TY  - JOUR
AU  - Chen, Yu-Che
AU  - Lee, Jooho
T1  - Collaborative data networks for public service: governance, management, and performance.
JO  - Public Management Review
PY  - 2018/5
VL  - 20
IS  - 5
SP  - 
EP  - 
UR  - 
M3  - 
KW  - 
L1  - 
SN  - 
N1  - 
N1  - 
AB  - This study aims to advance the theory and practice of managing collaborative data networks for information and decision-support services that exist in over 400 US metropolitan areas. Integrating insights from collaborative governance, network management, and cross-boundary information sharing, this study develops a framework to outline the interplay between context, management, collaborative dynamics, technology, and performance. This study further utilizes the framework to conduct an exploratory in-depth case study of a metropolitan transportation data network to examine such interplay. The findings suggest ways to improve the performance of collaborative data networks and their implications are discussed. [ABSTRACT FROM AUTHOR]
ER  -

TY  - CONF
AU  - Mitchell, T.
AU  - Cohen, W.
AU  - Hruscha, E.
AU  - Talukdar, P.
AU  - Betteridge, J.
AU  - Carlson, A.
AU  - Dalvi, B.
AU  - Gardner, M.
AU  - Kisiel, B.
AU  - Krishnamurthy, J.
AU  - Lao, N.
AU  - Mazaitis, K.
AU  - Mohammad, T.
AU  - Nakashole, N.
AU  - Platanios, E.
AU  - Ritter, A.
AU  - Samadi, M.
AU  - Settles, B.
AU  - Wang, R.
AU  - Wijaya, D.
AU  - Gupta, A.
AU  - Chen, X.
AU  - Saparov, A.
AU  - Greaves, M.
AU  - Welling, J.
A2  - 
T1  - Never-Ending Learning
T2  - AAAI
PB  - 
CY  - 
PY  - 2015/
M2  - 
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://www.cs.cmu.edu/~wcohen/pubs.html
M3  - 
KW  - nell
KW  - semantic
KW  - learning
KW  - ontology
KW  - toread
L1  - 
SN  - 
N1  - Papers by William W. Cohen
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Alsubaiee, Sattam
AU  - Altowim, Yasser
AU  - Altwaijry, Hotham
AU  - Behm, Alexander
AU  - Borkar, Vinayak
AU  - Bu, Yingyi
AU  - Carey, Michael
AU  - Grover, Raman
AU  - Heilbron, Zachary
AU  - Kim, Young-Seok
AU  - Li, Chen
AU  - Onose, Nicola
AU  - Pirzadeh, Pouria
AU  - Vernica, Rares
AU  - Wen, Jian
T1  - ASTERIX: an open source system for "Big Data" management and analysis (demo)
JO  - Proceedings of the VLDB Endowment
PY  - 2012/08
VL  - 5
IS  - 12
SP  - 1898
EP  - 1901
UR  - http://dl.acm.org/citation.cfm?id=2367502.2367532
M3  - 
KW  - big
KW  - asterix
KW  - analysis
KW  - data
L1  - 
SN  - 
N1  - 
N1  - 
AB  - At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.
ER  -

TY  - JOUR
AU  - Behm, Alexander
AU  - Borkar, Vinayak
AU  - Carey, Michael
AU  - Grover, Raman
AU  - Li, Chen
AU  - Onose, Nicola
AU  - Vernica, Rares
AU  - Deutsch, Alin
AU  - Papakonstantinou, Yannis
AU  - Tsotras, Vassilis
T1  - ASTERIX: towards a scalable, semistructured data platform for evolving-world models
JO  - Distributed and Parallel Databases
PY  - 2011/
VL  - 29
IS  - 3
SP  - 185
EP  - 216
UR  - http://dx.doi.org/10.1007/s10619-011-7082-y
M3  - 10.1007/s10619-011-7082-y
KW  - database
KW  - time
KW  - asterix
KW  - data
L1  - 
SN  - 
N1  - 
N1  - 
AB  - ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal—the storage and analysis of data pertaining to evolving-world models . We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead.
ER  -

