Principal
Investigator
Jiawei Han
Department
of Computer Science
2132
Email: hanj at cs.uiuc.edu
URL: http://www.cs.uiuc.edu/homes/hanj
Co-PI
Philip S. Yu
Department
of Computer Science
Rm 1138 SEOL
Email: psyu at cs.uic.edu
URL: http://www.cs.uic.edu/~psyu
Co-PI
Xifeng Yan
Computer
Science Department
University of California at Santa
Barbara
Rm 1111,
Email: xyan
at cs.ucsb.edu
URL: http://www.cs.ucsb.edu/~xyan
Future
Directions
Based on our study on information
network analysis and data mining, we identify the following several promising
research directions on this promising and rising research theme with potential
broad and deep impact to science, engineering and society.
1. Knowledge discovery in large scale
heterogeneous information networks:
Various
kinds of hidden knowledge can be discovered from heterogeneous information
networks, by exploration of the power of links and “information
redundancy” in interconnected networks, e.g., clustering, classification,
ranking, pattern discovery, and outlier analysis of information networks, with
tons of algorithms can be developed and lots of applications can be
explored. This is a fertile land of
research and may have deep implications in data mining, network science, and
their broad applications.
2. OLAP and similarity search in
heterogeneous information networks:
Structuring
information networks in multi-dimensional space may facilitate search, OLAP,
multidimensional analysis and interactive mining of massive, heterogeneous
information networks. This is an
exciting and emerging research frontier, with many applications. Interesting research topics in this frontier
may include summarization of information networks, indexing and similarity
search of information networks, OLAP and cube construction/materialization of
information networks.
3. Evolution of large-scale, temporal
information-associated information networks:
Considering
information network often have temporal information associated with, and the
discovery of trends, evolution regularities and outliers/anomalies along with
time is another important task, with broad applications.
4. Knowledge discovery in
cyber-physical information networks:
Sensors and
GPS system may be connected into sensor networks and they are also connected
with some information network entities.
The interconnected information and sensor networks will form a
cyber-physical network and therefore pose many challenging research issues on
information discovery and search in cyber-physical networks.
5. Integration of text search and
text information analysis with multi-dimensional information network analysis:
Nodes and
links in an information network may contain rich text information, such as
blogs, product descriptions, forums, discussions, audio and video information,
and moreover, documents could be linked together by co-references, lines of
following-up discussions, or other functions, forming text-based information
networks. Searching and mining of such
information networks poses many new challenge research issues and increases the
power of information network analysis.
6. Mining large databases: An
information network analysis approach:
One may
view database as a gigantic information network where data are inter-connected
and information-related entities (objects).
Thus information network analysis methods can be developed to analyze
large, relatively structured databases.
7. Web mining by integration of Web
structure discovery and information network analysis:
One may
view web as interconnected information networks instead of isolated objects
stored as a data repository. Information network analysis methods can be
developed to analyze database data, which may facilitate mining information in
large databases.
8. Data cleaning, data integration
and data validation by information network analysis:
Using
interconnected, often redundant information in a networked environment, one can
often perform intelligent and effective data cleaning, data integration and
data validation (such as veracity analysis) by further development of
information network analysis functions.
9. Role discovery, concept hierarchy
discovery and ontology enrichments by information network analysis:
It is often
important to generate ontology and concept hierarchies for a particular domain,
and even domain experts may disagree each other on
such information but often consensus can be built by sophisticated information
network analysis methods.
10. Ranking and promotion analysis by
information network analysis and for information network analysis:
One may
often want to cluster, rank and promote objects in data analysis and such functions
are desirable for information network analysis.
Ranking queries and promotion queries have been studied in databases and
it is important to re-examine their interactions with information network
analysis, especially how to help ranking and promotion analysis in a database
if we view database as an information networks, and how to perform ranking and
promotion analysis in information networks.
Keywords
Information network analysis
online
analytical processing (OLAP)
data cube
knowledge discovery
and data mining
graph
summarization
graph mining
efficiency and
scalability
Project
Summary
Information networks have been
fast expanding and attracted broad interests in recent years, ranging from
intrusion pattern detection to social community discovery. Typical information
networks include communication networks, social networks, the Web, and
biological networks. In contrast to the rising popularity and increasing scale
of information networks, there is no general analytical processing framework
available to information networks. The lack of such framework makes sensible
navigation and interactive knowledge exploration virtually impossible in
large-scale networks.
