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Implementing Materialized View of Large-Scale Power Consumption Log Using MapReduce

Implementing Materialized View of Large-Scale Power Consumpt...

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Yuki Ise Shintaro Yamamoto Shinsuke Matsumoto Sachio Saiki Masahide Nakamura Graduate School of System Informatics Kobe University Hyogo Japan

ISBN: (纸本)9781479903719

Smart city provides various value-added services by collecting large-scale data from houses and infrastructures within a city. However, it takes a long time for individual applications to use and process the large-scale raw data directly. To reduce the response time, we use the concept of materialized view of database. For a given requirement of an application, the proposed method constructs a materialized view for caching the application-specific data. In this paper, we especially develop a method that uses MapReduce for large-scale power consumption data stored in HBase KVS. We conduct an experimental evaluation to compare the response time between cases with and without the materialized view. As a result, the proposed method with materialized view is effective especially when application repeatedly access the same data, or when the application-specific data is derived from a large set of raw data.

关键词： Power demand Cities and towns Time factors distributed databases Time measurement Home appliances distributed processing

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System Architecture and Feature Design for Engineering a Web-Based Travel Advisor System: FanOnTour

System Architecture and Feature Design for Engineering a Web...

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Amir Maddah Manolya Kavakli Department of Computing Macquarie University Sydney Australia

Many Advisor Systems have been designed and developed to assist us in decision-making, each with their strengths and weaknesses. A popular type of Advisor systems is Travel Advisor systems that assist travelers in their travel arrangements. We have designed and implemented a Travel Advisor system to assist travelers, evaluating a number of factors after analyzing the features of other Travel Advisor systems. these factors are travelers' budgets, distance, their friends' interests, individual and group interests, dislikes, transportation mode and travel histories. We demonstrate the validity of the solution using case studies and usability testing results. In this study, our major goal is to measure the system's usability both with participants who previously travelled to the location and with participants who have never been there to investigate if familiarity correlates with positive rating of the system features. Our findings state that familiarity with the location correlates with positive rating of system's features.

关键词： Usability Testing Cities and towns Knowledge based systems Legged locomotion Transportation Meteorology

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Cut-and-sew: A distributed autonomous localization algorithm for 3D surface wireless sensor networks 13

Cut-and-sew: A distributed autonomous localization algorithm...

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14th ACM international Symposium on Mobile Ad Hoc networking and computing, MobiHoc 2013

作者： Zhao, Yao Wu, Hongyi Jin, Miao Yang, Yang Zhou, Hongyu Xia, Su Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette United States

ISBN: (纸本)9781450321938

Location awareness is imperative for a variety of sensing applications and network operations. Although a diversity of GPS-less and GPS-free solutions have been developed recently for autonomous localization in wireless sensor networks, they primarily target at 2D planar or 3D volumetric settings. there exists unique and fundamental hardness to extend them to 3D surface. the contributions of this work are twofold. First, it proposes a theoretically-proven algorithm for the 3D surface localization problem. Seeing the chal-lenges to localize general 3D surface networks and the solvability of the localization problem on single-value (SV) surface, this work proposes the cut-and-sew algorithm that takes a divide-and-conquer approach by partitioning a general 3D surface network into SV patches, which are localized individually and then merged into a unified coordinates system. the algorithm is optimized by discovering the minimum SV partition, an optimal partition that creates a minimum set of SV patches. Second, it develops practically-viable solutions for real-world sensor network settings where the inputs are often noisy. the proposed algorithm is implemented and evaluated via simulations and experiments in an indoor testbed. the results demonstrate that the proposed cut-and-sew algorithm achieves perfect 100% localization rate and the desired robustness against measurement errors. Copyright 2013 ACM.

关键词： Wireless sensor networks

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A distributed Delaunay triangulation algorithm based on centroidal Voronoi tessellation for wireless sensor networks 13

A distributed Delaunay triangulation algorithm based on cent...

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14th ACM international Symposium on Mobile Ad Hoc networking and computing, MobiHoc 2013

作者： Zhou, Hongyu Jin, Miao Wu, Hongyi University of Louisiana at Lafayette Lafayette LA 70503 United States