TY  - JOUR
AU  - Chen, P.
AU  - Redner, S.
T1  - Community structure of the physical review citation network
JO  - Journal of Informetrics
PY  - 2010/
VL  - 4
IS  - 3
SP  - 278 
EP  -  290
UR  - http://www.sciencedirect.com/science/article/pii/S1751157710000027
M3  - 10.1016/j.joi.2010.01.001
KW  - 
L1  - 
SN  - 
N1  - ScienceDirect.com - Journal of Informetrics - Community structure of the physical review citation network
N1  - 
AB  - We investigate the community structure of physics subfields in the citation network of all Physical Review publications between 1893 and August 2007. We focus on well-cited publications (those receiving more than 100 citations), and apply modularity maximization to uncover major communities that correspond to clearly identifiable subfields of physics. While most of the links between communities connect those with obvious intellectual overlap, there sometimes exist unexpected connections between disparate fields due to the development of a widely applicable theoretical technique or by cross fertilization between theory and experiment. We also examine communities decade by decade and also uncover a small number of significant links between communities that are widely separated in time.
ER  -

TY  - CONF
AU  - Wang, Yonggang
AU  - Zhai, Ennan
AU  - Hu, Jianbin
AU  - Chen, Zhong
A2  - 
T1  - Claper: Recommend classical papers to beginners
T2  - Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery
PB  - IEEE
CY  - 
PY  - 2010/08
M2  - 
VL  - 6
IS  - 
SP  - 2777
EP  - 2781
UR  - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227
M3  - 10.1109/FSKD.2010.5569227
KW  - paper
KW  - item
KW  - recommender
KW  - analysis
KW  - citation
KW  - research
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.
ER  -

TY  - CHAP
AU  - Yang, Wei-Ting
AU  - Luo, Zhiqiang
AU  - Chen, I-Ming
AU  - Yeo, SongHuat
A2  - Parenti Castelli, Vincenzo
A2  - Schiehlen, Werner
T1  - A Method for Comparing Human Postures from Motion Capture Data
T2  - ROMANSY 18 Robot Design, Dynamics and Control
PB  - Springer Vienna
CY  - 
PY  - 2010/
VL  - 524
IS  - 
SP  - 441
EP  - 448
UR  - http://dx.doi.org/10.1007/978-3-7091-0277-0_52
M3  - 10.1007/978-3-7091-0277-0_52
KW  - cyberoculus
KW  - mocap
L1  - 
SN  - 978-3-7091-0276-3
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Chen, Li
AU  - Pu, Pearl
T1  - Interaction design guidelines on critiquing-based recommender systems
JO  - User Modeling and User-Adapted Interaction
PY  - 2009/
VL  - 19
IS  - 3
SP  - 167
EP  - 206
UR  - http://dx.doi.org/10.1007/s11257-008-9057-x
M3  - 10.1007/s11257-008-9057-x
KW  - stair
KW  - critiquing
KW  - recommender
KW  - interaction
L1  - 
SN  - 
N1  - 
N1  - 
AB  - A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user’s preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user’s interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: 
ER  -

TY  - JOUR
AU  - Doan, AnHai
AU  - Naughton, Jeffrey F.
AU  - Ramakrishnan, Raghu
AU  - Baid, Akanksha
AU  - Chai, Xiaoyong
AU  - Chen, Fei
AU  - Chen, Ting
AU  - Chu, Eric
AU  - DeRose, Pedro
AU  - Gao, Byron
AU  - Gokhale, Chaitanya
AU  - Huang, Jiansheng
AU  - Shen, Warren
AU  - Vuong, Ba-Quy
T1  - Information extraction challenges in managing unstructured data
JO  - SIGMOD Record
PY  - 2009/03
VL  - 37
IS  - 4
SP  - 14
EP  - 20
UR  - http://doi.acm.org/10.1145/1519103.1519106
M3  - 10.1145/1519103.1519106
KW  - information
KW  - ie
KW  - extraction
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Over the past few years, we have been trying to build an end-to-end system at Wisconsin to manage unstructured data, using extraction, integration, and user interaction. This paper describes the key information extraction (IE) challenges that we have run into, and sketches our solutions. We discuss in particular developing a declarative IE language, optimizing for this language, generating IE provenance, incorporating user feedback into the IE process, developing a novel wiki-based user interface for feedback, best-effort IE, pushing IE into RDBMSs, and more. Our work suggests that IE in managing unstructured data can open up many interesting research challenges, and that these challenges can greatly benefit from the wealth of work on managing structured data that has been carried out by the database community.
ER  -

