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XER: A Recommendation for XML Element

XER: A Recommendation for XML Element

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International Conference on Information Technology: New Generations (ITNG)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

The strong point of the XML is that it allows document owners to describe their documents in their own format (structure and element names). There might be the same information has been describe in various ways. When user wants to search some information from XML documents, user might not retrieve all related results because of the difference of elements (tag names). To retrieve all related results, user needs to have queries for all possible elements. XML Element Recommendation (XER) is an idea to make XML documents easier for searching. It receives XML document as an input and compares that input with XML documents from database. It compares word similarity and semantic of each element. For semantic similarity of each element, it checks those elements to find which element (word) should be used by using word ranking (most common words). The output is a recommendation for each element of input XML document.

关键词： XML Mobile communication Mobile ad hoc networks Routing protocols Ad hoc networks Earthquakes Network topology Motion analysis Vehicle dynamics Costs

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XML Document Recommendation by Using Case Based Reasoning

XML Document Recommendation by Using Case Based Reasoning

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

Case Based Reasoning (CBR) is the process of solving new problem based on the solutions of similar previous problems. XML is a markup language that allows document owners describe their data in their own formats. There might be the same information has been described in various ways. This makes searching those information from different formats difficult. CXDR (CBR for XML Document Recommendation) is an idea to make searching XML documents easier. CXDR applies CBR framework to collect the mostly use XML document and recommend it to its user. CBR in CXDR consists of two parts, cases and knowledge. The case is XML documents and the knowledge is an XML document recommendation. The recommendation from CXDR is the most use XML element names and structure.

关键词： XML Keyword search Information retrieval Markup languages Database languages software engineering Artificial intelligence Distributed computing software systems systems engineering and theory

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Gene Expression Classification with a Novel Coevolutionary Based Learning Classifier System on Public Clouds

Gene Expression Classification with a Novel Coevolutionary B...

引用

IEEE International Conference on E-science Workshops

作者： Christian Vecchiola Mani Abedini Michael Kirley Xingchen Chu Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia

Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.

关键词： Cloud computing Gene expression Accuracy Machine learning Containers computer architecture

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A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

A Particle Swarm Optimization-Based Heuristic for Scheduling...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Suraj Pandey Linlin Wu Siddeswara Mayura Guru Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia CSIRO Tasmanian ICT Centre Hobart Australia

ISBN: (纸本)9781424466955;9780769540184

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

关键词： Particle swarm optimization Processor scheduling Cloud computing Application software Cost function Computational efficiency Dynamic scheduling computer science software engineering Application virtualization

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A Taxonomy of Autonomic Application Management in Grids

A Taxonomy of Autonomic Application Management in Grids

引用

International Conference on Parallel and Distributed systems (ICPADS)

作者： Mustafizur Rahman Rajiv Ranjan Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives.

关键词： Monitoring Resource management Taxonomy Processor scheduling Decision making Dynamic scheduling

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Energy-Aware Loop Parallelism Maximization for Multi-core DSP Architectures

Energy-Aware Loop Parallelism Maximization for Multi-core DS...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Meikang Qiu Jian-Wei Niu Laurence T. Yang Xiao Qin Senlin Zhang Bin Wang Department of Electrical and Computer Engineering University of Kentucky KY USA State Key Laboratory of Software Development Environment Beihang University Beijing China Department of Computer Science Saint Francis Xavier University NS Canada Department of Computer Science and Software Engineering Aubum University Auburn AL USA Department of Systems Science and Engineering University of Zhejiang Hangzhou China Department of Computer Science and Engineering Wright State University Dayton OH USA

With the advance of semiconductor, multi-core architecture is inevitable in today's embedded system design. Nested loops are usually the most critical part in multimedia and high performance DSP (Digital Signal Processing) systems. Hence, maximizing loop parallelism is an important issue to improve the performance of a modern compiler. This paper studies how to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops on multi-core DSP architectures. An algorithm, EALPM (Energy-Aware Loop Parallelism Maximization), is proposed in this paper. We implemented a two phase strategy. First, the strategy uses retiming and loop transformation to parallelize nested loops. Then the strategy employs a novel voltage assignment algorithm to reduce total energy consumption. The experimental results show that on average, both performance and energy-saving can be significantly improved using the EALPM algorithm.

