuanlong Shao, Hujun Bao, and Jiawei Han, “Laplacian Regularized Gaussian Mixture Model for Data Clustering", IEEE Transactions on Knowledge and Data Engineering, 2010.

6.      Duo Zhang, ChengXiang Zhai, Jiawei Han, Ashok Srivastava, and Nikunj Oza, “Topic Modeling for OLAP on Multidimensional Text Databases: Topic Cube and its Applications", Statistical Analysis and Data Mining, 2(5-6):378-395, 2009.

7.      Hongyan Liu, Xiaoyu Wang, Jun He, Jiawei Han, Dong Xin, Zheng Shao, “Top-down mining of frequent closed patterns from very high dimensional data", Information Sciences, 179(7):899-924, 2009.

Book and Book Chapters

 

1.      Tarek Abdelzaher, Mohammad Khan, Hieu Le, Hossein Ahmadi, and Jiawei Han, “Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned", in Alfredo Cuzzocrea (ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data, IGI Global, 2010.

2.      Hector Gonzalez, Jiawei Han, Hong Cheng, Tianyi Wu, “Warehousing RFID and Location-BasedSensor Data", Chapter 3 of Intelligent Techniques for Warehousing and Mining Sensor Network Data, Alfredo Cuzzocrea (ed.), IGI Global, 2009.

3.      Xifeng Yan and Jiawei Han, “Graph Indexing", Edited by Charu C. Aggarwal and Haixun Wang (eds.), Managing and Mining Graph Data, Kluwer Academic Publishers, 2009, pp. 143-164.

4.      Hong Cheng and Xifeng Yan and Jiawei Han, “Mining Graph Patterns", Edited by Charu C. Aggarwal and HaixunWang (eds.), Managing and Mining Graph Data, Kluwer Academic Publishers, 2009, pp. 353-382.

5.      Harvey J. Miller and Jiawei Han, “Geographic Data Mining and Knowledge Discovery: An Overview", Harvey J. Miller and Jiawei Han (eds.), Geographic Data Mining and Knowledge Discovery, 2nd ed., Taylor & Francis, 2009, pp. 1-26.

6.      Yvan Bedard and Jiawei Han, “Fundamentals of Spatial Data Warehousing and Geographic Knowledge Discovery", Harvey J. Miller and Jiawei Han (eds.), Geographic Data Mining and Knowledge Discovery, 2nd ed., Taylor & Francis, 2009, pp. 45-68.

7.      Jiawei Han, Jae-Gil Lee and Micheline Kamber, “An Overview of Clustering Methods in Geographic Data Analysis", Harvey J. Miller and Jiawei Han (eds.), Geographic Data Mining and Knowledge Discovery, 2nd ed., Taylor & Francis, 2009, pp. 149-188.

8.      Jiawei Han, “Data Mining", in M. Tamer  Ozsu and Ling Liu (eds.), Encyclopedia of Database Systems, Springer, 2009

9.      Hong Cheng and Jiawei Han, “Frequent Itemsets and Association Rules", in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia of Database Systems, Springer, 2009

10.  Hong Cheng and Jiawei Han, “Pattern-Growth Methods", in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia of Database Systems, Springer, 2009

11.  Jiawei Han and Bolin Ding, “Stream Mining", in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia of Database Systems, Springer, 2009

12.  Ronnie Alves, Joel Ribeiro, Orlando Belo, and Jiawei Han, “Ranking Gradients in Multi-Dimensional Spaces", as Chapter 11, in T. M. Nguyen (ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, IGI Global, 2009. ISBN: 978-1-60566-748-5.

13.  Jiawei Han and Jing Gao, “Research Challenges for Data Mining in Science and Engineering", in H. Kargupta, et al., (eds.), Next Generation of Data Mining, Chapman & Hall/CRC, 2009, pp.3-28.

14.  Feida Zhu, Xifeng Yan, Jiawei Han and Philip S. Yu, \Mining Frequent Approximate Sequential Patterns", in H. Kargupta, et al., (eds.), Next Generation of Data Mining, Chapman & Hall/CRC, 2009, pp. 69-90.

15.  Jiawei Han and Xiaolei Li, “Classification and Clustering for Homeland Security", in John G. Voeller (ed.), Wiley Handbook of Science and Technology for Homeland Security, John Wiley & Sons, 2009.

 

Refereed Conference Publications

1.      Ming Ji, Yizhou Sun, Marina Danilevsky and Jiawei Han, “Graph Regularized Transductive Classification on Heterogeneous Information Networks", Proc. 2010 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'10), Barcelona, Spain, Sept. 2010.

2.      Hyung Sul Kim, Sangkyum Kim, Tim Weninger, Jiawei Han, and Tarek Abdelzaher, “NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification", Proc. 2010 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'10), Barcelona, Spain, Sept. 2010.

3.      Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham, “Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space", Proc. 2010 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'10), Barcelona, Spain, Sept. 2010.

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

5.      Peixiang Zhao and Jiawei Han, “On Graph Query Optimization in Large Networks", Proc. 2010 Int. Conf. on Very Large Data Bases (VLDB'10), Singapore, Sept. 2010.

6.      Zhijun Yin, Manish Gupta, Tim Weninger, and Jiawei Han, “A Unified Framework for Link Recommendation Using Random Walks", Proc. 2010 Int. Conf. on Advances in Social Networks Analysis and Mining (ASONAM'10), Odense, Denmark, Aug. 2010.

7.      Tim Weninger, Surya Ramachandran, Daniel Greene, Jack Hart, Anand Kancherlapalli, William H. Hsu, and Jiawei Han, “Speech-Assisted Radiology System for Retrieval, Reporting and Annotation", Proc. 2010 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'10), (demo paper), Washington D.C., July 2010.

