NSF III: Small: Multi-Dimensional
Structuring, Summarizing and Mining of Social Media Data
National Science Foundation Award Number: NSF
IIS 16-18481 (08-01-2016—07-31-2019)
E-mail: hanj at cs.uiuc.edu
URL: http://www.cs.uiuc.edu/~hanj
List of Supported Students and Staff
·
Meng Jiang,
Postdoc Research Fellow, Department of Computer Science, University of Illinois
at Urbana-Champaign (Finished in Aug. 2017)
·
Quan Yuan,
Postdoc Research Fellow, Department of Computer Science, University of Illinois
at Urbana-Champaign (collaborative) (Finished in Sept. 2017)
·
Ahmed Elkishky, Ph.D.
student, Department of Computer Science, University of Illinois at
Urbana-Champaign (collaborative)
·
Xiang Ren, Ph.D. student, Department of Computer
Science, University of Illinois at Urbana-Champaign (collaborative) (graduated
Dec. 2017)
·
Jiaming Shen,
Ph.D. student, Department of Computer Science, University of Illinois at
Urbana-Champaign
·
Chao Zhang, Ph.D. student, Department of Computer
Science, University of Illinois at Urbana-Champaign (graduated Dec. 2018)
·
Honglei Zhuang,
Ph.D. student, Department of Computer Science, University of Illinois at
Urbana-Champaign (graduated May 2019)
·
Xiaotao Gu, Ph.D. student, Department of Computer Science,
University of Illinois at Urbana-Champaign
·
Shi Zhi, Ph.D. student,
Department of Computer Science, University of Illinois at Urbana-Champaign
·
Award Number: NSF IIS 16-18481
·
Duration: 08/01/2016—07/31/2019
·
Title: NSF III: Small: Multi-Dimensional Structuring,
Summarizing and Mining of Social Media Data
·
Keywords: Big data; data mining;
social media analysis; data integration; text mining; text summarization and
OLAP; information trustworthiness analysis; information network analysis; efficiency and
scalability; applications
Project Summary
·
Various
kinds of social media have impacted billions of users on their ways of
obtaining and sharing information across the globe. This creates great opportunities but also
poses tremendous challenges on understanding, summarizing, and mining of such
data due to its huge volume as well as dynamic and unstructured nature of its
text contents. In response to such
challenges, this project focuses on text-based social media, proposes a
multi-dimensional data structuring approach, which mines unstructured social
media data to uncover its hidden multi-dimensional structures. The project investigates principle,
methodologies and algorithms for social media structuring, summarizing and
mining, and develops effective and scalable technology for multi-dimensional
social media data analysis. The
principles and methodologies developed in this study can be extended to
scalable and multi-dimensional analysis of other kinds of massive unstructured
data as well.
·
To
conduct effective multi-dimensional social media structuring, this project
develops a distant supervision-based methodology with minimal effort of human
curation and labeling. It takes data in
Wikipedia, Freebase, or other knowledge-bases as references, integrates social
media data with the corresponding news or other relevant documents, conducts
phrase mining, entity and event discovery and typing, and uncover critical
aspects, attributes, and values associated with such entities and events from
social media. By organizing social media
data in a structured way, massive social media can be summarizing effectively
in a context-aware semantic OLAP (online analytical processing) framework and
can be analyzed systematically under a general multi-dimensional social media
querying and mining framework for many tasks, such as modeling behavioral
patterns and uncovering bursty events and detecting
social frauds or anomalies.
Intellectual Merit:
·
We
propose a multi-dimensional
data structuring approach,
which mines unstructured social media data to uncover its hidden multi-dimensional structures. Multi-dimensional
structuring will involve integrating social media data
with news, wikipedia, Freebase, and other knowledge-base data, conducting phrase mining, entity/event discovery and typing, and uncovering
aspects associated with such entities and events. Organizing massive social media data in
a conceptually structured way will facilitate understand and summarize social media information effectively,
support context-aware semantic OLAP, facilitate multi-dimensional mining of
social media data, such as finding bursty events and
detecting anomalies in social media.
