P2P Content Sharing

Serge Abiteboul, INRIA Saclay and University Paris-Sud

Information ubiquity has created a large crowd of users (e.g.scientists), who can use database technology to process and share their data more effectively. We consider the issue of building content sharing communities in peer-to-peer environments. The users should be able to manage and share their data with minimal effort with the system in charge of indexing it (to make it accessible), replicating it (for availability), and reorganizing its physical storage (for better query performance). We will outline research challenges, most notably in data mining, that need to be addressed in order to realize this vision.

Online Social Networks: Modeling and Mining

Ravi Kumar,Yahoo! Research

Online social networks have become major and driving phenomena on theweb. In this talk, we will address key modeling and algorithmic questions related to large online social networks. From the modeling perspective, we raise the question of whether there is a generative model for network evolution. The availability of time-stamped data makes it possible to study this question at an extremely fine granularity. We exhibit a simple, natural model that leads to synthetic networks with properties similar to the online ones. From an algorithmic viewpoint, we focus on data mining challenges posed by the magnitude of data in these networks. In particular, we examine topics related to influence and correlation in user activities and neighborhood connectivity structure of users.

Geographic Theory and Geospatial Knowledge Discovery

Harvey J. Miller,Department of GeographyUniversity of Utah, USA

Knowledge discovery from databases is a very complex process. Consequently, researchers have introduced techniques to incorporate existing knowledge about the domain of interest to facilitate the discovery of new knowledge. For example, background knowledge can be used to guide the discovery process, assess the value of discovered patterns and help interpret these patterns. Geospatial knowledge discovery shares the difficulty of traditional KDD but adds complex objects, spatial measurement frameworks with multifaceted implicit relationships, and intricate spatial transformations over time. Consequently, geospatial knowledge discovery is daunting and can benefit greatly from background geographic knowledge.

There is a rich source of background geographic knowledge to exploit in geospatial knowledge discovery. Although many outside scientists think of geography as only a source of empirical facts, there is a rich body of geographic ideas and theories waiting to be “discovered.” In this lecture, I will discuss great geographic ideas and theories that can be exploited in the geospatial knowledge discovery process. Some of these ideas include spatial dependency and heterogeneity, spatial logic, idealized geographies, transformations among alternative geo-spaces, time-spaces and landscape succession. I will illustrate these concepts and provide examples of how they can be used to facilitate the geospatial knowledge discovery process.