111,414 research outputs found
A Lightweight Distributed Solution to Content Replication in Mobile Networks
Performance and reliability of content access in mobile networks is
conditioned by the number and location of content replicas deployed at the
network nodes. Facility location theory has been the traditional, centralized
approach to study content replication: computing the number and placement of
replicas in a network can be cast as an uncapacitated facility location
problem. The endeavour of this work is to design a distributed, lightweight
solution to the above joint optimization problem, while taking into account the
network dynamics. In particular, we devise a mechanism that lets nodes share
the burden of storing and providing content, so as to achieve load balancing,
and decide whether to replicate or drop the information so as to adapt to a
dynamic content demand and time-varying topology. We evaluate our mechanism
through simulation, by exploring a wide range of settings and studying
realistic content access mechanisms that go beyond the traditional
assumptionmatching demand points to their closest content replica. Results show
that our mechanism, which uses local measurements only, is: (i) extremely
precise in approximating an optimal solution to content placement and
replication; (ii) robust against network mobility; (iii) flexible in
accommodating various content access patterns, including variation in time and
space of the content demand.Comment: 12 page
Rough Sets Clustering and Markov model for Web Access Prediction
Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation
An empirical comparison of supervised machine learning techniques in bioinformatics
Research in bioinformatics is driven by the experimental data.
Current biological databases are populated by vast amounts of
experimental data. Machine learning has been widely applied to
bioinformatics and has gained a lot of success in this research
area. At present, with various learning algorithms available in the
literature, researchers are facing difficulties in choosing the best
method that can apply to their data. We performed an empirical
study on 7 individual learning systems and 9 different combined
methods on 4 different biological data sets, and provide some
suggested issues to be considered when answering the following
questions: (i) How does one choose which algorithm is best
suitable for their data set? (ii) Are combined methods better than
a single approach? (iii) How does one compare the effectiveness
of a particular algorithm to the others
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