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检索条件"主题词=Partitioning algorithms"

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In-Map/In-Reduce: Concurrent Job Execution in MapReduce

In-Map/In-Reduce: Concurrent Job Execution in MapReduce

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Muhammad Idris Shujaat Hussain Sungyoung Lee College of Electronics and Information Kyung Hee University Republic of Korea

Hadoop based Map Reduce (MR) has emerged as big data processing mechanism in terms of its data intensive applications. In data intensive systems, analysis and visualizations as a result of various algorithms can lead to differentiable and comparable results. Current implementations of MR facilitates to reuse the results of MR jobs in other MR jobs and to distribute the cloud resources among jobs. However, very little work is done in terms of using same data for multiple algorithms at the same time in a single job using either shared resources or dynamic resource allocation based on the data and scheduling of Map Reduce jobs. In this paper we propose a method to execute multiple algorithms on same data in HDFS concurrently and to use the same available resources by dynamically managing the task assignment and results aggregation. Our proposed approach reduces the execution time and supports multiple algorithms execution in parallel. In-Map/In-Reduce shows 200% decrease in execution time.

关键词： partitioning algorithms Clustering algorithms Big data Algorithm design and analysis Distributed databases Educational institutions

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Bi-Lipschitz Bijection between the Boolean Cube and the Hamming Ball

Bi-Lipschitz Bijection between the Boolean Cube and the Hamm...

引用

Annual IEEE Symposium on Foundations of Computer Science

作者： Itai Benjamini Gil Cohen Igor Shinkar Department of Mathematics Weizmann Institute of Science Rehovot Israel Department of Computer Science Weizmann Institute of Science Rehovot Israel

We construct a bi-Lipschitz bijection from the Boolean cube to the Hamming ball of equal volume. More precisely, we show that for all even n E N there exists an explicit bijection ψ: {0, 1} n → {x E {0, 1}n+1 : |x| > n/2} such that for every x ≠ y E {0, 1} n+1 it holds that 1/5 ≤ dist(ψ(x), ψ(y)) ≤ 4 5 - dist(x, y) where dist(·, ·) denotes the Hamming distance. In particular, this implies that the Hamming ball is bi-Lipschitz transitive. This result gives a strong negative answer to an open problem of Lovett and Viola [CC 2012], who raised the question in the context of sampling distributions in low-level complexity classes. The conceptual implication is that the problem of proving lower bounds in the context of sampling distributions requires ideas beyond the sensitivity-based structural results of Boppana [IPL 97]. We study the mapping ψ further and show that it (and its inverse) are computable in DLOGTIME-uniform TC°, but not in AC°. Moreover, we prove that ψ is “approximately local” in the sense that all but the last output bit of ψ are essentially determined by a single input bit.

关键词： Boolean functions Complexity theory partitioning algorithms Computer science Hamming distance Equations Context

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Team building by data clustering with constraints

Team building by data clustering with constraints

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International Conference on Computer Supported Cooperative Work in Design

作者： Chao-Lung Yang Maisyatus S Irfana Febriliyan Samopa Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Information System Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Making cooperative teams from multiple participants is a common exercise during the beginning of teamwork collaboration. Due to the priori knowledge among participants, several team building constraints can be formed. For example, it might be required to place participants from the same organization in the same group. In this research, we consider the cooperative team building as a constrained clustering problem. Clustering algorithms equipped with instance level must-link (ML) and cannon-link (CL) constraints are known to improve the efficiency and accuracy on handling data clustering. This paper presents the result of applying the constrained clustering algorithm to accommodate the complete must-link (CML) constraints, a special case of ML constraints, in which all participants should be pre-grouped with other members. The result shows the CML clustering can be used to group teams by pre-group requirements with promising computational performance.

