In the last few years, the increasing interest in location-based services (LBS) has favored the introduction of geo-referenced information in various Web 2.0 applications, as well as the rise of location-based social networks (LBSN). Foursquare, one of the most popular LBSNs, gives incentives to users who visit (check in) specific places (venues) by means of, for instance, mayorships to frequent visitors. Moreover, users may leave tips at specific venues as well as mark previous tips as done in sign of agreement. Unlike check ins, which are shared only with friends, the lists of mayorships, tips and dones of a user are publicly available to everyone, thus raising concerns about disclosure of the user's movement patterns and interests. We analyze how users explore these publicly available features, and their potential as sources of information leakage. Specifically, we characterize the use of mayorships, tips and dones in Foursquare based on a dataset with around 13 million users. We also analyze whether it is possible to easily infer the home city (state and country) of a user from these publicly available information. Our results indicate that one can easily infer the home city of around 78% of the analyzed users within 50 kilometers.

MapReduce is an important paradigm to support modern data-intensive applications. In this paper we address the challenge of modeling performance of one implementation of MapReduce called Hadoop Online Prototype (HOP), with a specific target on the intra-job pipeline parallelism. We use a hierarchical model that combines a precedence model and a queuing network model to capture the intra-job synchronization constraints. We first show how to build a precedence graph that represents the dependencies among multiple tasks of the same job. We then apply it jointly with an approximate Mean Value Analysis (aMVA) solution to predict mean job response time and resource utilization. We validate our solution against a queuing network simulator in various scenarios, finding that our performance model presents a close agreement, with maximum relative difference under 15%.

This work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimensions of the problem: (i) term co-occurrence with tags pre-assigned to the target object, (ii) terms extracted from multiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic methods, which extend state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object's content. We also exploit two learning-to-rank (L2R) techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Further improvements can also be achieved with the new L2R strategies, which have the additional advantage of being quite flexible and extensible to exploit other aspects of the tag recommendation problem.