37,914 research outputs found
The NASA Astrophysics Data System: Architecture
The powerful discovery capabilities available in the ADS bibliographic
services are possible thanks to the design of a flexible search and retrieval
system based on a relational database model. Bibliographic records are stored
as a corpus of structured documents containing fielded data and metadata, while
discipline-specific knowledge is segregated in a set of files independent of
the bibliographic data itself.
The creation and management of links to both internal and external resources
associated with each bibliography in the database is made possible by
representing them as a set of document properties and their attributes.
To improve global access to the ADS data holdings, a number of mirror sites
have been created by cloning the database contents and software on a variety of
hardware and software platforms.
The procedures used to create and manage the database and its mirrors have
been written as a set of scripts that can be run in either an interactive or
unsupervised fashion.
The ADS can be accessed at http://adswww.harvard.eduComment: 25 pages, 8 figures, 3 table
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
Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground
The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD
AMaĻoSāAbstract Machine for Xcerpt
Web query languages promise convenient and efficient access
to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web
query language with strong emphasis on novel high-level constructs for
effective and convenient query authoring, particularly tailored to versatile
access to data in different Web formats such as XML or RDF.
However, so far it lacks an efficient implementation to supplement the
convenient language features. AMaĻoS is an abstract machine implementation
for Xcerpt that aims at efficiency and ease of deployment. It
strictly separates compilation and execution of queries: Queries are compiled
once to abstract machine code that consists in (1) a code segment
with instructions for evaluating each rule and (2) a hint segment that
provides the abstract machine with optimization hints derived by the
query compilation. This article summarizes the motivation and principles
behind AMaĻoS and discusses how its current architecture realizes
these principles
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for answering the questions. We show that, in comparison to
other recently introduced large-scale datasets, TriviaQA (1) has relatively
complex, compositional questions, (2) has considerable syntactic and lexical
variability between questions and corresponding answer-evidence sentences, and
(3) requires more cross sentence reasoning to find answers. We also present two
baseline algorithms: a feature-based classifier and a state-of-the-art neural
network, that performs well on SQuAD reading comprehension. Neither approach
comes close to human performance (23% and 40% vs. 80%), suggesting that
TriviaQA is a challenging testbed that is worth significant future study. Data
and code available at -- http://nlp.cs.washington.edu/triviaqa/Comment: Added references, fixed typos, minor baseline updat
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
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