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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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international Workshop on Mobile Networks for Biometric Data Analysis, 2014

International Workshop on Mobile Networks for Biometric Data...

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international Workshop on Mobile Networks for Biometric Data Analysis, 2014

ISBN: (纸本)9783319396989

The proceedings contain 25 papers. The special focus in this conference is on ICT for Health Care, Embedded Systems for Biometric Data Acquisition, Biometric Data in Automotive, Home applications, Biometric Data Analysis of Cardiac Signals and Gait Analysis. The topics include: The role of ICT in the Italian health care system;a non-intrusive model for the collection and analysis of data, feedback and needs;analyzing environmental conditions and vital signs to increase healthy living;preliminary study of a novel shelving system for nutrition habits measuring;a rule-based assistant system for managing the clothing cycle;state of charge monitor for wireless sensor networks;development of an algorithm and a sensor to monitor the heart rate by volumetric measurement techniques;piezoelectric energy harvesting on running shoes;implementing steganocryptography in scanner and angio-scanner medical images;stress-aware generation of recommendations in a driving system to increase user acceptance;stress map based information system for increasing road safety;an openCV based android application for drowsiness detection on mobile devices;distributed speech and speaker identification system for personalized domotic control;surface EMG fatigue analysis by means of homomorphic deconvolution;high performance dynamic threshold calibration for RR interval detection in a QRS complex using a parallel programing;an analog front-end for combined EMG/ECG wireless sensors;health monitoring in sport through wearable sensors;a new segmented-beat modulation algorithm for maternal ECG estimation from abdominal recordings and fall detection using wearable accelerometers and Smartphone.

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Performance-vetted 3-D MAC Processors for parallel volumetric convolution algorithm: A 256×256×20 MRI Filtering case study

Performance-vetted 3-D MAC Processors for Parallel volumetri...

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Al-Sadeq international conference on Multidisciplinary in IT and Communication Science and applications

作者： Sami Hasan System Engineering Department College of Information Engineering Al-Nahrain University Baghdad Iraq

ISBN: (纸本)9781509032488

3-D raw data collections introduce noise and artifacts that need to be recovered from degradation by an automated filtering system before further machine analysis. Serving this goal, five performance-efficient FPGA-prototyped processors are devised to realize parallel 3-D "filtering algorithm". These parallel processors tackle the major bottlenecks and limitations of existing multiprocessor systems in input volumetric data, processing word-length, output boundary conditions and inter-processor communications. Then, greyscale 256×256×20 MRI case study are efficiently filtered and improved by a class of common convolution operators and their developed ones respectively. Analytically, the performance of the five implemented processors are evaluated in term of area, speed, dynamic power, and throughput. All five processors efficiently perform in high real-time throughput up to (114 VPS), lowest power consumption of down to (64 mW) at maximum operating frequency. The devised processors can be embedded in mobile MRI or fMRI scanner and as a pre-filtering stage in any portable automated fMRI systems.

关键词： 3-D MRI Neuroscience FPGA parallel Algorithms Architectures Power Throughput Filtering

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Quality based clustering using MapReduce framework

Quality based clustering using MapReduce framework

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Online international conference on Green Engineering and Technologies (IC-GET)

作者： R. Gowri R. Rathipriya Periyar University Salem Tamil Nadu IN

ISBN: (纸本)9781509045570

The problem of data deluge is prevailing everywhere. Analyzing voluminous and variety of data is a great challenge to the researchers. The MapReduce framework is adapted to many computational methodologies to overcome these issues. Clustering is one of the most commonly used data mining techniques in various pattern analysis applications. This paper is mainly focuses on quality based data clustering using MapReduce framework for fast processing. In order to satisfy the many pattern analysis research applications.

关键词： Indexes Mathematical model Data mining Correlation parallel processing Big Data Couplings

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GraphCEP: real-time data analytics using parallel complex event and graph processing 16

GraphCEP: real-time data analytics using parallel complex ev...

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Proceedings of the 10th ACM international conference on distributed and Event-based Systems

作者： Ruben Mayer Christian Mayer Muhammad Adnan Tariq Kurt Rothermel IPVS University of Stuttgart Germany

ISBN: (纸本)9781450340212

In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in areas such as disaster detection, business analytics or health-care. parallel Complex Event processing has evolved as the paradigm of choice to analyze data streams in a timely manner, where the incoming data streams are split and processed independently by parallel operator instances. However, the degree of parallelism is limited by the feasibility of splitting the data streams into independent parts such that correctness of event processing is still ensured. In this paper, we overcome this limitation for graph-structured data by further parallelizing individual operator instances using modern graph processing systems. These systems partition the graph data and execute graph algorithms in a highly parallel fashion, for instance using cloud resources. To this end, we propose a novel graph-based Complex Event processing system GraphCEP and evaluate its performance in the setting of two case studies from the DEBS Grand Challenge 2016.