ISBN: (纸本)9781450321938

A wireless sensor network can be represented by a graph. While the network graph is extremely useful, it often exhibits undesired irregularity. therefore, special treatment of the graph is required by a variety of network algorithms and protocols. In particular, many geometry-oriented algorithms depend on a type of subgraph called Delaunay triangulation. However, when location information is unavailable, it is nontrivial to achieve Delaunay triangulation by using connectivity information only. the only connectivitybased algorithm available for Delaunay triangulation is built upon the property that the dual graph for a Voronoi diagram is a Delaunay triangulation. this approach, however, often fails in practical wireless sensor networks because the boundaries of Voronoi cells can be arbitrarily short in discrete sensor network settings. In a sensor network with connectivity information only, it is fundamentally unattainable to correctly judge neighboring cells when a Voronoi cell boundary is less than one hop. Consequently, the Voronoi diagram-based Delaunay triangulation fails. the proposed algorithm employs a distributed approach to perform centroidal Voronoi tessellation, and constructs its dual graph to yield Delaunay triangulation. It exhibits several distinctive properties. First, it eliminates the problem due to short cell boundaries and thus effectively avoids crossing edges. Second, the proposed algorithm is proven to converge and succeed in constructing a Delaunay triangulation, if the CVT cell size is greater than a constant threshold. third, the established Delaunay triangulation consists of close-to-equilateral triangles, benefiting a range of applications such as geometric routing, localization, coverage, segmentation, and data storage and processing. Extensive simulations are carried out under various 2D network models to evaluate the effectiveness and efficiency of the proposed CVT-based triangulation algorithm. Copyright 2013 ACM.

关键词： Computational geometry

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Efficient SVM Training Using Parallel Primal-Dual Interior Point Method on GPU 14

Efficient SVM Training Using Parallel Primal-Dual Interior P...

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14th international conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Jin, Jing Cai, Xianggao Lin, Xiaola Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sch Informat Management Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781479924189

the training of SVM can be viewed as a Convex Quadratic Programming (CQP) problem which becomes difficult to be solved when dealing with the large scale data sets. Traditional methods such as Sequential Minimal Optimization (SMO) for SVM training is used to solve a sequence of small scale sub-problems, which costs a large amount of computation time and is hard to be accelerated by utilizing the computation power of GPU. Although Interior Point Method (IPM) such as primal-dual interior point method (PDIPM) can be also addressed SVM training well and has favourable potential for parallelizing on GPU, it contains comparatively high time complexity O(l(3)) and space complexity O(l(2)), where l is the number of training instances. Fortunately, by invoking low-rank approximation methods such as Incomplete Cholesky Factorization (ICF) and Sherman Morrison Woodbury formula (SMW), the requirements of both storage and computation of PDIPM can be reduced significantly. In this paper, a parallel PDIPM method (P-PDIPM) along with a parallel ICF method (P-ICF) is proposed to accelerate the SVM training on GPU. Experimental results indicate that the training speed of P-PDIPM on GPU is almost 40x faster than that of the serial one (S-PDIPM) on CPU. Besides, without extensive optimization, P-PDIPM can obtain about 8x speedup over the state of the art tool LIBSVM while maintaining high prediction accuracy.

关键词： Parallel computing Graphic Processing Unit Support Vector Machines Primal-Dual Interior Point Method Incomplete Cholesky Factorization Sherman Morrison Woodbury Formula

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Privacy-Preserving Ranked Fuzzy Keyword Search over Encrypted Cloud Data 14

Privacy-Preserving Ranked Fuzzy Keyword Search over Encrypte...

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14th international conference on Parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Xu, Qunqun Shen, Hong Sang, Yingpeng Tian, Hui Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781479924189

As Cloud computing becomes popular, more and more data owners prefer to store their data into the cloud for great flexibility and economic savings. In order to protect the data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although traditional searchable symmetric encryption schemes allow users to securely search over encrypted data through keywords and selectively retrieve files of interest without capturing any relevance of data files or search keywords, and fuzzy keyword search on encrypted data allows minor typos and format inconsistencies, secure ranked keyword search captures the relevance of data files and returns the results that are wanted most by users. these techniques function unilaterally, which greatly reduces the system usability and efficiency. In this paper, for the first time, we define and solve the problem of privacy-preserving ranked fuzzy keyword search over encrypted cloud data. Ranked fuzzy keyword search greatly enhances system usability and efficiency when exact match fails. It returns the matching files in a ranked order with respect to certain relevance criteria (e.g., keyword frequency) based on keyword similarity semantics. In our solution, we exploit the edit distance to quantify keyword similarity and dictionary-based fuzzy set construction to construct fuzzy keyword sets, which greatly reduces the index size, storage and communication costs. We choose the efficient similarity measure of "coordinate matching", i.e., as many matches as possible, to obtain the relevance of data files to the search keywords.

关键词： cloud computing searchable encryption fuzzy keyword search dictionary-based fuzzy set ranked keyword search one-to-many order-preserving mapping

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Cloud Resource Provisioning and Bursting Approaches

Cloud Resource Provisioning and Bursting Approaches

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Arwa S. Fadel Ayman G. Fayoumi Department of Computer Sciences King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781479903719

Cloud computing recently emerged as a new paradigm that aims to deploy services via Internet. Under hybrid cloud environment, cloud bursting is a technique that combines local (organizations or in-house) resources with public cloud resources, these resources are leased based on a pay-per-use basis. It used to process the overload work within local resource or to accelerate the execution time of distributed applications with respect of the required level of QoS, also to achieve the efficient use of private resources. When Cloud bursting is applied, the important issues are determining how many and which type of resources will be provisioned. Also, the important issues that should be considered before cloud bursting decision are which workload will be burst to public cloud and when these resources will be released. these issues attract the attention of researchers to tackle them. In this paper we intend to review the recent researches that concern on cloud bursting and resource provisioning. We will explore how each study address the problem, what are the proposed solutions and what are the differences between them, the main researches are compared interms of several criteria such as type of application, which was targeted by the research, environment that used to implement the experiment, results, and limitations.

