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Important Dates

Abstract submission
March 28, 2012
Paper submission
April 11, 2012 (extended)
April 4, 2012
Notification of acceptance
May 15, 2012
May 7, 2012
Accommodation reservation
May 23, 2012
May 15, 2012

Camera-ready copy

May 23, 2012
May 18, 2012
Early registration
May 23, 2012
May 18, 2012
June 24-27, 2012


Invited Speakers

  • Dr. Krishna Gummadi

  • Max Planck Institute for Software Systems (MPI-SWS),Germany.
  • Extracting Relevant and Trustworthy Information from Microblogs.


Microblogging sites like Twitter have emerged as a popular platform for exchanging real-time information on the Web. Twitter is used by hundreds of millions of users ranging from popular news organizations and celebrities to domain experts in fields like computer science and astrophysics and spammers. As a result, the quality of information posted in Twitter is highly variable and finding the users that are authoritative sources of relevant and trust-worthy information on specific topics (i.e., topical experts) is a key challenge. I will attempt to address this challenge in this two-part talk.

In the first part of the talk, I will focus on understanding and combating link farming activity in Twitter. Users, especially spammers, resort to link farming to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user's direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user's tweets by search engines. I will first discuss results from our recent studies investigating link farming activity in the Twitter network and then propose mechanisms to discourage the activity.

In the second part of the talk, I will focus on the problem of finding topic experts in Twitter. I will propose a new methodology that relies on the wisdom of the Twitter crowds. Specifically, we leverage Twitter Lists, which are often carefully created by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provide valuable semantic cues to experts' domain of expertise. I will first describe how we mined List information to build Cognos, an expert search system for Twitter and then present results from a real-world deployment.


Krishna Gummadi is tenured faculty member and head the networked systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He received his Ph.D. (2005) and M.S. (2002) degrees in Computer Science and Engineering from the University of Washington, Seattle. He also holds a B.Tech (2000) degree in Computer Science and Engineering from the Indian Institute of Technology, Madras. Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on enabling the social Web. Specifically, they include (a) understanding the structure and evolution of social network graphs, (b) understanding how content and information propagates through social networks, (c) leveraging social networks for building better information sharing systems (i.e., better search results and content recommendations as well as filtering unwanted communication and content), and (d) building scalable infrastructures for supporting social networking sites and their workloads. Krishna's work on online social networks, Internet access networks, and peer-to-peer systems has led to a number of widely cited papers. He also received best paper awards at OSDI, SIGCOMM IMW, and MMCN for his work on Internet measurements and peer-to-peer systems.


  • Prof. Timos Sellis
  • Research Center "Athena" and National Technical University of Athens, Greece
  • Personalization in web search and data management (joint work with T. Dalamagas, G. Giannopoulos and A. Arvanitis)


We address issues on web search personalization by exploiting users' search histories to train and combine multiple ranking models for result reranking. These methods aim at grouping users' clickthrough data (queries, results lists, clicked results), based either on content or on specific features that characterize the matching between  queries and results and that capture implicit user search behaviors. After obtaining clusters of similar clickthrough data, we train multiple ranking functions (using  Ranking SVM model), one for each cluster. Finally, when a new query is posed, we combine ranking functions that correspond to clusters similar to the query, in order  to rerank/personalize its results.

We also present how to support personalization in data management systems by providing users with mechanisms for specifying their preferences. In the past, a number of methods have been proposed for ranking tuples according to user-specified preferences. These methods include for example top-k, skyline, top-k dominating queries etc. However, neither of these methods has attempted to push preference evaluation inside the core of a database management system (DBMS). Instead, all ranking algorithms or special indexes are offered on top of a DBMS, hence they are not able to exploit any optimization provided by the query optimizer. In this talk we present a framework for supporting user preference as a   fist-class construct inside a DBMS, by extending relational algebra with preference operators and by appropriately modifying query plans based on these preferences.

Bio Sketch

Prof. Timos Sellis received his diploma degree in Electrical Engineering in 1982 from the National Technical University of Athens (NTUA), Greece. In 1983 he received the M.Sc. degree from Harvard University and in 1986 the Ph.D. degree from the University of California at Berkeley, both in Computer Science. In 1986, he joined the Department of Computer Science of the University of Maryland, College Park as an Assistant Professor, and became an Associate Professor in 1992. Between 1992 and 1996 he was an Associate Professor at NTUA, where he is currently a Full Professor. Prof. Sellis is also the Director of the Institute for the Management of Information Systems (IMIS) of the "Athena" Research Center. His research interests include data streams, peer-to-peer database systems, personalization, the integration of Web and databases, and spatio-temporal database systems. He has published over 190 articles in refereed journals and international conferences in the above areas and has been invited speaker in major international events. Prof. Sellis is a recipient of the prestigious Presidential Young Investigator (PYI) award given by the President of USA to the most talented new researchers (1990), and of the VLDB 1997 10 Year Paper Award for his work on spatial databases. He was the president of the National Council for Research and Technology of Greece (2001-2003) and a member of the VLDB Endowment (1996-2000). In November 2009, he was awarded the status of IEEE Fellow, for his contributions to database query optimization, and spatial data management.  He has also participated and co-ordinated several national and european research projects.