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Integration ofWorkflow and Pipeline for Language Service Composition 9

Integration ofWorkflow and Pipeline for Language Service Com...

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9th international conference on Language Resources and Evaluation (LREC)

作者： Trang, Mai Xuan Murakami, Yohei Lin, Donghui Ishida, Toru Kyoto Univ Dept Social Informat Sakyo Ku Kyoto 6068501 Japan

ISBN: (纸本)9782951740884

Integrating language resources and language services is a critical part of building natural language processing applications. Service workflow and processing pipeline are two approaches for sharing and combining language resources. Workflow languages focus on expressive power of the languages to describe variety of workflow patterns to meet users' needs. Users can combine those language services in service workflows to meet their requirements. The workflows can be accessible in distributed manner and can be invoked independently of the platforms. However, workflow languages lack of pipelined execution support to improve performance of workflows. Whereas, the processing pipeline provides a straightforward way to create a sequence of linguistic processing to analyze large amounts of text data. It focuses on using pipelined execution and parallel execution to improve throughput of pipelines. However, the resulting pipelines are standalone applications, i.e., software tools that are accessible only via local machine and that can only be run with the processing pipeline platforms. In this paper we propose an integration framework of the two approaches so that each offests the disadvantages of the other. We then present a case study wherein two representative frameworks, the Language Grid and UIMA, are integrated.

关键词： Service Workflow processing pipeline Language Grid UIMA

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Big Data Analysis Using Hadoop Cluster 5

Big Data Analysis Using Hadoop Cluster

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5th IEEE international conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Saldhi, Ankita Goel, Abhinav Yadav, Dipesh Saldhi, Ankur Saksena, Dhruv Indu, S. Ctr Dev Telemat Mandi Rd Delhi 110030 India Aardee Solut Delhi 110059 India Designo Interior Delhi 110085 India Jamia Millia Islamia Dept Comp Engn Delhi 110025 India Carnegie Mellon Univ Pittsburgh PA 15213 USA Delhi Technol Univ Elect & Commun Engn Dept Delhi 110042 India

ISBN: (纸本)9781479939756

Industries keep a check on all statistics of their business and process this data using various data mining techniques to measure profit trends, revenue, growing markets and interesting opportunities to invest. These statistical records keep on increasing and increase very fast. Unfortunately, as the data grows it becomes a tedious task to process such a large data set and extract meaningful information. Also if the data generated is in various formats, its processing possesses new challenges. Owing to its size, big data is stored in Hadoop distributed File System (HDFS). In this standard architecture, all the DataNodes function parallel but functioning of a single Data Node is still in sequential fashion. This paper proposes to execute tasks assigned to a single Data Node in parallel instead of executing them sequentially. We propose to use a bunch of streaming multi-processors (SMs) for each single Data Node. An SM can have various processors and memory and all SMs run in parallel and independently. We process big data which may be coming from different sources in different formats to run parallelly on a Hadoop cluster, use the proposed technique and yield desired results efficiently. We have applied proposed methodology to the raw data of an industrial firm, for doing intelligent business, with a final objective of finding profit generated for the firm and its trends throughout a year. We have done analysis over a yearlong data as trends generally repeat after a year.

关键词： Big data Hadoop distributed data processing data mining Mappers Reducers

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Evaluating CP techniques to plan dynamic resource provisioning in distributed stream processing

Evaluating CP techniques to plan dynamic resource provisioni...

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11th international conference on the Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, CPAIOR 2014

作者： Reale, Andrea Bellavista, Paolo Corradi, Antonio Milano, Michela Department of Computer Science and Engineering - DISI Università di Bologna Italy

ISBN: (纸本)9783319070452

A growing number of applications require continuous processing of high-throughput data streams, e.g., financial analysis, network traffic monitoring, or big data analytics. Performing these analyses by using distributed Stream processing Systems (DSPSs) in large clusters is emerging as a promising solution to address the scalability challenges posed by these kind of scenarios. Yet, the high time-variability of stream characteristics makes it very inefficient to statically allocate the data-center resources needed to guarantee application Service Level Agreements (SLAs) and calls for original, dynamic, and adaptive resource allocation strategies. In this paper we analyze the problem of planning adaptive replication strategies for DSPS applications under the challenging assumption of minimal statistical knowledge of input characteristics. We investigate and evaluate how different CP techniques can be employed, and quantitatively show how different alternatives offer different trade-offs between problem solution time and stream processing runtime cost through experimental results over realistic testbeds. © 2014 Springer international Publishing.