关键词： Clustering algorithms Buildings Clustering methods Algorithm design and analysis partitioning algorithms Complexity theory Teamwork

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A New Scalable Parallel Algorithm for Fock Matrix Construction

A New Scalable Parallel Algorithm for Fock Matrix Constructi...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xing Liu Aftab Patel Edmond Chow School of Computational Science and Engineering College of Computing Georgia Institute of Technology Atlanta Georgia USA

ISBN: (纸本)9781479938018

Hartree-Fock (HF) or self-consistent field (SCF) calculations are widely used in quantum chemistry, and are the starting point for accurate electronic correlation methods. Existing algorithms and software, however, may fail to scale for large numbers of cores of a distributed machine, particularly in the simulation of moderately-sized molecules. In existing codes, HF calculations are divided into tasks. Fine-grained tasks are better for load balance, but coarse-grained tasks require less communication. In this paper, we present a new parallelization of HF calculations that addresses this trade-off: we use fine grained tasks to balance the computation among large numbers of cores, but we also use a scheme to assign tasks to processes to reduce communication. We specifically focus on the distributed construction of the Fock matrix arising in the HF algorithm, and describe the data access patterns in detail. For our test molecules, our implementation shows better scalability than NWChem for constructing the Fock matrix.

关键词： Heuristic algorithms Dynamic scheduling Equations partitioning algorithms Approximation algorithms Hafnium Approximation methods

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Reducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data

Reducing the Search Space for Big Data Mining for Interestin...

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IEEE International Conference on Services Economics (SE)

作者： Carson Kai-Sang Leung Richard Kyle MacKinnon Fan Jiang Department of Computer Science University of Manitoba Winnipeg MB Canada

Many existing data mining algorithms search interesting patterns from transactional databases of precise data. However, there are situations in which data are uncertain. Items in each transaction of these probabilistic databases of uncertain data are usually associated with existential probabilities, which express the likelihood of these items to be present in the transaction. When compared with mining from precise data, the search space for mining from uncertain data is much larger due to the presence of the existential probabilities. This problem is worsened as we are moving to the era of Big data. Furthermore, in many real-life applications, users may be interested in a tiny portion of this large search space for Big data mining. Without providing opportunities for users to express the interesting patterns to be mined, many existing data mining algorithms return numerous patterns -- out of which only some are interesting. In this paper, we propose an algorithm that (i) allows users to express their interest in terms of constraints and (ii) uses the MapReduce model to mine uncertain Big data for frequent patterns that satisfy the user-specified constraints. By exploiting properties of the constraints, our algorithm greatly reduces the search space for Big data mining of uncertain data, and returns only those patterns that are interesting to the users for Big data analytics.

关键词： Data mining Databases Big data Data models Computational modeling partitioning algorithms Program processors

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Fast intra partition algorithm for HEVC screen content coding

Fast intra partition algorithm for HEVC screen content codin...

引用

IEEE Visual Communications and Image Processing (VCIP)

作者： Mengmeng Zhang Yuhui Guo Huihui Bai College of Information Engineering North China University of Technology Beijing China Institute of Information Science Beijing Jiaotong University Beijing China

ISBN: (纸本)9781479961405

Since the publication of the High Efficiency Video Coding standard as the newest video coding standard, several extensions have been made. Among these, the use of the screen content coding in many fields is one of the important extensions. In terms of coding tree unit (CTU) partitioning, rate distortion optimization is still used in screen content coding. The complexity of the process has resulted in problems in relation to real-time application. Thus, this paper proposes a fast-deciding CTU partition mode algorithm based on entropy and coding bits. Experimental results show that the proposed algorithm can save 32% of encoding time on average compared with the default algorithm in HM-12.1+RExt-5.1 with only 0.8% bit rate increment in coding performance.

关键词： Encoding partitioning algorithms Entropy PSNR Algorithm design and analysis Signal processing algorithms Video sequences

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Overlapping community detection via link partition of asymmetric weighted graph

Overlapping community detection via link partition of asymme...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Wenju Zhang Naiyang Guan Xuhui Huang Zhigang Luo Jianwu Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 National University of Defense Technology Changsha Hunan CN Beijing Institute of Technology Beijing Beijing CN

Link partition clusters edges of a complex network to discover its overlapping communities. Due to Its effectiveness, link partition has attracted much attentions from the network science community. However, since link partition assigns each edge of a network to unique community, it cannot detect the disjoint communities. To overcome this deficiency, this paper proposes a link partition on asymmetric weighted graph (LPAWG) method for detecting overlapping communities. Particularly, LPAWG divides each edge into two parts to distinguish the roles of connected nodes. This strategy biases edges to a specific node and helps assigning each node to its affiliated community. Since LPAWG introduces more edges than those in the original network, it cannot efficiently detect communities from some networks with relative large amount of edges. We therefore aggregate the line graph of LPAWG to shrink its scale. Experimental results of community detection on both synthetic datasets and the realworld networks show the effectiveness of LPAWG comparing with the representative methods.