关键词： Parallel processing Energy consumption Voltage control Optimization Algorithm design and analysis computer architecture Digital signal processing

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CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications

CloudAnalyst: A CloudSim-based Visual Modeller for Analysing...

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International Conference on Advanced Information Networking and Applications

作者： Bhathiya Wickremasinghe Rodrigo N. Calheiros Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The University of Melbourne Pontifical Catholic University of Rio Grande do Sul

ISBN: (纸本)9781424466955;9780769540184

Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

关键词： Cloud Computing Modeling Simulation

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TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of Mass Storage systems

TRACER: A Trace Replay Tool to Evaluate Energy-Efficiency of...

引用

IEEE International Conference on Cluster Computing

作者： Zhuo Liu Fei Wu Xiao Qin Changsheng Xie Jian Zhou Jianzong Wang Wuhan National Laboratory of Optoelectronics Key Laboratory of Data Storage Systems Ministry of Education of China School of Computer Science and Tech Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Software Engineering Aubum University Auburn AL USA

Improving energy efficiency of mass storage systems has become an important and pressing research issue in large HPC centers and data centers. New energy conservation techniques in storage systems constantly spring up; however, there is a lack of systematic and uniform way of accurately evaluating energy-efficient storage systems and objectively comparing a wide range of energy-saving techniques. This research presents a new integrated scheme, called TRACER, for evaluating energy-efficiency of mass storage systems and judging energy-saving techniques. The TRACER scheme consists of a toolkit used to measure energy efficiency of storage systems as well as performance and energy metrics. In addition, TRACER contains a novel and accurate workload-control module to acquire power varying with workload modes and I/O load intensity. The workload generator in TRACER facilitates a block-level trace replay mechanism. The main goal of the workload-control module is to select a certain percentage (e.g., anywhere from 10% to 100%) of trace entries from a real-world I/O trace file uniformly and to replay filtered trace entries to reach any level of I/O load intensity. TRACER is experimentally validated on a general RAID5 enterprise disk array. Our experiments demonstrate that energy-efficient mass storage systems can be accurately evaluated on full scales by TRACER. We applied TRACER to investigate impacts of workload modes and load intensity on energy-efficiency of storage devices. This work shows that TRACER can enable storage system developers to evaluate energy efficiency designs for storage systems.

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An associative memory system for incremental learning and temporal sequence

An associative memory system for incremental learning and te...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Shen, Furao Yu, Hui Kasai, Wataru Hasegawa, Osamu State Key Laboratory for Novel Software Technology Jiangyin Information Technology Research Institute Nanjing University Nanjing 210093 China Department of Computer Intelligence and Systems Science Tokyo Institute of Technology Yokohama 226-8503 Japan Imaging Science and Engineering Lab. Tokyo Institute of Technology R2-52 4259 Nagatsuda Midori-ku Yokohama 226-8503 Japan

ISBN: (纸本)9781424469178

An associative memory (AM) system is proposed to realize incremental learning and temporal sequence learning. The proposed system is constructed with three layer networks: The input layer inputs key vectors, response vectors, and the associative relation between vectors. The memory layer stores input vectors incrementally to corresponding classes. The associative layer builds associative relations between classes. The proposed method can incrementally learn key vectors and response vectors;store and recall both static information and temporal sequence information;and recall information from incomplete or noise-polluted inputs. Experiments using binary data, real-value data, and temporal sequences show that the proposed method works well. © 2010 IEEE.

关键词： Vectors

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A cost and space efficient method for unbalanced assignment problems

A cost and space efficient method for unbalanced assignment ...

引用

IEEE International Conference on Industrial engineering and engineering Management

作者： Anupong Iampang Veera Boonjing Pisit Chanvarasuth Software Systems Engineering Laboratory Department of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

ISBN: (纸本)9781424485017

In this paper, a new cost and space efficient solution to unbalanced assignment problems is presented. The new solution uses linear space complexity instead of polynomial complexity as used by the Hungarian method. Moreover, its optimal cost is lower than of the Kumar method - an existing linear space complexity solution to unbalanced assignment problems.

关键词： Complexity theory Simulation Space technology Electronic mail Polynomials Logistics

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