TY  - CONF
AU  - Dong, Xishuang
AU  - Chen, Xiaodong
AU  - Guan, Yi
AU  - Yu, Zhiming
AU  - Li, Sheng
A2  - Burgin, Mark
A2  - Chowdhury, Masud H.
A2  - Ham, Chan H.
A2  - Ludwig, Simone A.
A2  - Su, Weilian
A2  - Yenduri, Sumanth
T1  - An Overview of Learning to Rank for Information Retrieval.
T2  - CSIE (3)
PB  - IEEE Computer Society
CY  - 
PY  - 2009/
M2  - 
VL  - 
IS  - 
SP  - 600
EP  - 606
UR  - http://dblp.uni-trier.de/db/conf/csie/csie2009-3.html#DongCGYL09
M3  - 
KW  - information
KW  - learning-to-rank
KW  - rank
KW  - learning
KW  - overview
KW  - retrieval
L1  - 
SN  - 978-0-7695-3507-4
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Guan, Ziyu
AU  - Bu, Jiajun
AU  - Mei, Qiaozhu
AU  - Chen, Chun
AU  - Wang, Can
A2  - Allan, James
A2  - Aslam, Javed A.
A2  - Sanderson, Mark
A2  - Zhai, ChengXiang
A2  - Zobel, Justin
T1  - Personalized tag recommendation using graph-based ranking on multi-type interrelated objects.
T2  - SIGIR
PB  - ACM
CY  - 
PY  - 2009/
M2  - 
VL  - 
IS  - 
SP  - 540
EP  - 547
UR  - http://www-personal.umich.edu/~qmei/pub/sigir09-tag.pdf
M3  - 
KW  - tag
KW  - recommendation
KW  - toread
L1  - 
SN  - 978-1-60558-483-6
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Lu, Caimei
AU  - Chen, Xin
AU  - Park, E. K.
A2  - 
T1  - Exploit the tripartite network of social tagging for web clustering
T2  - Proceeding of the 18th ACM conference on Information and knowledge management
PB  - ACM
CY  - New York, NY, USA
PY  - 2009/
M2  - 
VL  - 
IS  - 
SP  - 1545
EP  - 1548
UR  - http://doi.acm.org/10.1145/1645953.1646167
M3  - 10.1145/1645953.1646167
KW  - clustering
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 978-1-60558-512-3
N1  - 
N1  - 
AB  - In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.
ER  -

TY  - CONF
AU  - Yuen, Man-Ching
AU  - Chen, Ling-Jyh
AU  - King, I.
A2  - 
T1  - A Survey of Human Computation Systems
T2  - Proceedings of the International Conference on Computational Science and Engineering, CSE '09
PB  - 
CY  - 
PY  - 2009/08
M2  - 
VL  - 4
IS  - 
SP  - 723
EP  - 728
UR  - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5283450&tag=1
M3  - 10.1109/CSE.2009.395
KW  - human
KW  - intelligence
KW  - survey
KW  - social
KW  - computation
KW  - cirg
KW  - collective
KW  - toread
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Human computation is a technique that makes use of human abilities for computation to solve problems. The human computation problems are the problems those computers are not good at solving but are trivial for humans. In this paper, we give a survey of various human computation systems which are categorized into initiatory human computation, distributed human computation and social game-based human computation with volunteers, paid engineers and online players. For the existing large number of social games, some previous works defined various types of social games, but the recent developed social games cannot be categorized based on the previous works. In this paper, we define the categories and the characteristics of social games which are suitable for all existing ones. Besides, we present a survey on the performance aspects of human computation system. This paper gives a better understanding on human computation system.
ER  -

TY  - CONF
AU  - Hu, Jian
AU  - Fang, Lujun
AU  - Cao, Yang
AU  - Zeng, Hua-Jun
AU  - Li, Hua
AU  - Yang, Qiang
AU  - Chen, Zheng
A2  - Myaeng, Sung-Hyon
A2  - Oard, Douglas W.
A2  - Sebastiani, Fabrizio
A2  - Chua, Tat-Seng
A2  - Leong, Mun-Kew
T1  - Enhancing text clustering by leveraging Wikipedia semantics.
T2  - SIGIR
PB  - ACM
CY  - 
PY  - 2008/
M2  - 
VL  - 
IS  - 
SP  - 179
EP  - 186
UR  - http://dblp.uni-trier.de/db/conf/sigir/sigir2008.html#HuFCZLYC08
M3  - 
KW  - semantics
KW  - toread
KW  - wikipedia
L1  - 
SN  - 978-1-60558-164-4
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Krestel, Ralf
AU  - Chen, Ling
A2  - 
T1  - The Art of Tagging: Measuring the Quality of Tags
T2  - Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
PB  - Springer-Verlag
CY  - Berlin, Heidelberg
PY  - 2008/
M2  - 
VL  - 
IS  - 
SP  - 257
EP  - 271
UR  - http://dx.doi.org/10.1007/978-3-540-89704-0_18
M3  - 10.1007/978-3-540-89704-0_18
KW  - quality
KW  - tag
L1  - 
SN  - 978-3-540-89703-3
N1  - The Art of Tagging
N1  - 
AB  - <p>Collaborative tagging, supported by many social networking websites, is currently enjoying an increasing popularity. The usefulness of this largely available tag data has been explored in many applications including web resources categorization,deriving emergent semantics, web search etc. However, since tags are supplied by users &amp;lt;em&amp;gt;freely&amp;lt;/em&amp;gt; , not all of them are useful and reliable, especially when they are generated by spammers with malicious intent. Therefore, identifying tags of high quality is crucial in improving the performance of applications based on tags. In this paper, we propose TRP-Rank (Tag-Resource Pair Rank), an algorithm to measure the quality of tags by manually assessing a seed set and &amp;lt;em&amp;gt;propagating the quality&amp;lt;/em&amp;gt; through a graph. The three dimensional relationship among users, tags and web resources is firstly represented by a graph structure. A set of seed nodes, where each node represents a tag annotating a resource, is then selected and their quality is assessed. The quality of the remaining nodes is calculated by propagating the known quality of the seeds through the graph structure. We evaluate our approach on a public data set where tags generated by suspicious spammers were manually labelled. The experimental results demonstrate the effectiveness of this approach in measuring the quality of tags.</p>
ER  -