关键词： complex event processing distributed graph processing

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CPU Frequency Tuning to Improve Energy Efficiency of MapReduce Systems

CPU Frequency Tuning to Improve Energy Efficiency of MapRedu...

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international conference on parallel and distributed Systems (ICPADS)

作者： Nidhi Tiwari Umesh Bellur Santonu Sarkar Maria Indrawan Infosys Ltd. India IITB-Monash Research Academy India Indian Institute of Technology Bombay India BITS Pilani Goa India

ISBN: (纸本)9781509053827

Energy efficiency is a major concern in today's data centers that house large scale distributed processing systems such as data parallel MapReduce clusters. Modern power aware systems utilize the dynamic voltage and frequency scaling mechanism available in processors to manage the energy consumption. In this paper, we initially characterize the energy efficiency of MapReduce jobs with respect to built-in power governors. Our analysis indicates that while a built-in power governor provides the best energy efficiency for a job that is CPU as well as IO intensive, a common CPU-frequency across the cluster provides best the energy efficiency for other types of jobs. In order to identify this optimal frequency setting, we derive energy and performance models for MapReduce jobs on a HPC cluster and validate these models experimentally on different platforms. We demonstrate how these models can be used to improve energy efficiency of the machine learning MapReduce applications running on the Yarn platform. The execution of jobs at their optimal frequencies improves the energy efficiency by average 25% over the default governor setting. In case of mixed workloads, the energy efficiency improves by up to 10% when we use an optimal CPU-frequency across the cluster.

关键词： Energy consumption Data models Predictive models Electronic mail distributed databases Tuning Program processors

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Exploring Controlled RDF Distribution

Exploring Controlled RDF Distribution

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IEEE international conference on Cloud Computing Technology and Science (CloudCom)

作者： Raqueline R.M. Penteado Rebeca Scroeder Carmem S. Hara Universidade Estadual de Maringá Maringá PR Brazil Universidade do Estado de Santa Catarina Florianopolis Santa Catarina BR Universidade Federal do Paraná Curitiba PR Brazil

ISBN: (纸本)9781509014460

RDF datasets have increased rapidly over the last few years. In order to process SPARQL queries on these large datasets, much effort has been spent on developing horizontally scalable techniques, which involve data partitioning and parallel query processing. While distribution may provide storage scalability, it may also incur high communication costs for processing queries. In this paper, we present a parallel and distributed query rocessing approach that explores the existence of data allocation patterns, provided by a controlled data distribution, that determine how RDF triples should be grouped and stored on the same server. Fragments of the RDF datastore follow a given allocation pattern and correspond also to units of communication among servers. Based on this distribution model, we define two communication strategies for query processing: get-frag, which requests remote servers to send fragments that contain data required by a query, and send-result, which forwards intermediate results. These strategies are combined on a method, called 2ways, that chooses the adequate communication strategy whenever queries traverse fragment boundaries. We provide a cost function used to determine this choice and present experimental results. They show that our proposed technique effectively reduces the communication cost and improves the response time for processing SPARQL queries on a distributed RDF datastore.

关键词： Servers Resource description framework Query processing distributed databases Resource management Data models Adaptation models

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Predictive Spatio-Temporal Query Processor on Resilient distributed Datasets

Predictive Spatio-Temporal Query Processor on Resilient Dist...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Vijay Akkineni Berkay Aydin Sajitha Naduvil-Vadukootu Rafal Angryk Department of Computer Science Georgia State University Atlanta Georgia

ISBN: (纸本)9781509039371

Moving object prediction and indexing have been a well studied area of research and include applications in environment monitoring, traffic prediction, advertising, and efficient routing. Spark is a cluster computing framework, which utilizes Resilient distributed Datasets (RDD) on a cluster of several commodity machines. Spark is popularly used for parallel processing of massive datasets. The modeling of cloud-based and distributed predictive spatio-temporal query processing framework for large-scale data is an interesting problem that has many practical applications. We propose a data-driven framework for moving region prediction using linear regression and distributed spatio-temporal query processing on RDDs. Our framework is designed to scale well to large-scale datasets, and process the predictive kNN and range queries with interactive query response times. Our experimental evaluation offer insight into properties of our framework and indicate that it fulfills its design goals, and scalable query processing for big spatiotemporal data.

