Keynotes


  • Prof. Christian S. Jensen, Aalborg University
  • Dr. Divesh Srivastava, AT&T Labs Research
  • Prof. Neoklis Polyzotis, University of California - Santa Cruz
  • Prof. Fang Binxin, Beijing University of Posts and Telecommunications
Prof. Fang Binxing , Academician of the Chinese Academy of Engineering, Beijing University of Posts and Telecommunications, China

Professor Binxing Fang is academician of Chinese Academy of Engineering, the expert of information network and information security. He once was appointed as the Director and Chief Engineer of National Computer Network and information Security Management Center, the Coordination Office Director of National Computer Network Emergency Response Technical Team/Coordination Center of China, Chairman of the science and Technology Committee of the National Computer Network and Information Security Management Center, Senior Engineer with professor title and PhD Supervisor. His main research area covers network security, information content security, parallel processing, and internet technology and so on. He is the first inventor who proposed the conception to build China National Network and Information Security Infrastructure and designed the practical corresponding system, Therefore he has successively won a first prize and tow second prizes of State Scientific and Technological Progress Award. In the area of information security theory, he gave a unified formalization definition of the information security concept which involves physical security, operation security, data security and content security, At the same time, he is conduction research on computability of the information security attribute.

New Progress in Online Social Network Analysis
Internet is gradually evolved into ubiquitous computing platform and information dissemination platform. The emergence and rapid growth of social networking applications, such as online social networking sites, microblog, blog, forums and wikis, makes the way humans use the Internet a profound change – from simple information search and web browsing to construction and maintenance of online social relations and information creations, exchange and sharing based on social relations. The 973 project – “Basic Research on Social Network analysis and Network Information Dissemination” proposes three key scientific issues including “Structural properties and evolving mechanism”, “Crowds behavior formation and their interaction laws” and “Information dissemination laws and evolving mechanism” from the social networks three key elements which consist of “Structure and evolvement”, “Crowds and their interaction” and “Information and its dissemination”. This report studies on these topics and focuses on the latest progress of this project research work.




Prof. Christian S. Jensen , Aalborg University

Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was previously with Aarhus University for three years and spent a 1-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research. He is an Editor-in-Chief of The VLDB Journal and will take over as Editor-in-Chief of ACM Transactions on Database Systems in June.

Keyword-Based Spatial Web Querying - Where We Are and Where We Are Going
The web is being accessed increasingly by users for which an accurate geo-location can be determined, and a spatial, or geographical, web is emerging where both users and content are associated with locations that are used in a wide range of location-based services. In particular, studies suggest that each week, several billions of web queries are issued that have some form of local intent and that target so-called spatial web objects, i.e., point-of-interest data with locations and textual descriptions on the web.
This state of affairs gives prominence to spatial web data management, and it opens to a research area full of new and exciting opportunities and challenges. A prototypical spatial web query takes a user location and user-supplied keywords as arguments, and it returns web objects that are spatially and textually relevant to these arguments. Due perhaps to the rich semantics of geographical space and its importance to our daily lives, many different kinds of relevant spatial web queries may be envisioned.
Based on recent and ongoing work by the speaker and his colleagues, the talk presents key functionality, concepts, and techniques relating to spatial web querying; it presents functionality that addresses different kinds of local intent; and it outlines directions for the future development of keyword-based spatial web querying.




Dr. Divesh Srivastava, AT&T Labs-Research

Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is an ACM fellow, on the board of trustees of the VLDB Endowment, the managing editor of the Proceedings of the VLDB Endowment (PVLDB) and an associate editor of the ACM Transactions on Database Systems (TODS). His research interests and publications span a variety of topics in data management.

Controversy Detection in Wikipedia
The advent of Web 2.0 gave birth to new applications where content is generated through the collaborative contribution of many different users. This form of content generation is believed to generate data of higher quality since the “wisdom of the crowds” makes its way into the data. However, as it is generally the case in real life, there are many issues for which there is no generally accepted opinion. These issues are characterised as controversial. Knowing these issues when processing the user generated content is of major importance in understanding the quality of the data and the trust that should be given to them. We present a technique that finds these controversial issues by analyzing the edits that have been performed on the data over time. We apply our technique on Wikipedia, the world’s largest known collaboratively generated database and we report our findings. This is joint work with Siarhei Bykau, Flip Korn and Yannis Velegrakis.




Prof. Neoklis Polyzotis, University of California Santa Cruz

Neoklis Polyzotis is a researcher in the Structured Data Group of Google Research in Mountain View, California, and a professor at the University of California Santa Cruz. He received his PhD from the Univ. of Wisconsin at Madison in 2003. He is the recipient of an NSF CAREER award, two best paper awards (in VLDB'07 and PODS'08), and several faculty research awards from Google and IBM. His recent research revolves around techniques to scale machine learning to big data, crowdsourcing, and automatic tuning for big data systems.

Scaling Machine Learning to Big Data.
Statistical Machine Learning has undergone a phase transition from a pure academic endeavor to being one of the main drivers of modern commerce and science. Even more so, recent results such as those on tera-scale learning and on very large neural networks suggest that scale is an important ingredient in quality modeling. This talk introduces current applications, techniques and systems on large-scale machine learning, emphasizing the potential of cross-fertilizing research between several communities. In particular, the talk will make the case for two sets of open research questions: Better systems support for the already established use cases of Machine Learning and support for recent advances in Machine Learning research.