关键词： Cloud computing Organizations Data transfer Acceleration Quality of service Time factors Resource management

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A Study of Data Management in Hybrid Cloud Configuration

A Study of Data Management in Hybrid Cloud Configuration

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Shigeaki Tanimoto Yorihiro Sakurada Yosiaki Seki Motoi Iwashita Shinsuke Matsui Hiroyuki Sato Atsushi Kanai Chiba Institute of Technology Japan NTT Secure Platform Laboratories Japan University of Tokyo Japan Hosei University Japan

ISBN: (纸本)9781479903719

Cloud computing is currently spreading with the further implementation of IT infrastructure. Clouds involve certain negative factors, however, which must be addressed to promote further cloud utilization. As a means of addressing some of these negative factors, a hybrid cloud configuration combining public and private clouds has attracted attention. this configuration stores personal and confidential information in a private cloud and other data in a public cloud. Unfortunately, no detailed classification of where various kinds of data should be stored has yet been fully established. Hence, this paper proposes an enterprise data management method for a hybrid cloud configuration. Specifically, enterprise data was subdivided into 28 categories through a work breakdown structure (WBS) method. then, the subdivided data was classified in terms of whether it did or did not consist of personal or confidential information. the resulting data portfolio required storing only about 18% of data in a private cloud, with the remaining 82% in a public cloud. In accordance with this basic guideline, a fundamental data management proposal is developed for the hybrid cloud configuration.

关键词： Cloud computing Portfolios Companies Security Computational modeling Guidelines

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An efficient and self-Adaptive model based on scatter search: Solving the grid resources selection problem

An efficient and self-Adaptive model based on scatter search...

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14th international conference on Computer Aided Systems theory, EUROCAST 2013

作者： Botón-Fernández, María Vega-Rodríguez, Miguel A. Prieto-Castrillo, Francisco Ceta-Ciemat Dept. Science and Technology Trujillo Spain University of Extremadura Dept. Technologies of Computers and Communications Caceres Spain

ISBN: (纸本)9783642538551

Grid computing environments are distributed systems formed by a heterogeneous and geographically distributed resource set. In spite of the advantages of such paradigm, several problems related to resources availability and resources selection have become a challenge extensively studied by the grid community in last years. the aim of this work is to provide an intelligent and self-Adaptive model for selecting grid resources during applications execution. this adaptive capability is obtained by applying during the selection process an evolutionary method known as Scatter Search (it is based on quality and diversity criteria). Finally, the model is evaluated in a real grid infrastructure. the results show that the infrastructure throughput is enhanced. Even more, a reduction in the applications execution time and an improvement of the successfully finished tasks rate are also achieved. As a conclusion, the proposed model is a feasible solution for grid applications. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Grid computing

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Transactional failure recovery for a distributed key-value store

Transactional failure recovery for a distributed key-value s...

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14th ACM/IFIP/USENIX Middleware conference, Middleware 2013

作者： Ahmad, Muhammad Yousuf Kemme, Bettina Brondino, Ivan Patinõ-Martínez, Marta Jiḿenez-Peris, Ricardo McGill University Canada Universidad Politécnica de Madrid Spain

ISBN: (纸本)9783642450648

With the advent of cloud computing, many applications have embraced the ensuing paradigm shift towards modern distributed key-value data stores, like HBase, in order to benefit from the elastic scalability on offer. However, many applications still hesitate to make the leap from the traditional relational database model simply because they cannot compromise on the standard transactional guarantees of atomicity, isolation, and durability. To get the best of both worlds, one option is to integrate an independent transaction management component with a distributed key-value store. In this paper, we discuss the implications of this approach for durability. In particular, if the transaction manager provides durability (e.g., through logging), then we can relax durability constraints in the key-value store. However, if a component fails (e.g., a client or a key-value server), then we need a coordinated recovery procedure to ensure that commits are persisted correctly. In our research, we integrate an independent transaction manager with HBase. Our main contribution is a failure recovery middleware for the integrated system, which tracks the progress of each commit as it is flushed down by the client and persisted within HBase, so that we can recover reliably from failures. During recovery, commits that were interrupted by the failure are replayed from the transaction management log. Importantly, the recovery process does not interrupt transaction processing on the available servers. Using a benchmark, we evaluate the impact of component failure, and subsequent recovery, on application performance. © IFIP international Federation for Information Processing 2013.

关键词： Fault tolerance

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