·
To
systematically develop this approach, we organize the proposal into three themes: (1) multidimensional structuring of social
media data, (2) context-aware summarization in
multi-dimensional space, and (3) a general framework for multidimensional
social media mining.
We will
systematically develop principle, methodologies and algorithms along the three
lines of the proposed research and generate
effective and scalable technology for multi-dimensional social media data structuring, summarization and mining.
·
Built
on our existing work, this project has the following intellectual merit. (1) Developing new principles,
methods, and technologies for structuring, summarizing, and mining of massive,
time-evolving social media data: New
technologies will be developed for
entity
extraction/typing, aspect discovery, context-aware semantic OLAP, and
multidimensional event discovery and anomaly mining, and
thus advance the state-of-the-art;
(2) Enriching the principles and technologies of data mining: Structuring and mining massive, dynamic and unstructured data, such as social media
data, is a major challenge in data mining.
Broader Impacts:
·
With
tremendous amounts of social media data being generated in all aspects of our
society, this project will have the following broad impacts: (1) Benefits our social-media permeated
society: Social media
penetrates every aspect of our life. The project, enhancing our analysis power
on social media, will benefit our society in many ways; (2) Benefits data mining and information
technology: New technologies
and tools will be generated for mining massive unstructured data and will be
transferred to ARL, etc., as we did before; (3) Benefits education and training: The project will train a good number
of researchers, especially female
and minority students,
educating a great number of undergraduates and graduates via our research
publications, tutorials, massive online courses, workshops, and demo-systems.
·
This
project focuses on text-based social media, not on in-depth analysis of image,
audio, and video data. Also, we will use publicly accessible social media data
(e.g., publicly released tweets) with no
links to users' personal information.
·
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/social_media.htm).
Selected Publications and Products:
Books (authored)
·
Chao Zhang and Jiawei Han, Multidimensional
Mining of Massive Text Data, Morgan & Claypool
Publishers, 2019. (Zhang's thesis: 2019 ACM SIGKDD Dissertation
Award Runner-Up)
·
Xiang Ren and Jiawei
Han, Mining
Structures of Factual Knowledge from Text: An Effort-Light Approach, Morgan & Claypool Publishers, 2018. (Ren's thesis: 2018 ACM SIGKDD Dissertation Award)
Journal and Refereed Conference
Publications
·
Yu Shi, Xinwei
He, Naijing Zhang, Carl Yang, and Jiawei Han,
"User-Guided Clustering in Heterogeneous Information Networks via
Motif-Based Comprehensive Transcription", in Proc. 2019 European Conf. on
Machine Learning and Principles and Practice of Knowledge Discovery in
Databases (ECMLPKDD'19), Wurzburg, Germany, Sept. 2019
·
Carl Yang, Huy
Hoang Do, Tomas Mikolov and Jiawei Han “Place
Deduplication with Embeddings", in Proc. the Web
Conf. 2019 (WWW'19), San Franscisco, CA, May 2019
·
Honglei Zhuang, Timothy Hanratty,
and Jiawei Han, “Aspect-Based Sentiment Analysis with Minimal Guidance",
in Proc. 2019 SIAM Int. Conf. on Data Mining (SDM'19), Calgary, Alberta, Canada,
May 2019
·
Sha Li, Chao Zhang, Dongming Lei, Ji Li, Jiawei Han, “GeoAttn:
