Databases Theory and Applications

Graphs have always been important data types for database researchers. With the recent growth of social networks, Wikipedia, Linked Data, RDF, and other networks, the interest in managing very large graphs have again gained momentum. In this talk I will first present a taxonomy of graph processing systems and then summarize research on querying and analytics over property graphs and management and querying of RDF graphs. Short Biography. M. Tamer Özsu is Professor of Computer Science at the David R. Cheriton School of Computer Science, and Associate Dean (Research) of the Faculty of Mathematics at the University of Waterloo. His research is in data management focusing on large-scale data distribution and management of non-traditional data. He is a Fellow of the Association for Computing Machinery (ACM), and of the Institute of Electrical and Electronics Engineers (IEEE), an elected member of the Science Academy of Turkey, and member of Sigma Xi and American Association for the Advancement of Science (AAAS). He currently holds a Cheriton Faculty Fellowship at the University of Waterloo. Knowledge Graphs: From a Fistful of Triples to Deep Data and Deep Text

[1]  Aditya G. Parameswaran,et al.  Evaluating the crowd with confidence , 2013, KDD.

[2]  Ansaf Salleb-Aouissi,et al.  Improving Crowd Labeling through Expert Evaluation , 2012, AAAI Spring Symposium: Wisdom of the Crowd.

[3]  Rui Wang,et al.  Towards social user profiling: unified and discriminative influence model for inferring home locations , 2012, KDD.

[4]  Gang Luo Adaptive Join Plan Generation in Hadoop For CPS 296 . 1 Course Project , 2010 .

[5]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[6]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[7]  Xiaowei Wang,et al.  Distributed Human Computation Framework for Linked Data Co-reference Resolution , 2011, ESWC.

[8]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[9]  Juan-Zi Li,et al.  Understanding retweeting behaviors in social networks , 2010, CIKM.

[10]  Matthew Lease,et al.  On Quality Control and Machine Learning in Crowdsourcing , 2011, Human Computation.

[11]  Kai Zheng,et al.  A crowd-based route recommendation system-CrowdPlanner , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[12]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[13]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[14]  Alice Pigul Generalized Parallel Join Algorithms and Designing Cost Models , 2012, SYRCoDIS.

[15]  Purnamrita Sarkar,et al.  Active Learning for Crowd-Sourced Databases , 2012, ArXiv.

[16]  Tim Kraska,et al.  CrowdDB: Query Processing with the VLDB Crowd , 2011, Proc. VLDB Endow..

[17]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Elizabeth Chang,et al.  In-house Crowdsourcing-Based Entity Resolution: Dealing with Common Names , 2014, 2014 IEEE 11th International Conference on e-Business Engineering.

[19]  Duncan J. Watts,et al.  Financial incentives and the "performance of crowds" , 2009, HCOMP '09.

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[22]  Miles Osborne,et al.  RT to Win! Predicting Message Propagation in Twitter , 2011, ICWSM.

[23]  Thomas Gottron,et al.  Bad news travel fast: a content-based analysis of interestingness on Twitter , 2011, WebSci '11.

[24]  Gianluca Demartini,et al.  ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking , 2012, WWW.

[25]  Gerhard Weikum,et al.  Crowdsourced Entity Markup , 2013, CrowdSem.

[26]  Hector Garcia-Molina,et al.  Quality control for comparison microtasks , 2012, CrowdKDD '12.

[27]  Ralf Herbrich,et al.  Predicting Information Spreading in Twitter , 2010 .

[28]  Abraham Bernstein,et al.  Cognition-based Task Routing: Towards Highly-Effective Task-Assignments in Crowdsourcing Settings , 2014, ICIS.