8.      Jing Gao, Feng Liang,Wei Fan, Chi Wang, Yizhou Sun, and Jiawei Han, “Community Outliers and their Efficient Detection in Information Networks", Proc. 2010 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'10), Washington D.C., July 2010.

9.      Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays, and Peter Nye, “Mining Hidden Periodic Behaviors for Moving Objects", Proc. 2010 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'10), Washington D.C., July 2010.

10.  Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, and Jiawei Han, “A Statistical Model for Popular Event Tracking in Social Communities", Proc. 2010 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'10), Washington D.C., July 2010.

11.  Chi Wang, Jiawei Han, Yuntao Jia, Jie Tang, Duo Zhang, Yintao Yu, and Jingyi Guo, “Mining Advisor-Advisee Relationships from Research Publication Networks", Proc. 2010 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'10), Washington D.C., July 2010.

12.  Cuiping Li, Jiawei Han, Xin Jin, Yizhou Sun, Yintao Yu, and Tianyi Wu, “Fast Computation of SimRank for Static and Dynamic Information Networks”, Proc. 2010 Int. Conf. on Extending Data Base Technology (EDBT'10), Lausanne, Switzerland, March 2010.

13.  Tianyi Wu, Yizhou Sun, Cuiping Li, and Jiawei Han, “Region-based Online Promotion Analysis", Proc. 2010 Int. Conf. on Extending Data Base Technology (EDBT'10), Lausanne, Switzerland, March 2010.

14.  Dustin Bortner and Jiawei Han, "Progressive Clustering of Networks Using Structure-Connected Order of Traversal", Proc. 2010 Int. Conf. on Data Engineering (ICDE'10), Long Beach, CA, March 2010.

15.  Bolin Ding, Bo Zhao, Cindy Xide Lin, Jiawei Han, Chengxiang Zhai, "TopCells: Keyword-Based Search of Top-k Aggregated Documents in Text Cube", Proc. 2010 Int. Conf. on Data Engineering (ICDE'10), Long Beach, CA, March 2010.

16.   Xifeng Yan, Bin He, Feida Zhu, Jiawei Han, "Top-K Aggregation Queries Over Large Networks", Proc. 2010 Int. Conf. on Data Engineering (ICDE'10), Long Beach, CA, March 2010.

17.  Yizhou Sun, Jiawei Han, Jing Gao, and Yintao Yu, “iTopicModel: Information Network-Integrated Topic Modeling", Proc. 2009 Int. Conf. on Data Mining (ICDM'09), Miami, FL, Dec. 2009.

18.  Xiao Yu, Lu An Tang, and Jiawei Han, “Filtering and Refinement: A Two-Stage Approach for Efficient and Effective Anomaly Detection", Proc. 2009 Int. Conf. on Data Mining (ICDM'09), Miami, FL, Dec. 2009.

19.  Samson Hauguel, ChengXiang Zhai, and Jiawei Han, “Parallel PathFinder Algorithms for Mining Structures from Graphs", Proc. 2009 Int. Conf. on Data Mining (ICDM'09), Miami, FL, Dec. 2009.

20.  Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, and Jiawei Han, “Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models", Proc. NIPS 2009 Neural Info. Processing Systems Conf. (NIPS'09), Vancouver, B.C., Canada, Dec. 2009.

21.  Peixiang Zhao, Jiawei Han, Yizhou Sun, “P-Rank: A Comprehensive Structural Similarity Measure over Information Networks", Proc. 2009 ACM Conf. on Information and Knowledge Management (CIKM'09), Hong Kong, China, Nov. 2009.

22.  Chandrasekar Ramachandran, Rahul Malik, Xin Jin, Jing Gao, Klara Nahrstedt, and Jiawei Han, “VideoMule: A Consensus Learning Approach to Multi-Label Classification from Noisy User-Generated Videos", Proc. 2009 ACM Int. Conf. on Multimedia (ACM-MM'09), Beijing, China, Oct. 2009.

23.  Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham, “Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams", Proc. 2009 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'09), Bled, Slovenia, Sept. 2009.

24.  Min-Soo Kim and Jiawei Han, "A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks", Proc. 2009 Int. Conf. on Very Large Data Bases (VLDB'09), Lyon, France, Aug. 2009.

25.  Tianyi Wu, Dong Xin, Qiaozhu Mei, and Jiawei Han, "Promotion Analysis in Multi-Dimensional Space", Proc. 2009 Int. Conf. on Very Large Data Bases (VLDB'09), Lyon, France, Aug. 2009.

26.  Chen Chen, Cindy Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, and Jiawei Han, "Mining Graph Patterns Efficiently via Randomized Summaries", Proc. 2009 Int. Conf. on Very Large Data Bases (VLDB'09), Lyon, France, Aug. 2009.

27.  Yintao Yu, Cindy X. Lin, Yizhou Sun, Chen Chen, Jiawei Han, Binbin Liao, Tianyi Wu, ChengXiang Zhai, Duo Zhang, and Bo Zhao, “iNextCube: Information Network-Enhanced Text Cube", Proc. 2009 Int. Conf. on Very Large Data Bases (VLDB'09) (system demo), Lyon, France, Aug. 2009.

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

Area Background

This project is based on the previous research on data mining, information network analysis, 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 and Kamber, 2006, (Hastie, Tibshirani, and Friedman,  2ed., 2009) and (Miller and Han 2009).

 

Potential Related Projects

§  Any project related to cyber-physical systems, sensor networks, information and social network analysis, spatiotemporal data mining, and knowledge discovery.

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

Online software:  Online software related to this project can be downloaded at www.illimine.cs.uiuc.edu

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