关键词： Communities Image edge detection partitioning algorithms Benchmark testing Acceleration Educational institutions Complex networks

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The design and implementation of approving system on private IaaS cloud platform

The design and implementation of approving system on private...

引用

International Conference on Information Technology and Electronic Commerce

作者： Yifan Tang Jun Zheng Computer Center East China Normal University Shanghai China

ISBN: (纸本)9781479953004

To make the allocation of hardware resources more reasonable and flexible on private infrastructure as a service cloud platform in enterprises, approving system described in this paper provides an efficient solution. Each user has a basic quota. If users need to get more resources, the reason must be given to apply. According to the performance of the eventually practice on OpenStack, this system can prevent users from allocating more hardware resources than they need, and save the hardware resources. We implemented the approving system on OpenStack, and the resource of server was saved.

关键词： Cloud computing Hardware Resource management Servers Virtual machining partitioning algorithms Computers

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Random projection based clustering for population genomics

Random projection based clustering for population genomics

引用

IEEE International Conference on Big Data

作者： Sotiris Tasoulis Lu Cheng Niko Välimäki Nicholas J. Croucher Simon R. Harris William P. Hanage Teemu Roos Jukka Corander Department of Computer Science University of Helsinki Finland Department of Information and Computer Science Aalto University Finland Department of Epidemiology Harvard School of Public Health Boston Massachusetts USA Department of Mathematics and Statistics University of Helsinki Finland

Recent data revolution in population genomics for bacteria has increased the size of aligned sequence data sets by two-to-three orders of magnitude. This trend is expected to continue in the near future, putting an emphasis on applicability of big data techniques to leverage biologically important insights. Moreover, with the increasing density of sampling, it may also be necessary to consider alignment-free sequence analysis techniques combined with clustering to yield a sufficient insight to data. This leads to ultra high-dimensional data with tens of millions of variables, which can no longer be handled by the existing population genomic methods. Using the largest bacterial sequence data sets published to date, we demonstrate that random projection based clustering provides a highly accurate and several orders of magnitude faster approach to the analysis of both alignment-based and alignment-free genome data sets, compared with the Bayesian model-based analysis that is currently considered as the state-of-the-art. Hence, clustering methods for big data harbor considerable potential for important applications in genomics and could pave way for novel analysis pipelines even in the online setting when executed in a massively parallel computing environment.

关键词： Clustering algorithms Genomics Bioinformatics partitioning algorithms Microorganisms Sociology Statistics

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MOSt-CB: SLA enforcement and smart VNE (Virtual network embedding) in a multi cloud providers environment

MOSt-CB: SLA enforcement and smart VNE (Virtual network embe...

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IEEE Globecom Workshops

作者： Mustapha Ait-Idir El Hadi Cherkaoui Elie Rachkidi Nada Chendeb Nazim Agoulmine COSMO University of Evry France OSMO IBISC Laboratory University of Evry France Lebanese University Faculty of Engineering and Azm Center for Researches Tripoli Lebanon

Cloud Computing has become in just few years a key consideration for businesses and governments willing to externalize their infrastructure, increase flexibility and reduce the cost of their infrastructure (CAPEX), maintenance and operation (OPEX). Although several commercial solutions are available, there are still numerous issues to solve. Two important aspects to address are the lock-in risk with one Cloud Provider (CP) and the fulfillment of the contractual SLA (Service Level Agreement). In this paper, we propose an integrated approach based on the concept of Broker that takes benefit from different CPs to efficiently embed a complex service into the various available cloud infrastructures. The Broker approach allows Cloud Customer (CC) to be independent from any particular CP while taking benefit from the overall available resources and avoid the lock-in with a particular one. In addition, we leverage the service model to provide Customers with the possibility to request different types of services (e.g. compound service) and propose a solution to fulfill requested Customer SLA in terms of availability, cost and geographic location according to the contracted SLA between the Broker and the CPs. We have implemented the solution in a lab cloud infrastructure and performed several tests and simulations to highlight the benefit of our approach.

关键词： Quality of service Substrates Clustering algorithms Cloud computing partitioning algorithms Bandwidth Conferences

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