As information networks continue to grow in applications
such as social networks and the Web, supporting Online Analytical Processing
(OLAP) operations on large networks becomes critical to many next generation
graph-intensive applications. In this proposal, we present the Information
Network OLAP Framework (called Infonet-OLAP), an effort to develop a general
system that exploits OLAP concepts and measures unique in the graph space,
explores constraints and monotonicity hidden in these
measures, and performs discovery-driven OLAP operations for fast and accurate
knowledge discovery. We will further support the Infonet-OLAP
framework by structure discovery, network summarizations, and self quality
assurance of underlying networks. If successful, our techniques would simplify
information network analytical processing and transform existing ad hoc graph
exploratory work into a uniŻed framework as
traditional OLAP does to multidimensional data analysis.
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
§
Collaborations: For this
project we have established collaborations with Army Research Lab, NASA, HP
Labs,
Publications
and Products
Edited
Books
1. H. J.
Miller and J. Han (eds.), Geographic Data Mining and Knowledge
Discovery, 2nd ed., Springer Verlag, 2009.
2. Hillol Kargupta, Jiawei Han, Philip S. Yu, and Rajeev Motwani
(eds.), Next Generation of Data Mining, (Chapman & Hall/CRC Data
Mining and Knowledge Discovery Series), 2009 (605 + xxiv pages).
Articles
in Refereed Journals
1. Mohammad
M. Masud, Jing Gao, Latifur Khan, Jiawei Han, and Bhavani Thuraisingham, “Classification
and Novel Class Detection in Concept-Drifting Data Streams under Time
Constraints", IEEE Transactions on Knowledge and Data Engineering,
accepted Feb. 2010.
2. Deng Cai, Xiaofei
He, and Jiawei Han, “Locally
Consistent Concept Factorization for Document Clustering", IEEE
Transactions on Knowledge and Data Engineering, 22,
2010, accepted 14-Jan-2010.
3. 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.
4. Tianyi Wu, Yuguo Chen, and Jiawei Han, “Re-Examination of
Interestingness Measures in Pattern Mining: A Uni_ed
Framework", Data Mining and Knowledge Discovery, 2010
(in print) (online pub. Jan. 05, 2010: DOI 10.1007/s10618-009-0161-2)
5. Charu C. Aggarwal, Chen Chen and Jiawei Han, “The
Inverse Classification Problem", Journal of Computer Science and
Technology, accepted, Dec. 2009.
6. 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)
7. 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, accepted, Nov. 2009.
8. 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, accepted, Nov. 2009.
9. 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.
10. 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.
11. Hailiang Chen, Hongyan Liu, Jiawei Han, Xiaoxin Yin, “Exploring
Optimization of Semantic Relationship Graph for Multi-relational Bayesian
Classification", Decision Support Systems, 2009.
Online publication complete: 13-AUG-2009. DOI information:
10.1016/j.dss.2009.07.004.
12. Chen Chen, Xifeng Yan, Feida Zhu, Jiawei Han, Philip S.
Yu, “Graph OLAP: A Multi-Dimensional Framework for Graph Data Analysis", Knowledge
and Information Systems (KAIS), 21(1):41-63, 2009.
Selected Publications
in Refereed Books and Monographs
1. Hector
Gonzalez, Jiawei Han, Hong Cheng, Tianyi
Wu, “Warehousing RFID and Location-Based Sensor Data",
Chapter 3 of Intelligent Techniques for Warehousing and Mining
Sensor Network Data, Alfredo Cuzzocrea (ed.),
IGI Global, 2009.
2. 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.
3. 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.
4. 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.,
5. 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.,
6. 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.
7. Jiawei Han, “Data Mining",
in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia
of Database Systems, Springer, 2009
8. Hong
Cheng and Jiawei Han, “Frequent
Itemsets and Association Rules",
in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia
of Database Systems, Springer, 2009
9. Hong
Cheng and Jiawei Han, “Pattern-Growth
Methods", in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia
of Database Systems, Springer, 2009
10. Jiawei Han and Bolin Ding, “Stream
Mining", in M. Tamer Ozsu and Ling Liu (eds.), Encyclopedia
of Database Systems, Springer, 2009
11. Ronnie Alves, Joel Ribeiro,
12. 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.
13. 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.
14. 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.
Selected
Publications in Refereed Conference Proceedings
1. Mohammad
M. Masud, Jing Gao,
Latifur Khan, Jiawei Han,
and Bhavani Thuraisingham,
“Classification and Novel Class Detection in Data Streams with Active
Mining ", Proc. 2010 Paci_c-Asia
Conf. on Knowledge Discovery and Data Mining (PAKDD'10),
2. Cindy Xide Lin, Yintao Yu, Jiawei Han, and Bing Liu, “Hierarchical
Clustering of Webpages via Cross-Page and In-Page
Link Structures", Proc. 2010 Pacific-Asia Conf.
on Knowledge Discovery and Data Mining (PAKDD'10),
3. Mohammad
Mai_ Hasan Khan, Hieu K.
