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A NoSQL Data Model for Scalable Big Data Workflow Execution

A NoSQL Data Model for Scalable Big Data Workflow Execution

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IEEE international conference on Services Economics (SE)

作者： Aravind Mohan Mahdi Ebrahimi Shiyong Lu Alexander Kotov Wayne State University Detroit MI USA

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.

关键词： Data models Big data Virtual machining Cloud computing parallel processing Databases

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Repairing Functional Dependency Violations in distributed Data 20th

Repairing Functional Dependency Violations in Distributed Da...

引用

20th international conference on Database Systems for Advanced applications (DASFAA)

作者： Chen, Qing Tan, Zijing He, Chu Sha, Chaofeng Wang, Wei Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9783319181202;9783319181196

One of the problems central to data consistency is data repairing. Given a database D violating a set Sigma of data dependencies as data quality rules, it aims to modify D for a new relation D' satisfying Sigma. When D is a centralized database, a host of methods have been provided to address this problem. In practice, a database may be fragmented and distributed to multiple sites, which is advocated by distributed systems for better scalability and is readily supported by commercial systems. This paper makes a first effort to develop techniques for repairing functional dependency violations in a horizontally partitioned database. (1) Based on a message-passing distributed computing model and two complexity measures (parallel time and data shipment) for distributed algorithms, we study data repairing with equivalence classes in the distributed setting. We show that it is NP-complete to build equivalence classes when the data is horizontally partitioned, and when we aim to minimize either data shipment or parallel computation time. (2) Despite the intractability, we propose efficient distributed algorithms and optimization techniques for data repairing based on equivalence classes. (3) We experimentally verify the effectiveness and efficiency of our algorithms, using both real-life and synthetic data.

关键词： Message passing

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BAYESIAN SPARSE REGULARIZED RECONSTRUCTION IN parallel MRI WITH SENSITIVITY MATRIX IMPRECISION

BAYESIAN SPARSE REGULARIZED RECONSTRUCTION IN PARALLEL MRI W...

引用

international conference on Advances in Biomedical Engineering (ICABME)

作者： Chaari, Lotfi Univ Toulouse IRIT INP ENSEEIHT Toulouse France MIRACL Lab Sfax Tunisia

ISBN: (纸本)9781467365161

parallel MRI is a fast imaging technique that allows reconstruction of full Field-of-View (FoV) images based on under-sampled k-space data acquired using multiple receiver coils with complementary sensitivity profiles. It enables the acquisition of highly resolved images either in space or time, which is of particular interest in applications like functional neuroimaging. These features are counterbalanced by a degraded SNR and the presence of artifacts that depend on the reconstruction algorithm. These artifacts are mainly caused by acquisition noise and imprecisions in the sensitivity matrices, which are a priori estimated. In this paper, we present a novel method for parallel MRI Bayesian regularized reconstruction while accounting for sensitivity maps imperfections and correcting them. The proposed method is validated on realistic simulated data and results show the outperformance of our method even compared to regularized techniques.

关键词： Bayes methods biomedical MRI image reconstruction matrix algebra medical image processing parallel processing Bayesian sparse regularized reconstruction coil sensitivity profiles degraded SNR fast imaging technique full FoV image reconstruction functional neuroimaging highly spatially resolved images highly temporally resolved images imaging artifacts multiple receiver coils parallel MRI Bayesian regularized reconstruction reconstruction algorithm sensitivity map imperfections sensitivity matrix imprecision undersampled k-space data Coils Estimation Image reconstruction Magnetic resonance imaging Sensitivity Sparse matrices Bayes methods image reconstruction Reconstruction algorithm biomedical MRI Functional Neuroimaging Sparse matrix Medical image processing Magnetic Resonance Imaging Coils matrix algebra parallel processing (COMPUTERS) Multiple Receiver Imaging

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Accelerated parallel Training of Logistic Regression using OpenCL 25

Accelerated Parallel Training of Logistic Regression using O...

引用

25th international conference on Computer Theory and applications, ICCTA 2015

作者： Zahera, Hamada M. El-Sisi, Ashraf B. Menofia University Faculty of Computers and Information Shebin El Kom Egypt

ISBN: (纸本)9781509002177

This paper presents an accelerated approach for training logistic regression in parallel and running on Graphics processing Units (GPU). Many researchers have worked out in boosting the performance of logistic regression using different techniques. Our study focuses on showing the ultimate capabilities of GPU processing and OpenCL framework. GPU and OpenCL are the low cost and high-performance solutions for scaling up logistic regression to handle large datasets. The proposed approach is implemented in OpenCL C/C++ and tested with different datasets. All results showed a significant improvement in execution time in large datasets, which is reduced almost with the available GPU devices. © 2015 IEEE.

关键词： Graphics processing unit

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Proceedings of the international conference on parallel processing Workshops

Proceedings of the International Conference on Parallel Proc...

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43rd international conference on parallel processing Workshops, ICPPW 2014

ISBN: (纸本)9781479956159

The proceedings contain 54 papers. The topics discussed include: Perf-Sat: runtime detection of performance saturation for GPGPU applications;scalability analysis and evaluation of divisible load scheduling;slowest server emulation syndrome: a hidden cost of load imbalance;consistency and fault tolerance considerations for the next iteration of the DOE fast forward storage and io project;using an object-based active storage framework to improve parallel storage systems;energy-aware workflow scheduling using frequency scaling;PMC-based power modelling with workload classification on multicore systems;performance and energy-aware characterization of the sparse matrix-vector multiplication on multithreaded architectures;flow graph designer: a tool for designing and analyzing Intel threading building blocks flow graphs;overhead analysis of performance counter measurements;and CASITA: a tool for identifying critical optimization targets in distributed heterogeneous applications.