Fine-Grained Localization of Social Media Messages via Attentional Memory
Network", in Proc. 2019 SIAM Int. Conf. on Data Mining (SDM'19), Calgary,
Alberta, Canada, May 2019
·
Jiaming Shen, Ruiliang Lyu, Xiang Ren, Michelle Vanni,
Brian Sadler, Jiawei Han, “Mining Entity Synonyms with Efficient Neural Set
Generation", in Proc. 2019 AAAI Conf. on Artificial Intelligence
(AAAI-19), Honolulu, Hawaii, Jan. 2019
·
Yu Meng,
Jiaming Shen, Chao Zhang and Jiawei Han,
“Weakly-Supervised Hierarchical Text Classification", in Proc. 2019 AAAI
Conf. on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, Jan. 2019
·
Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R. Voss, Jiawei Han,
“Automated Phrase Mining from Massive Text Corpora", IEEE Transactions on
Knowledge and Data Engineering, 30(10):1825-1837, 2018
·
Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, and Jiawei Han, “Unsupervised
Meta-path Selection for Text Similarity Measure based on Heterogeneous
Information Networks", Data Mining and Knowledge Discovery (DMKD), 32(6):
1735-1767 (2018)
·
Julie Yixuan
Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor O. K. Li, Jiawei Han, Yu Zheng, “pg-Causality: Identifying Spatiotemporal Causal Pathways
for Air Pollutants with Urban Big Data", IEEE Transactions on Big Data
(TBD), 4(4): 571-585 (2018)
·
Chao Zhang, Dongming
Lei, Quan Yuan, Honglei
Zhuang, Lance Kaplan, Shaowen Wang, Jiawei Han, “GeoBurst+: Effective and Real-Time Local Event Detection in
Geo-Tagged Tweet Streams", ACM Transactions on Intelligent Systems and
Technology (ACM TIST), 9(3): 34:1-34:24 (2018)
·
Jingbo Shang, Meng Jiang, Wenzhu Tong, Jinfeng Xiao, Jian
Peng, Jiawei Han, “DPPred: An Effective Prediction
Framework with Concise Discriminative Patterns", IEEE Transactions on
Knowledge and Data Engineering, 30(7): 1226-1239 (2018)
·
Xuan Wang, Yu Zhang, Qi Li, Cathy
Wu, and Jiawei Han, “PENNER: Pattern-enhanced Nested Named Entity Recognition
in Biomedical Literature", in Proc. 2018 Int. Conf. on Bioinformatics and
Biomedicine (BIBM'18), Madrid, Spain, Dec. 2018, pp. 540-547
·
Qi Li, Xuan Wang, Yu Zhang, Fei Ling, Cathy Wu, and Jiawei Han, “Pattern Discovery for
Wide-Window Open Information Extraction in Biomedical Literature", in
Proc. 2018 Int. Conf. on Bioinformatics and Biomedicine (BIBM'18), Madrid,
Spain, Dec. 2018, pp. 420-427
·
Shi Zhi,
Fan Yang, Zheyi Zhu, Qi Li, Zhaoran
Wang, and Jiawei Han, “Dynamic Truth Discovery on Numerical Data", in
Proc. of 2018 IEEE Int. Conf. on Data Mining (ICDM'18), Singapore, Nov. 2018,
pp. 817-826
·
Carl Yang, Yichen
Feng, Pan Li, Yu Shi, and Jiawei Han, “Meta-Graph Based HIN Spectral Embedding:
Methods, Analyses, and Insights", in Proc. of 2018 IEEE Int. Conf. on Data
Mining (ICDM'18), Singapore, Nov. 2018, pp. 657-666
·
Fangbo Tao, Chao Zhang, Xiusi
Chen, Meng Jiang, Tim Hanratty,
Lance Kaplan, and Jiawei Han, “Doc2Cube: Automated Document Allocation to Text
Cube via Dimension-Aware Joint Embedding", in Proc. of 2018 IEEE Int.
Conf. on Data Mining (ICDM'18), Singapore, Nov. 2018, pp. 1260-1265
·
Doris Xin, Ahmed El-Kishky, De Liao, Brandon Norick,
and Jiawei Han, “Active Learning on Heterogeneous Information Networks: A
Multi-armed Bandit Approach", in Proc. of 2018 IEEE Int. Conf. on Data
Mining (ICDM'18), Singapore, Nov. 2018, pp. 1350-1355
·
Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren and Jiawei Han, “Learning Named Entity Tagger
using Domain-Specific Dictionary", in Proc. of 2018 Conf. on Empirical
Methods in Natural Language Processing (EMNLP'18), Brussels, Belgium, Oct.