Le, Michael LeMay, Parya Moinzadeh, Lili Wang, Yong Yang,
Dong K. Noh, Tarek Abdelzaher,
Carl A. Gunter, Jiawei Han, Xin
Jin, “Diagnostic Powertracing
for Sensor Node Failure Analysis", Proc.
2010 Int. Conf. on Information Processing in Sensor Networks
(IPSN'10), Stockholm, Sweden, April, 2010.
4. Xin Jin, Scott Spangler, Rui
Ma, and Jiawei Han, “Topic
Initiator Detection on the World Wide Web", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010.
5. Tim Weninger, William H. Hsu, and Jiawei
Han, “CETR Content Extraction via Tag Ratios", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010.
6. Liangliang Cao, Andrey Del Pozo, Xin Jin, Jiebo Luo, Jiawei
Han, and Thomas S. Huang, “RankCompete:
Simultaneous Ranking and Clustering of Web Photos", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010
(poster paper).
7. Zhenhui Li, Ding Zhou, YunFang
Juan, and Jiawei Han, “Keyword
Extraction For Social Snippets", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010.
(poster paper)
8. Xide Lin, Bo Zhao, Tim Weninger,
Jiawei Han, and Bing Liu, “Entity
Relation Discovery from Web Tables and Links", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010.
(poster paper)
9. Zhijun Yin, Manish Gupta, Tim Weninger
and Jiawei Han, “LINKREC: A Uni_ed Framework for Link Recommendation with User
Attributes and Graph Structure", Proc. 2010 Int. World Wide Web
Conf. (WWW'10), Raleigh, NC, April 2010. (poster paper)
10. Jie Yu, Xin Jin, Jiawei Han, and Jiebo Luo, “Social Group Suggestion from
User Image Collections", Proc. 2010 Int. World Wide Web
Conf. (WWW'10), Raleigh, NC, April 2010. (poster paper)
11. Hyun Duk Kim, ChengXiang Zhai and Jiawei Han, “Aggregation
of Multiple Judgments for Evaluating Ordered Lists", Proc.
2010 European Conf. on Information Retrieval (ECIR'10),
12. Liangliang Cao, Jiebo Luo, Andrew Gallagher, Xin Jin, Jiawei Han, and Thomas S. Huang, “A
Worldwide Tourism Recommendation System Based on Geotagged
Web Photos", Proc. 2010 Int. Conf. on Acoustics, Speech, and
Signal Processing (ICASSP'10), Dallas, TX, March
2010.
13. 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.
14. 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),
15. Zhenhui Li, Jae-Gil Lee, Xiaolei
Li, and Jiawei Han, “Incremental
Clustering for Trajectories", Proc. 2010 Int. Conf. on
Database Systems for Advanced Applications (DASFAA'10),
16. Lu Liu, Feida Zhu, Chen Chen, Xifeng Yan, Jiawei Han, Philip
Yu, and Shiqiang Yang, \Mining Diversity on
Networks", Proc. 2010 Int. Conf. on Database Systems for
Advanced Applications (DASFAA'10), Tsukuba, Japan, April 2010.
17. Dustin Bortner and Jiawei Han, “Progressive
Clustering of Networks Using Structure-Connected Order of Traversal", Proc.
2010 Int. Conf. on Data Engineering (ICDE'10),
18. 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.
19. Xifeng Yan, Bin He, Feida Zhu, Jiawei Han, “Top-K Aggregation Queries Over
Large Networks", Proc. 2010 Int. Conf. on Data
Engineering (ICDE'10),
20. Yizhou Sun, Jiawei Han, Jing Gao, and Yintao
Yu, \iTopicModel: Information
Network-Integrated Topic Modeling", Proc.
2009 Int. Conf. on Data Mining (ICDM'09),
21. Xiao Yu,
Lu An Tang, and Jiawei Han,
\Filtering and Re_nement: A Two-Stage Approach
for E_cient and E_ective
Anomaly Detection", Proc. 2009 Int. Conf. on Data
Mining (ICDM'09),
22. Samson Hauguel, ChengXiang Zhai, and Jiawei Han, “Parallel
PathFinder Algorithms for Mining Structures from
Graphs", Proc. 2009 Int. Conf. on Data Mining (ICDM'09),
23. Jing Gao, Feng
Liang, Wei Fan, Yizhou Sun, and Jiawei
Han, “Bipartite Graph-based Consensus Maximization among
Supervised and Unsupervised Models", Proc.