关键词： Big data

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Real-time reconstruction solution for positron emission mammography imaging-guided intervention

Real-time reconstruction solution for positron emission mamm...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Xiaoyue Gu Lin Li Long Wei Mingkai Yun Wei Zhou Zhenrui Lu Leiming Shang Pengfei Ying Chinese Academy of Sciences Beijing Engineering Research Center of Radiographic Techniques and Equipment Beijing China

Positron Emission Mammography (PEM) imaging systems with the ability in detection of millimeter-sized tumors were developed in recent years. And some of them have been well used in clinical applications. In consideration of biopsy application, a double-plane PET is more practical than a ring detector for the convenience of breast immobilization. In this study, the possibility of the imaging-guided intervention under a double-plane PET was studied. The distance between the planes is changeable with different breast size. And the system matrix is computed in real-time for every plane spacing. The biopsy motivation under the double-plane PET requires a high sensitivity, great resolution performance and a quickly reconstruction speed. The sensitivity result shows the central value is 13.28% with the 6cm spacing and 200-750keV energy window. The resolution modeling method shows the resolution recovery achieved in the cross-plane. The 3D-OSEM algorithm is applied. The reconstruction is supported with GPU (GeForce GTX 660). For the GPU acceleration, both projection and back-projection procedures use the tube model. Each tube is distributed with a thread and calculated in parallel in the GPU. A double plane PET is simulated above Geant4 Application for Emission Tomography (GATE) software based on MC methods. With all the span tubes calculated, the GPU takes 19 seconds to reconstruct the image while the CPU takes 1037 seconds. The image quality was tested with different acquisition times. The series of biopsy images are showed. The point and rod source both could be imaged in the simulated biopsy process.

关键词： Graphics processing units Image reconstruction Positron emission tomography Biopsy Energy resolution Real-time systems Breast

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Efficient multidimensional AkNN query processing in the cloud

Efficient multidimensional AkNN query processing in the clou...

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25th international conference on Database and Expert Systems applications, DEXA 2014

作者： Nodarakis, Nikolaos Pitoura, Evaggelia Sioutas, Spyros Tsakalidis, Athanasios Tsoumakos, Dimitrios Tzimas, Giannis Computer Engineering and Informatics Department University of Patras 26500 Patras Greece Computer Science Department University of Ioannina Greece Department of Informatics Ionian University 49100 Corfu Greece Computer and Informatics Engineering Department Technological Educational Institute of Western Greece 26334 Patras Greece

ISBN: (纸本)9783319100722

A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. In this work, we propose a novel method for classifying multidimensional data using an AkNN algorithm in the MapReduce framework. Our approach exploits space decomposition techniques for processing the classification procedure in a parallel and distributed manner. To our knowledge, we are the first to study the kNN classification of multidimensional objects under this perspective. Through an extensive experimental evaluation we prove that our solution is efficient, robust and scalable in processing the given queries. © 2014 Springer international Publishing Switzerland.

关键词： MapReduce

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An adaptive parallel background model for high-throughput video applications and smart cameras embedding 14

An adaptive parallel background model for high-throughput vi...

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8th ACM/IEEE international conference on distributed Smart Cameras, ICDSC 2014

作者： Renò, Vito Marani, Roberto D'Orazio, Tiziana Stella, Ettore Nitti, Massimiliano National Research Council Institute of Intelligent Systems for Automation Via Amendola 122 D/O Bari70126 Italy

ISBN: (纸本)9781450329255

Background (BG) modelling is a key task in every computer vision system (CVS) independently of the final purpose for which it is designed. Even if many BG approaches exist (for example Mixture of Gaussians or Eigenbackground), they can not efficiently process real time videos due to the model complexity and to the high throughput of the video flux. One of the most challenging real time applications is the athletic scene processing, because, in this context, there are many critical aspects for defining a BG model: no a-priori knowledge of the static scene, sudden illumination changes and many moving objects that slow down the upgrade phase. The aim of this work is to provide an adaptive BG model able to deal with high frame rate videos (≥ 100 fps) in real time processing, and suitable for smart cameras embedding, finding a good compromise between the model complexity and its responsiveness. Real experiments demonstrate that this BG model approach shows great performances and robustness during the real time processing of athletic video frames, up to 100 fps. Copyright 2014 ACM.

