Items where author is affiliated with The Pennsylvania State University

The behavioral description based Web Service Composition (WSC) problem aims at the automatic construction of a coordinator web service that controls a set of web services to reach a goal state. However, solving the WSC problem exactly with a realistic model is doubly-exponential in the number of variables in web service descriptions. In this paper, we propose a novel efficient approximation-based algorithm using automatic abstraction and refinement to dramatically reduce the number of variables needed to solve the problem.

Social Networking is one of the major technological phe- nomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expres- sion for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mech- anism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solu- tion that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who pro- mote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been pro- posed. In the paper, we also show a proof-of-concept appli- cation, which we implemented in the context of Facebook, one of today’s most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users.

This paper identifies and explores the problem of seed selection in a web-scale crawler. We argue that seed selection is not a trivial but very important problem. Selecting proper seeds can increase the number of pages a crawler will discover, and can result in a collection with more “good” and less “bad” pages. Based on the analysis of the graph structure of the web, we propose several seed selection algorithms. Effectiveness of these algorithms is proved by our experimental results on real web data.