关键词： Spatiotemporal phenomena Indexing Query processing distributed databases Trajectory Data models Sparks

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Data Dissemination and parallel processing techniques Research Based on Massively parallel processing

Data Dissemination and Parallel Processing Techniques Resear...

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2016 international conference on Wireless Communication and Network Engineering (WCNE2016)

作者： Qiao SUN Bu-qiao DENG Xiao-bo NIE Hui-yuan MA Jia-song SUN Beijing Guodiantong Network Technology Co. Ltd. State Grid Beijing Electric Power Company Electronic Engineering Department Tsinghua University

In the current distributed database system architecture enterprise-class, the massively parallel processing architecture is used frequently. This method can be used to carry out large-scale analysis of data through distributed across multiple nodes and storage and query process, from its scope of application produce simple reports to perform complex analytics workloads. However, due to the characteristics of shared-nothing MPP technology, to carry out large-scale data analysis query and maintain data consistency there are some difficulties. In this paper, a relational SQL-based query parsing distributed MPP data distribution and parallel processing technology, the goal is to maintain and improve the consistency of distributed data query speed. First SQL query analysis section, according to the syntax analysis, semantic analysis and sentence parsing steps such order;in the form of work distribution node/data node in the data distribution phase, all tasks emanating from the work of a distribution node, all need to treated results are returned to the node;when parallel processing, each node needs to store a copy of the lookup table, and on each node concurrent execution of SQL statements for each query. Experimental results show that the proposed MPP data distribution and parallel processing scheme can support large volume of data processing, ensuring data consistency in the premise of improving query processing speed.

关键词： distributed database Massively parallel processing Data distribution Data consistency

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parallel implementation and performance evaluation of facial recognition algorithms using open source technologies

Parallel implementation and performance evaluation of facial...

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international conference on Electrical, Electronics, and Optimization techniques (ICEEOT)

作者： J. Suryaprasad D. S. Sandesh I. Priyanka G. N. Pravalika Aman Kumar PES Institute of Technology Bangalore India

ISBN: (纸本)9781467399401

Critical to any automated surveillance system is the recognition component. Since the inception of Pattern Recognition & Image processing, researchers across the globe continue to propose newer facial recognition algorithms. Each of these facial algorithms has its own pros and cons. A typical facial recognition algorithm consists of three steps namely: facial detection, feature extraction and recognition. In this paper we discuss serial and parallel implementation of four standard facial recognition algorithms namely, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) and FISHER Face. Work presented in this paper consists of two sections. In section 1, we discuss serial and parallel implementation of the algorithms. Section 2, presents a comparative performance analysis of the above 4 algorithms. A major challenge in this effort is the ability to explore and exploit concurrency in existing facial recognition algorithms to improve performance. Based on the algorithms performance, we have made few suggestions regarding the suitability of each of these algorithms for different domains/applications. All algorithms are implemented using open source OPENCV and OPENMP libraries.

关键词： Face recognition Principal component analysis Algorithm design and analysis Face Signal processing algorithms Training parallel processing

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Topology and Affinity Aware Hierarchical and distributed Load-Balancing in Charm++

Topology and Affinity Aware Hierarchical and Distributed Loa...

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international Workshop on Communication Optimizations in HPC (COMHPC)

作者： Emmanuel Jeannot Guillaume Mercier François Tessier Inria Bordeaux Sud-Ouest Talence France Bordeaux INP Talence France

The evolution of massively parallel supercomputers make palpable two issues in particular: the load imbalance and the poor management of data locality in applications. Thus, with the increase of the number of cores and the drastic decrease of amount of memory per core, the large performance needs imply to particularly take care of the load-balancing and as much as possible of the locality of data. One mean to take into account this locality issue relies on the placement of the processing entities and load balancing techniques are relevant in order to improve application performance. With large-scale platforms in mind, we developed a hierarchical and distributed algorithm which aim is to perform a topology-aware load balancing tailored for Charm++ applications. This algorithm is based on both LibTopoMap for the network awareness aspects and on TREEMATCH to determine a relevant placement of the processing entities. We show that the proposed algorithm improves the overall execution time in both the cases of real applications and a synthetic benchmark as well. For this last experiment, we show a scalability up to one millions processing entities.

关键词： Topology Load management Network topology Runtime Optimization Central processing Unit conferences

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