关键词： Throughput

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Java Evolutionary Framework based on Genetic Programming

Java Evolutionary Framework based on Genetic Programming

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1st international conference on Signal processing and Integrated Networks (SPIN)

作者： Karasek, Jan Burget, Radim Dutta, Malay Kishore Singh, Anushikha Brno Univ Technol Fac Elect Engn & Commun CS-61090 Brno Czech Republic

ISBN: (纸本)9781479928668

Automatic optimization techniques, such as evolutionary algorithms, have become popular in the recent years as a general, simple, robust, and scalable solution which can be applied when other optimization method fails. Recently, many evolutionary and/or genetic based optimization frameworks and libraries have been developed and lot of them is freely available. On the other hand, there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms. This paper proposes a new grammar driven genetic programming based framework implemented in cross-platform Java programming language which allows to implement own code, modify existing, and analyze algorithms. The framework described in this paper addresses the problem of flexibility, modularity, multiplatformness, and presents a general architecture for evolutionary optimization based on genetic programming driven by context free grammar distributed under the LGPL license suitable for both scientific and business applications. In the paper is described a design of the framework, the motivation for development, and two use-cases.

关键词： Java context-free grammars genetic algorithms Java evolutionary framework LGPL license automatic optimization techniques business applications context free grammar cross-platform Java programming language evolutionary algorithms genetic based optimization frameworks genetic programming grammar driven genetic programming based frameworks scientific applications Algorithm design and analysis Genetic programming Grammar Java Optimization Production Signal processing algorithms

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Behavioral Patterns of Various JOIN techniques on Large Databases

Behavioral Patterns of Various JOIN Techniques on Large Data...

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international conference on Circuits, Systems, Communication and Information Technology applications (CSCITA)

作者： Mahajan, Sunita M. Jadhav, Vaishali P. Mumbai Educ Trust Dept Comp Sci Bombay Maharashtra India NMIMS Univ St Francis Inst Technol Res Scholar IT Dept Bombay Maharashtra India

ISBN: (纸本)9781479924943

This paper focused on various challenges occurred during processing of join operation in query optimization. Producing the query result in less execution time, reducing communication cost when data is distributed among different sites, preventing data loss during join operation, eliminating duplicate data during data transfer, reducing the amount of data to be transferred using data compression techniques - are some of the challenges occurred during join operation of the query. Paper suggests four join algorithms such as SEMI-join, BLOOM-join, PERF-join and DERF-join to address these problems. The behavior and performance of these algorithms is checked with four different types of queries such as Star, Clique, Circular and Chain query. Experimental results are carried out with benchmark queries to compare the performance of algorithms on large TPC-H database.

关键词： Query Optimization Semi Join Bloom Join PERF Join DERF Join TPC-H Database

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Communication techniques for Mobile Sensor Networks

Communication Techniques for Mobile Sensor Networks

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28th IEEE international conference on Advanced Information Networking and applications Workshops (IEEE WAINA)

作者： Kanzaki, Akimitsu Nishio, Shojiro Osaka Univ Dept Multimedia Engn Grad Sch Info Sci & Tech Suita Osaka Japan

ISBN: (纸本)9781479926527

In mobile sensor networks (MSNs), since sensor nodes and wireless networks are highly resource constrained, and it is highly required to manage sensor data in flexible and efficient manners. Under the MEXT research project(1) entitled "Studies on Efficient Data processing techniques for Mobile Sensor Networks," we have conducted researches on data management in MSNs. In this paper, we report some of our achievements in a sub-area of this project, which mainly addresses communication protocols for efficient data gathering in MSNs. In particular, we first show our achievements on how to efficiently gather sensor data using mobile sensor nodes in each of sparse and dense environments. We also show our another achievement on how to transmit sensor data assuming an environment where multiple mobile sinks exist.

关键词： mobile sensor networks communication protocols data gathering mobile sink

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A Framework for Tracking Memory Accesses in Scientific applications 43

A Framework for Tracking Memory Accesses in Scientific Appli...

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43rd international conference on parallel processing Workshops, ICPPW 2014

作者： Peña, Antonio J. Balaji, Pavan Mathematics and Computer Science Division Argonne National Laboratory ArgonneIL60439 United States

ISBN: (纸本)9781479956159

Profiling is of great assistance in understanding and optimizing applications' behavior. Today's profiling techniques help developers focus on the pieces of code leading to the highest penalties according to a given performance metric. In this paper we describe a pair of tools we have extended to complement the traditional algorithm-oriented analysis. Our extended tools provide new object-differentiated profiling capabilities that help software developers and hardware designers (1) understand access patterns, (2) identify unexpected access patterns, and (3) determine whether a particular memory object is consistently featuring a troublesome access pattern. Memory objects found in this way may have gone unnoticed with the traditional profiling approach. This additional view may lead developers to think of different ways of storing data, leveraging different algorithms, or employing different memory subsystems in future heterogeneous memory systems. © 2014 IEEE.

关键词： Heterogeneous memory Profiling Valgrind

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