17,578 research outputs found
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
Personal Volunteer Computing
We propose personal volunteer computing, a novel paradigm to encourage
technical solutions that leverage personal devices, such as smartphones and
laptops, for personal applications that require significant computations, such
as animation rendering and image processing. The paradigm requires no
investment in additional hardware, relying instead on devices that are already
owned by users and their community, and favours simple tools that can be
implemented part-time by a single developer. We show that samples of personal
devices of today are competitive with a top-of-the-line laptop from two years
ago. We also propose new directions to extend the paradigm
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
JGraphT -- A Java library for graph data structures and algorithms
Mathematical software and graph-theoretical algorithmic packages to
efficiently model, analyze and query graphs are crucial in an era where
large-scale spatial, societal and economic network data are abundantly
available. One such package is JGraphT, a programming library which contains
very efficient and generic graph data-structures along with a large collection
of state-of-the-art algorithms. The library is written in Java with stability,
interoperability and performance in mind. A distinctive feature of this library
is the ability to model vertices and edges as arbitrary objects, thereby
permitting natural representations of many common networks including
transportation, social and biological networks. Besides classic graph
algorithms such as shortest-paths and spanning-tree algorithms, the library
contains numerous advanced algorithms: graph and subgraph isomorphism; matching
and flow problems; approximation algorithms for NP-hard problems such as
independent set and TSP; and several more exotic algorithms such as Berge graph
detection. Due to its versatility and generic design, JGraphT is currently used
in large-scale commercial, non-commercial and academic research projects. In
this work we describe in detail the design and underlying structure of the
library, and discuss its most important features and algorithms. A
computational study is conducted to evaluate the performance of JGraphT versus
a number of similar libraries. Experiments on a large number of graphs over a
variety of popular algorithms show that JGraphT is highly competitive with
other established libraries such as NetworkX or the BGL.Comment: Major Revisio
DPM: A novel distributed large-scale social graph processing framework for link prediction algorithms
Large-scale graphs have become ubiquitous in social media. Computer-based recommendations in these huge graphs pose challenges in terms of algorithm design and resource usage efficiency when processing recommendations in distributed computing environments. Moreover, recommendation algorithms for graphs, particularly link prediction algorithms, have different requirements depending of the way the underlying graph is traversed. Path-based algorithms usually perform traversals in different directions to build a large ranking of vertices to recommend, whereas random walk-based algorithms build an initial subgraph and perform several iterations on those vertices to compute the final ranking. In this work, we propose a distributed graph processing framework called Distributed Partitioned Merge (DPM), which supports both types of algorithms and we compare its performance and resource usage w.r.t. two relevant frameworks, namely Fork-Join and Pregel. In our experiments, we show that in most tests DPM outperforms both Pregel and Fork-Join in terms of recommendation time, with a minor penalization in network usage in some scenarios.Fil: Corbellini, Alejandro. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Tandil. Instituto Superior de IngenierĂa del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de IngenierĂa del Software; Argentin
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