Pregelix: Big(ger) Graph Analytics on A Dataflow Engine

There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up to 35x speedup compared to distributed GraphLab), and makes more effective use of available machine resources to support Big(ger) Graph Analytics

Experimental Evaluation of Wireless Mesh Networks: A Case Study and Comparison

Price of WiFi devices has decreased dramatically in recent years, while new standards, as 802.11n, have multiplied its performance. This has fostered the deployment of Wireless Mesh networks (WMN), putting into practice concepts evolved from more than a decade of research in Ad Hoc networks. Nevertheless, evolution of WMN it is in its infancy, as shows the growing and diverse number of scenarios where WMN are being deployed. In these paper we analyze a particular case study of a Wireless Community Mesh Network, and we compare it with a selected experimental WMN studies found in the literature

Cluster searching strategies for collaborative recommendation systems

Cataloged from PDF version of article.In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster- skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. © 2012 Elsevier Ltd. All rights reserved