2018, pp. 2054-2064
·
Liyuan Liu, Xiang Ren, Jingbo
Shang, Xiaotao Gu, Jian
Peng and Jiawei Han, “Efficient Contextualized Representation: Language Model
Pruning for Sequence Labeling", in Proc. of 2018 Conf. on Empirical
Methods in Natural Language Processing (EMNLP'18), Brussels, Belgium, Oct.
2018, pp. 1215-1225
·
Quan Yuan, Xiang Ren, Wenqi He,
Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin
and Jiawei Han, “Open-Schema Event Profiling for Massive News Corpora", in
Proc. of 2018 ACM Int. Conf. on Information and Knowledge Management (CIKM'18),
Turin, Italy, Oct. 2018, pp. 587-596
·
Yu Meng,
Jiaming Shen, Chao Zhang and Jiawei Han,
“Weakly-Supervised Neural Text Classification", in Proc. of 2018 ACM Int.
Conf. on Information and Knowledge Management (CIKM'18), Turin, Italy, Oct.
2018, pp. 983-992
·
Jingbo Shang, Jiaming Shen, Tianhang Sun, Xingbang Liu, Anja Gruenheid, Flip Korn, Adam Lelkes, Cong Yu and Jiawei Han, “Investigating Rumor News
Using Agreement-Aware Search", in Proc. of 2018 ACM Int. Conf. on
Information and Knowledge Management (CIKM'18), Turin, Italy, Oct. 2018, pp.
2117-2125
·
Carl Yang, Mengxiong
Liu, Frank He, Xikun Zhang, Jian Peng, and Jiawei
Han, “Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery",
in Proc. of 2018 European Conf. on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (ECMLPKDD'18), Dublin, Ireland, Sept. 2018,
pp. 37-54
·
Jingbo Shang, Qi Zhu, Jiaming
Shen, Xuan Wang, Xiaotao Gu,
Lance Kaplan, Timothy Harratty and Jiawei Han, "AutoNet: Automated Network Construction and Exploration
System from Domain-Specific Corpora", in Proc. of 2018 ACM SIGKDD Int.
Conf. on Knowledge Discovery and Data Mining (KDD'18), (demo paper) London, UK,
August 2018
·
Jiaming Shen, Jinfeng
Xiao, Yu Zhang, Carl Yang, Jingbo Shang, Jinda Han, Saurabh Sinha, Peipei
Ping, Richard Weinshilboum, Zhiyong
Lu and Jiawei Han, "SetSearch+: Entity-Set-Aware
Search and Mining for Scientific Literature", in Proc. of 2018 ACM SIGKDD
Int. Conf. on Knowledge Discovery and Data Mining (KDD'18), (demo paper),
London, UK, August 2018
·
Hanwen Zha, Jiaming Shen, Keqian Li, Warren Greiff, Michelle Vanni, Jiawei
Han and Xifeng Yan, "FTS: Faceted Taxonomy
Construction and Search for Scientific Publications", in Proc. of 2018 ACM
SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'18), (demo
paper), London, UK, August 2018
·
Carl Yang, Xiaolin Shi, Jie Luo and Jiawei
Han, "I Know You’ll Be Back: Interpretable New User Clustering and Churn
Prediction on a Mobile Social Application", in Proc. of 2018 ACM SIGKDD
Int. Conf. on Knowledge Discovery and Data Mining (KDD'18), London, UK, August
2018
·
Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian
Sadler, Michelle Vanni and Jiawei Han, "TaxoGen: Constructing Topical Concept Taxonomy by Adaptive
Term Embedding and Clustering", in Proc. of 2018 ACM SIGKDD Int. Conf. on
