15,671 research outputs found
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
Beyond similarity: A network approach for identifying and delimiting biogeographical regions
Biogeographical regions (geographically distinct assemblages of species and
communities) constitute a cornerstone for ecology, biogeography, evolution and
conservation biology. Species turnover measures are often used to quantify
biodiversity patterns, but algorithms based on similarity and clustering are
highly sensitive to common biases and intricacies of species distribution data.
Here we apply a community detection approach from network theory that
incorporates complex, higher order presence-absence patterns. We demonstrate
the performance of the method by applying it to all amphibian species in the
world (c. 6,100 species), all vascular plant species of the USA (c. 17,600),
and a hypothetical dataset containing a zone of biotic transition. In
comparison with current methods, our approach tackles the challenges posed by
transition zones and succeeds in identifying a larger number of commonly
recognised biogeographical regions. This method constitutes an important
advance towards objective, data derived identification and delimitation of the
world's biogeographical regions.Comment: 5 figures and 1 supporting figur
Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus
This paper reports on a multi-fold approach for the building of user models based on the identification of navigation patterns in a virtual campus, allowing for adapting the campus’ usability to the actual learners’ needs, thus resulting in a great stimulation of the learning experience. However, user modeling in this context implies a constant processing and analysis of user interaction data during long-term learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily, the massive processing is a foremost step in extracting useful information. To this end, this work studies, first, the viability of processing large log data files of a real Virtual Campus using different distributed infrastructures. More precisely, we study the time performance of massive processing of daily log files implemented following the master-slave paradigm and evaluated using Cluster Computing and PlanetLab platforms. The study reveals the complexity and challenges of massive processing in the big data era, such as the need to carefully tune the log file processing in terms of chunk log data size to be processed at slave nodes as well as the bottleneck in processing in truly geographically distributed infrastructures due to the overhead caused by the communication time among the master and slave nodes. Then, an application of the massive processing approach resulting in log data processed and stored in a well-structured format is presented. We show how to extract knowledge from the log data analysis by using the WEKA framework for data mining purposes showing its usefulness to effectively build user models in terms of identifying interesting navigation patters of on-line learners. The study is motivated and conducted in the context of the actual data logs of the Virtual Campus of the Open University of Catalonia.Peer ReviewedPostprint (author's final draft
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