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A horizontally-scalable multiprocessing platform based on *** 14

A horizontally-scalable multiprocessing platform based on **...

引用

13th IEEE international Symposium on parallel and distributed processing with applications

作者： Maatouki, Ahmad Meyer, Joerg Szuba, Marek Streit, Achim Karlsruhe Inst Technol Steinbuch Ctr Comp Eggenstein Leopoldshafen Germany

ISBN: (纸本)9781467379526

This paper presents a scalable web-based platform called Node Scala which allows to split and handle requests on a parallel distributed system according to pre-defined use cases. We applied this platform to a client application that visualizes climate data stored in a NoSQL database MongoDB. The design of Node Scala leads to efficient usage of available computing resources in addition to allowing the system to scale simply by adding new workers. Performance evaluation of Node Scala demonstrated a gain of up to 74 % compared to the state-of-the-art techniques.

关键词： Big Data Cloud Node.js Scalability multiprocessing parallel processing (COMPUTERS) big data Scalability Client Application Platform Clouds Climatic data

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Network Coding for Effective NDN Content Delivery: Models, Experiments, and applications 44

Network Coding for Effective NDN Content Delivery: Models, E...

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44th Annual international conference on parallel processing Workshops (ICPPW)

作者： Lei, Kai Zhu, Fangxing Peng, Cheng Xu, Kuai Peking Univ Sch Elect & Comp Engn Inst Big Data Technol Shenzhen Key Lab Cloud Comp Technol & Applicat Beijing Peoples R China Arizona State Univ Sch Math & Nat Sci Tempe AZ 85287 USA

ISBN: (纸本)9781467375887

How to effectively distribute and share increasingly large volumes of data in large-scale network applications is a key challenge for Internet infrastructure. Although NDN, a promising new future internet architecture which takes data-oriented transfer approaches, aims to better solve such needs than IP, it still faces problems like data redundancy transmission and inefficient in-network cache utilization. This paper combines network coding techniques to NDN to improve network throughput and efficiency. The merit of our design is that it is able to avoid duplicate and unproductive data delivery while transferring disjoint data segments along multiple paths and with no excess modification to NDN fundamentals. To quantify performance benefits of applying network coding in NDN, we integrate network coding into an NDN streaming media system implemented in the ndnSIM simulator. Basing on BRITE generated network topologies in our simulation, the experimental results clearly and fairly demonstrate that considering network coding in NDN can significantly improve the performance, reliability and QoS. More importantly, our approach is capable of and well fit for delivering growing Big Data applications including high-performance and high-density video streaming services.

关键词： Encoding Internet Network coding Network topology Streaming media Throughput Topology Multi-path Forwarding Named Data Networking (NDN) Network Coding streaming media Streaming Video Network coding NDN gene Network topology Throughput Internets topology

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GPU Accelerated Background Subtraction 16

GPU Accelerated Background Subtraction

引用

IEEE 16th international conference on Communication Technology (ICCT)

作者： Liu, Dan Sci & Technol Commun Secur Lab Chengdu Peoples R China

ISBN: (纸本)9781467370059

Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of computation efficiency. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. This paper describes ViBe algorithm for background subtraction and presents implementation of it using the CUDA technology for parallel processing. The experiments indicate that our implementation outperforms the majority of earlier state-of-the-art algorithms not only in terms of accuracy, but also in terms of processing speed.

关键词： Background subtraction CUDA parallel computing Computer vision GPU

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Enhancing and Evaluating the Configuration Capability of a Skeleton for Irregular Computations 23

Enhancing and Evaluating the Configuration Capability of a S...

引用

23rd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Gonzalez, Carlos H. Fraguela, Basilio B. Univ A Coruna Dept Elect & Sistemas La Coruna Spain

ISBN: (纸本)9781479984909

Although skeletons largely facilitate the parallelization of algorithms, they often provide little support for the work decomposition. Also, while they have been widely applied to regular computations, this has not been case for irregular algorithms that can exploit amorphous data-parallelism, whose parallelization in fact requires much more effort from programmers and thus benefits more from a structured approach. In this paper we improve and evaluate the configurability of a recently proposed skeleton that allows to parallelize this latter kind of algorithms. Namely, the skeleton allows to easily change critical details such as the data structures, the work partitioning algorithm or the task granularity to use. The simple procedures to choose among these possibilities and their influence on performance are described and evaluated. We conclude that the skeleton allows to conveniently explore different possibilities for the parallelization of irregular applications, which can result in substantial performance improvements.

关键词： parallel skeletons amorphous parallelism libraries

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GRAPH/Z: A Key-Value Store Based Scalable Graph processing System

GRAPH/Z: A Key-Value Store Based Scalable Graph Processing S...

引用

IEEE international conference on Cluster Computing (CLUSTER)

作者： Li, Tonglin Ma, Chaoqi Li, Jiabao Zhou, Xiaobing Wang, Ke Zhao, Dongfang Sadooghi, Iman Raicu, Ioan IIT Chicago IL 60616 USA Argonne Natl Lab Argonne IL 60439 USA Hortonworks Santa Clara CA USA

ISBN: (纸本)9781467365987

The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.

关键词： Big data Graph database Graph processing Key-value store NoSQL

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