1,561 research outputs found
A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres
Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned
Department of Computer Science Activity 1998-2004
This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph
mining that facilitates evaluating and constructing high-performance graph
mining algorithms. First, GMS comes with a benchmark specification based on
extensive literature review, prescribing representative problems, algorithms,
and datasets. Second, GMS offers a carefully designed software platform for
seamless testing of different fine-grained elements of graph mining algorithms,
such as graph representations or algorithm subroutines. The platform includes
parallel implementations of more than 40 considered baselines, and it
facilitates developing complex and fast mining algorithms. High modularity is
possible by harnessing set algebra operations such as set intersection and
difference, which enables breaking complex graph mining algorithms into simple
building blocks that can be separately experimented with. GMS is supported with
a broad concurrency analysis for portability in performance insights, and a
novel performance metric to assess the throughput of graph mining algorithms,
enabling more insightful evaluation. As use cases, we harness GMS to rapidly
redesign and accelerate state-of-the-art baselines of core graph mining
problems: degeneracy reordering (by up to >2x), maximal clique listing (by up
to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x),
also obtaining better theoretical performance bounds
Descoberta de recursos para sistemas de escala arbitrarias
Doutoramento em InformáticaTecnologias de Computação DistribuÃda em larga escala tais como Cloud,
Grid, Cluster e Supercomputadores HPC estão a evoluir juntamente com a
emergência revolucionária de modelos de múltiplos núcleos (por exemplo:
GPU, CPUs num único die, Supercomputadores em single die, Supercomputadores
em chip, etc) e avanços significativos em redes e soluções de
interligação. No futuro, nós de computação com milhares de núcleos podem
ser ligados entre si para formar uma única unidade de computação
transparente que esconde das aplicações a complexidade e a natureza distribuÃda desses sistemas com múltiplos núcleos. A fim de beneficiar de forma
eficiente de todos os potenciais recursos nesses ambientes de computação
em grande escala com múltiplos núcleos ativos, a descoberta de recursos é um elemento crucial para explorar ao máximo as capacidade de todos
os recursos heterogéneos distribuÃdos, através do reconhecimento preciso e
localização desses recursos no sistema. A descoberta eficiente e escalável
de recursos ´e um desafio para tais sistemas futuros, onde os recursos e as
infira-estruturas de computação e comunicação subjacentes são altamente
dinâmicas, hierarquizadas e heterogéneas. Nesta tese, investigamos o problema
da descoberta de recursos no que diz respeito aos requisitos gerais da
escalabilidade arbitrária de ambientes de computação futuros com múltiplos
núcleos ativos. A principal contribuição desta tese ´e a proposta de uma
entidade de descoberta de recursos adaptativa hÃbrida (Hybrid Adaptive
Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente
e altamente escalável, construÃda sobre uma sobreposição hierárquica
virtual baseada na auto-organizaçãoo e auto-adaptação de recursos de processamento
no sistema, onde os recursos computacionais são organizados
em hierarquias distribuÃdas de acordo com uma proposta de modelo de
descriçãoo de recursos multi-camadas hierárquicas. Operacionalmente, em
cada camada, que consiste numa arquitetura ponto-a-ponto de módulos que,
interagindo uns com os outros, fornecem uma visão global da disponibilidade
de recursos num ambiente distribuÃdo grande, dinâmico e heterogéneo. O
modelo de descoberta de recursos proposto fornece a adaptabilidade e flexibilidade
para executar consultas complexas através do apoio a um conjunto
de caracterÃsticas significativas (tais como multi-dimensional, variedade e
consulta agregada) apoiadas por uma correspondência exata e parcial, tanto
para o conteúdo de objetos estéticos e dinâmicos. Simulações mostram
que o HARD pode ser aplicado a escalas arbitrárias de dinamismo, tanto
em termos de complexidade como de escala, posicionando esta proposta
como uma arquitetura adequada para sistemas futuros de múltiplos núcleos.
Também contribuÃmos com a proposta de um regime de gestão eficiente
dos recursos para sistemas futuros que podem utilizar recursos distribuÃos
de forma eficiente e de uma forma totalmente descentralizada. Além disso,
aproveitando componentes de descoberta (RR-RPs) permite que a nossa
plataforma de gestão de recursos encontre e aloque dinamicamente recursos
disponÃeis que garantam os parâmetros de QoS pedidos.Large scale distributed computing technologies such as Cloud, Grid, Cluster
and HPC supercomputers are progressing along with the revolutionary emergence
of many-core designs (e.g. GPU, CPUs on single die, supercomputers
on chip, etc.) and significant advances in networking and interconnect solutions.
In future, computing nodes with thousands of cores may be connected
together to form a single transparent computing unit which hides from applications
the complexity and distributed nature of these many core systems. In
order to efficiently benefit from all the potential resources in such large scale
many-core-enabled computing environments, resource discovery is the vital
building block to maximally exploit the capabilities of all distributed heterogeneous
resources through precisely recognizing and locating those resources
in the system. The efficient and scalable resource discovery is challenging for
such future systems where the resources and the underlying computation and
communication infrastructures are highly-dynamic, highly-hierarchical and
highly-heterogeneous. In this thesis, we investigate the problem of resource
discovery with respect to the general requirements of arbitrary scale future
many-core-enabled computing environments. The main contribution of this
thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel
efficient and highly scalable resource-discovery approach which is built upon
a virtual hierarchical overlay based on self-organization and self-adaptation
of processing resources in the system, where the computing resources are
organized into distributed hierarchies according to a proposed hierarchical
multi-layered resource description model. Operationally, at each layer, it
consists of a peer-to-peer architecture of modules that, by interacting with
each other, provide a global view of the resource availability in a large,
dynamic and heterogeneous distributed environment. The proposed resource
discovery model provides the adaptability and flexibility to perform complex
querying by supporting a set of significant querying features (such as
multi-dimensional, range and aggregate querying) while supporting exact
and partial matching, both for static and dynamic object contents. The
simulation shows that HARD can be applied to arbitrary scales of dynamicity,
both in terms of complexity and of scale, positioning this proposal as a
proper architecture for future many-core systems. We also contributed to
propose a novel resource management scheme for future systems which
efficiently can utilize distributed resources in a fully decentralized fashion.
Moreover, leveraging discovery components (RR-RPs) enables our resource
management platform to dynamically find and allocate available resources
that guarantee the QoS parameters on demand
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Resource discovery for distributed computing systems: A comprehensive survey
Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category
Distributed Database Management Techniques for Wireless Sensor Networks
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Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9
Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers
CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities.
The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers.
A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI
Mining a Small Medical Data Set by Integrating the Decision Tree and t-test
[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI
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