Knowledge Discovery and Data Mining (KDD'18), London, UK, August 2018
·
Qi Li, Meng Jiang, Xikun Zhang, Meng Qu, Timothy Hanratty, Jing
Gao and Jiawei Han, "TruePIE: Discovering
Reliable Patterns in Pattern-Based Information Extraction", in Proc. of
2018 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'18),
London, UK, August 2018
·
Jiaming Shen, Zeqiu
Wu, Dongming Lei, Chao Zhang, Xiang Ren, Michelle T. Vanni, Brian M. Sadler and Jiawei Han, "HiExpan: Task-Guided Taxonomy Construction by Hierarchical
Tree Expansion", in Proc. of 2018 ACM SIGKDD Int. Conf. on Knowledge
Discovery and Data Mining (KDD'18), London, UK, August 2018
·
Yu Shi, Qi Zhu,
Fang Guo, Chao Zhang and Jiawei Han, "Easing
Embedding Learning by Comprehensive Transcription of Heterogeneous Information
Networks", in Proc. of 2018 ACM SIGKDD Int. Conf. on Knowledge Discovery
and Data Mining (KDD'18), London, UK, August 2018
·
Yuning Mao, Xiang Ren, Jiaming
Shen, Xiaotao Gu and Jiawei
Han, "End-to-End Reinforcement Learning for Automatic Taxonomy
Induction", in Proc. of 2018 Annual Meeting of the Association for
Computational Linguistics (ACL'18), Melbourne, Australia, July 2018
·
Jiaming Shen, Jinfeng
Xiao, Xinwei He, Jingbo
Shang, Saurabh Sinha and Jiawei Han, "Entity Set Search of Scientific
Literature: An Unsupervised Ranking Approach", in Proc. of 2018 Int. ACM
SIGIR Conf. on Research and Development in Information Retrieval (SIGIR'18),
Ann Arbor, MI, July 2018
·
Yu Shi, Huan Gui, Qi Zhu, Lance Kaplan,Jiawei Han, “AspEm:
Embedding Learning by Aspects in Heterogeneous Information Networks,” Proc. of
2018 SIAM Int. Conf. on Data Mining (SDM’18), San Diego, CA, May 2018
·
Meng Qu, Xiang Ren, Yu Zhang, and Jiawei
Han, “Weakly-supervised Relation Extraction by Pattern-enhanced Embedding
Learning”, Proc. of 2018 Int. Conf. on World-Wide Web (WWW’18), Lyon, France,
Apr. 2018
·
Qi Zhu, Xiang
Ren, Jingbo Shang, Yu Zhang, Frank F. Xu and Jiawei
Han, "Open Information Extraction with Global Structure Constraints”,
(poster paper), Proc. of 2018 Int. Conf. on World-Wide Web (WWW’18), Lyon,
France, Apr. 2018 (received WWW'18 best poster award honorable mentioning)
·
Liyuan Liu, Jingbo
Sahng, Frank Xu, Xiang Ren, Huan
Gui, Jian Peng and Jiawei Han, "Empower Sequence
Labeling with Task-Aware Neural Language Model", in Proc. of 2018 AAAI
Conf. on Artificial Intelligence (AAAI'18), New Orleans, LA, Feb. 2018
·
Chao Zhang, Mengxiong Liu, Zhengchao Liu,
Carl Yang, Luming Zhang, Jiawei Han,
"Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized
Cross-Modal Embedding Approach", in Proc. of 2018 AAAI Conf. on Artificial
Intelligence (AAAI'18), New Orleans, LA, Feb. 2018
·
Wanzheng Zhu, Chao Zhang, Shuochao
Yao, Xiaobin Gao, Jiawei Han, "A Spherical
Hidden Markov Model for Semantics-Rich Human Mobility Modeling", in Proc.
of 2018 AAAI Conf. on Artificial Intelligence (AAAI'18), New Orleans, LA, Feb.