NIPS 2009 Neural Info. Processing Systems Conf. (NIPS'09),
24. 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),
25. Chandrasekar Ramachandran, Rahul Malik, Xin
Jin, Jing Gao, Klara Nahrstedt, and Jiawei Han,
“VideoMule: A Consensus Learning Approach to Multi-Label Classi_cation
from Noisy User-Generated Videos", Proc.
2009 ACM Int. Conf. on Multimedia (ACM-MM'09), Beijing,
China, Oct. 2009.
26. Tianyi Wu and Jiawei Han,
“Subspace Discovery for Promotion: A Cell Clustering Approach", Proc.
12th Int. Conf. on Discovery Science (DS'09),
27. Min-Soo Kim and Jiawei Han, “CHRONICLE:
A Two-Stage Density-based Clustering Algorithm for Dynamic Networks", Proc.
12th Int. Conf. on Discovery Science (DS'09),
28. 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.
29. 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),
30.
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),
31.
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),
System
demonstrations and invited keynote speech
1. Zhenhui Li, Ming Ji, Jae-Gil Lee,
LuAn Tang, Jiawei Han,
Roland Kays, “MoveMine: Mining Moving
Object Databases", (system demo), Proc.
2010 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'10),
Indianapolis, Indiana, June 2010
2. Xin Jin, Jiebo Luo, Jie Yu, Gang Wang, Dhiraj Joshi, and Jiawei Han,
“iRIN: Image Retrieval in Image Rich Information Networks", Proc.
2010 Int. World Wide Web Conf. (WWW'10), Raleigh, NC, April 2010. (demo
paper)
3.
Jiawei Han,
“Mining Heterogeneous Information Networks by Exploring the Power of
Links", Proc. 12th Int. Conf. on Discovery Science (DS'09),
4. 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),
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
§
Development of efficient and scalable
mechanisms for OLAP information networks: see ICDM’08, EDBT’09,
SDM’09, KDD’09 and VLDB’09 papers.
§
Development of multi-dimensional text
information-based network analysis methods: see ICDM’08 (text cube),
SDM’09 (topic cube), VLDB’09 (iNextCube)
demo, and ICDM’09 (iTopicModel)
§
Development of efficient methods for data
intensive knowledge discovery and data mining: SDM’09, KDD’09, VLDB’09,
WWW’10.
Area Background
This project is based on the previous research on information network
analysis, data mining, text data analysis, data cube, and
multidimensional analysis. There have been many research
papers published on these themes. Several textbooks on data
mining, information network analysis,
and information retrieval provide good overviews of the principles and
algorithms, including (Han and Kamber, 2006),
(Hastie, Tibshirani, and Friedman, 2009) and
(Manning, Raghavan and Schutze
2008).
Area References
1.
Chen Chen, Xifeng Yan, Feida Zhu, Jiawei Han, and Philip S. Yu, "Graph OLAP:
Towards Online Analytical Processing on Graphs", Proc. 2008 Int.
Conf. on Data Mining (ICDM'08),
2.
J. Han and
M. Kamber. Data Mining: Concepts and Techniques, 2nd ed., Morgan
Kaufmann, 2006.
3. T. Hastie, R. Tibshirani,
and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, Springer-Verlag 2001.
4.
Cindy
Xide Lin, Bolin Ding, Jiawei
Han, Feida Zhu, and Bo Zhao, "Text Cube: Computing IR Measures for
Multidimensional Text Database Analysis", Proc. 2008 Int.
Conf. on Data Mining (ICDM'08),
5. Yizhou Sun, Yintao
Yu, and Jiawei Han, “Ranking-Based
Clustering of Heterogeneous Information Networks with Star Network Schema", Proc.
2009 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'09),
6. Yizhou Sun, Jiawei
Han, Peixiang Zhao, Zhijun
Yin, Hong Cheng, Tianyi Wu, “RankClus:
Integrating Clustering with Ranking for Heterogeneous Information Network
Analysis", Proc. 2009 Int. Conf. on Extending Data Base
Technology (EDBT'09), Saint-Petersburg, Russia,
Mar. 2009.
Potential
Related Projects
This project is related to most
of information network analysis, data mining, and OLAP. In particularly,
it is related to P.I.'s NSF IIS 08-42769 (NSF/SGER:
CS-BibCube: OLAPing and
Mining of Computer Science Literature), and PI’s
ARL project NS-CTA INARC (
Project
Web site URL: http://www.cs.uiuc.edu/homes/hanj/projs/infonet.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