782 research outputs found
mARC: Memory by Association and Reinforcement of Contexts
This paper introduces the memory by Association and Reinforcement of Contexts
(mARC). mARC is a novel data modeling technology rooted in the second
quantization formulation of quantum mechanics. It is an all-purpose incremental
and unsupervised data storage and retrieval system which can be applied to all
types of signal or data, structured or unstructured, textual or not. mARC can
be applied to a wide range of information clas-sification and retrieval
problems like e-Discovery or contextual navigation. It can also for-mulated in
the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast
to Conway approach, the objects evolve in a massively multidimensional space.
In order to start evaluating the potential of mARC we have built a mARC-based
Internet search en-gine demonstrator with contextual functionality. We compare
the behavior of the mARC demonstrator with Google search both in terms of
performance and relevance. In the study we find that the mARC search engine
demonstrator outperforms Google search by an order of magnitude in response
time while providing more relevant results for some classes of queries
Evaluating a Cluster of Low-Power ARM64 Single-Board Computers with MapReduce
With the meteoric rise of enormous data collection in science, industry, and the cloud, methods for processing massive datasets have become more crucial than ever. MapReduce is a restricted programing model for expressing parallel computations as simple serial functions, and an execution framework for distributing those computations over large datasets residing on clusters of commodity hardware. MapReduce abstracts away the challenging low-level synchronization and scalability details which parallel and distributed computing often necessitate, reducing the concept burden on programmers and scientists who require data processing at-scale. Typically, MapReduce clusters are implemented using inexpensive commodity hardware, emphasizing quantity over quality due to the fault-tolerant nature of the MapReduce execution framework. The nascent explosion of inexpensive single-board computers designed around multi-core 64bit ARM processors, such as the RasberryPi 3, Pine64, and Odroid C2, has opened new avenues for inexpensive and low-power cluster computing. In this thesis, we implement a novel cluster around low-power ARM64 single-board computers and the Disco Python MapReduce execution framework. We use MapReduce to empirically evaluate our cluster by solving the Word Count and Inverted Link Index problems for the Wikipedia article dataset. We benchmark our MapReduce solutions against local solutions of the same algorithms for a conventional low-power x86 platform. We show our cluster out-performs the conventional platform for larger benchmarks, thus demonstrating low-power single-board computers as a viable avenue for data-intensive cluster computing
Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia
Hyperlinks are an essential feature of the World Wide Web. They are
especially important for online encyclopedias such as Wikipedia: an article can
often only be understood in the context of related articles, and hyperlinks
make it easy to explore this context. But important links are often missing,
and several methods have been proposed to alleviate this problem by learning a
linking model based on the structure of the existing links. Here we propose a
novel approach to identifying missing links in Wikipedia. We build on the fact
that the ultimate purpose of Wikipedia links is to aid navigation. Rather than
merely suggesting new links that are in tune with the structure of existing
links, our method finds missing links that would immediately enhance
Wikipedia's navigability. We leverage data sets of navigation paths collected
through a Wikipedia-based human-computation game in which users must find a
short path from a start to a target article by only clicking links encountered
along the way. We harness human navigational traces to identify a set of
candidates for missing links and then rank these candidates. Experiments show
that our procedure identifies missing links of high quality
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Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation
In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset
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