2018
·
Zeqiu Wu, Xiang Ren, Frank F. Xu, Ji Li
and Jiawei Han, "Indirect Supervision for Relation Extraction using
Question-Answer Pairs", in Proc. of 2018 ACM Int. Conf. on Web Search and
Data Mining (WSDM'18), Los Angeles, CA, Feb. 2018
·
Meng Qu, Jian Tang, and Jiawei Han,
"Curriculum Learning for Heterogeneous Star Network Embedding via Deep
Reinforcement Learning", in Proc. of 2018 ACM Int. Conf. on Web Search and
Data Mining (WSDM'18), Los Angeles, CA, Feb. 2018
·
Quan Yuan, Jingbo
Shang, Xin Cao, Chao Zhang, Xinhe Geng,
Jiawei Han, "Detecting Multiple Periods and Periodic Patterns in Event
Time Sequences", in Proc. of 2017 ACM Int. Conf. on Information and
Knowledge Management (CIKM'17), Singapore, Nov. 2017
·
Mengxiong Liu, Zhengchao
Liu, Chao Zhang, Keyang Zhang, Quan
Yuan, Tim Hanrantty and Jiawei Han, "Urbanity: A
System for Interactive Exploration of Urban Dynamics from Streaming Human
Sensing Data" (system demo), in Proc. of 2017 ACM Int. Conf. on
Information and Knowledge Management (CIKM'17), Singapore, Nov. 2017
·
Huan Gui, Jialu Liu, Fangbo Tao, Meng Jiang, Brandon Norick, Lance
Kaplan and Jiawei Han, "Embedding Learning with Events in Heterogeneous
Information Networks", IEEE Transactions on Knowledge and Data
Engineering, 29(11): 2428- 2441, 2017
·
Jiaming Shen, Zeqiu
Wu, Dongming Lei, Jingbo
Shang, Xiang Ren, Jiawei Han, "SetExpan:
Corpus-based Set Expansion via Context Feature Selection and Rank
Ensemble", in Proc. of 2017 European Conf. on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'17),
Skopje, Macedonia, Sept. 2017
·
Carl Yang, Lanxiao Bai, Chao Zhang, Quan
Yuan and Jiawei Han, "Bridging Collaborative Filtering and Semi-Supervised
Learning: A Neural Approach for POI recommendation", in Proc. of 2017 ACM
SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'17), Halifax,
Nova Scotia, Canada, Aug. 2017
·
Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty and Jiawei Han, "TrioVecEvent:
Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams",
in Proc. of 2017 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining
(KDD'17), Halifax, Nova Scotia, Canada, Aug. 2017
·
Xiang
Ren, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji,
Jiawei Han, "Label Noise Reduction in Entity Typing by Heterogeneous
Partial-Label Embedding", in Proc. of 2016 ACM SIGKDD Conf. on
Knowledge Discovery and Data Mining (KDD'16), San Francisco, CA, Aug. 2016
·
Meng Jiang, Christos Faloutsos, Jiawei Han, "CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors", in
Proc. of 2016 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining
(KDD'16), San Francisco, CA, Aug. 2016
·
Mengting Wan, Xiangyu Chen,
Lance Kaplan, Jiawei Han, Jing Gao, Bo Zhao, "An Uncertainty-Aware Model
to Summarize Trustworthy Quantitative Information", in Proc. of
2016 ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'16), San
Francisco, CA, Aug. 2016
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, IBM T.J. Watson Research
Center, Yahoo! Labs, Microsoft Research, Google 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
· This project is based on the previous research on data mining, information network analysis, spatiotemporal data analysis, and data cube and multidimensional analysis.
Area
References
· P. Yu, J. Han, and C. Faloutsos, editors. Link Mining: Models, Algorithms, and Applications. Springer, 2010
·
X. L.
Dong, L. Berti-Equille, and D. Srivastava. Truth
discovery and copying detection in a dynamic world. PVLDB, 2(1):562–573, 2009.
Potential Related Projects
·
Any project related to
social media analysis, information fusion, information and social network
analysis, spatiotemporal data mining, and knowledge discovery.
Project Web
site URL:
http://www.cs.uiuc.edu/~hanj/projs/social_media.htm
Online software:
Online software related to this project can be
downloaded at Github or at www.illimine.cs.uiuc.edu