TY  - JOUR
AU  - Chen, Wei
AU  - Zeng, Qingtian
AU  - Wenyin, Liu
AU  - Hao, Tianyong
T1  - A user reputation model for a user-interactive question answering system
JO  - Concurrency and Computation: Practice and Experience
PY  - 2007/
VL  - 19
IS  - 15
SP  - 2091
EP  - 2103
UR  - http://dx.doi.org/10.1002/cpe.1142
M3  - 10.1002/cpe.1142
KW  - collaborative
KW  - social
KW  - question
KW  - reputation
KW  - answering
KW  - search
KW  - web
L1  - 
SN  - 
N1  - 
N1  - 
AB  - In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley &amp; Sons, Ltd.
ER  -

TY  - CONF
AU  - LIU, Tie-Yan
AU  - YANG, Yiming
AU  - WAN, Hao
AU  - ZHOU, Qian
AU  - GAO, Bin
AU  - ZENG, Hua-Jun
AU  - CHEN, Zheng
AU  - MA, Wei-Ying
A2  - 
T1  - An experimental study on large-scale web categorization
T2  - Special interest tracks and posters of the 14th international conference on World Wide Web
PB  - ACM
CY  - New York, NY, USA
PY  - 2005/
M2  - 
VL  - 
IS  - 
SP  - 1106
EP  - 1107
UR  - http://doi.acm.org/10.1145/1062745.1062891
M3  - 10.1145/1062745.1062891
KW  - categorization
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 1-59593-051-5
N1  - An experimental study on large-scale web categorization
N1  - 
AB  - Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, <i>k</i>-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.
ER  -

TY  - CONF
AU  - Shen, Dou
AU  - Chen, Zheng
AU  - Yang, Qiang
AU  - Zeng, Hua-Jun
AU  - Zhang, Benyu
AU  - Lu, Yuchang
AU  - Ma, Wei-Ying
A2  - 
T1  - Web-page classification through summarization
T2  - Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
PB  - ACM
CY  - New York, NY, USA
PY  - 2004/
M2  - 
VL  - 
IS  - 
SP  - 242
EP  - 249
UR  - http://doi.acm.org/10.1145/1008992.1009035
M3  - 10.1145/1008992.1009035
KW  - classification
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 1-58113-881-4
N1  - Web-page classification through summarization
N1  - 
AB  - Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
ER  -

TY  - CONF
AU  - Dumais, Susan
AU  - Chen, Hao
A2  - 
T1  - Hierarchical classification of Web content
T2  - Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
PB  - ACM
CY  - New York, NY, USA
PY  - 2000/
M2  - 
VL  - 
IS  - 
SP  - 256
EP  - 263
UR  - http://doi.acm.org/10.1145/345508.345593
M3  - 10.1145/345508.345593
KW  - classification
KW  - bachelor:2011:bachmann
KW  - web
L1  - 
SN  - 1-58113-226-3
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Langer, G.
AU  - Langer, E.
AU  - Chen, C.-J.
T1  - Botryobasidium musaisporum sp. nov. collected in Taiwan
JO  - Mycol. Res
PY  - 2000/
VL  - 104 
IS  - 4
SP  - 510
EP  - 512
UR  - 
M3  - 
KW  - musaisporum
KW  - Taiwan
KW  - Botryobasidium
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -