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distributed and Scalable Platform for Collaborative Analysis of Massive Time Series Data Sets 8

Distributed and Scalable Platform for Collaborative Analysis...

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8th international conference on Data Science, Technology and applications (DATA)

作者： Duarte, Eduardo Gomes, Diogo Campos, David Aguiar, Rui L. Univ Aveiro Dept Elect Telecommun & Informat Aveiro Portugal Univ Aveiro Inst Telecommun Aveiro Portugal Bosch Thermotechnol P-3800533 Aveiro Portugal

ISBN: (纸本)9789897583773

the recent expansion of metrification on a daily basis has led to the production of massive quantities of data, which in many cases correspond to time series. To streamline the discovery and sharing of meaningful information within time series, a multitude of analysis software tools were developed. However, these tools lack appropriate mechanisms to handle massive time series data sets and large quantities of simultaneous requests, as well as suitable visual representations for annotated data. We propose a distributed, scalable, secure and high-performant architecture that allows a group of researchers to curate a mutual knowledge base deployed over a network and to annotate patterns while preventing data loss from overlapping contributions or unsanctioned changes. Analysts can share annotation projects with peers over a reactive web interface with a customizable workspace. Annotations can express meaning not only over a segment of time but also over a subset of the series that coexist in the same segment. In order to reduce visual clutter and improve readability, we propose a novel visual encoding where annotations are rendered as arcs traced only over the affected curves. the performance of the prototype under different architectural approaches was benchmarked.

关键词： Time Series Annotations Annotation Systems Collaborative Software Data Analysis Information Science Data Modeling Knowledge Management Database Management Systems distributed Systems Information Visualization

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Unifying Data and Replica Placement for Data-intensive Services in Geographically distributed Clouds 9

Unifying Data and Replica Placement for Data-intensive Servi...

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9th international conference on Cloud computing and Services Science (CLOSER)

作者： Atrey, Ankita Van Seghbroeck, Gregory Mora, Higinio De Turck, Filip Volckaert, Bruno Univ Ghent IDLAB Imec Technol Pk Ghent Belgium Univ Alicante Alicante Spain

ISBN: (纸本)9789897583650

the increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. the objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques.

关键词： Data Placement Replica Placement Geographically distributed Clouds Location-Based Services Online Social Networks Scalability Overlapping Clustering

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HIGHLY parallel MULTI-PHYSICS SIMULATION OF MUSCULAR ACTIVATION AND EMG 8

HIGHLY PARALLEL MULTI-PHYSICS SIMULATION OF MUSCULAR ACTIVAT...

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8th international conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED)

作者： Maier, B. Emamy, N. Kraemer, A. Mehl, M. Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 D-70569 Stuttgart Germany Univ Stuttgart Inst Appl Anal & Numer Simulat Allmandring 5b D-70569 Stuttgart Germany

ISBN: (纸本)9788494919459

Simulation of skeletal muscle activation can help to interpret electromyographic measurements and infer the behavior of the muscle fibers. Existing models consider simplified geometries or a low number of muscle fibers to reduce the computation time. We demonstrate how to simulate a finely-resolved model of biceps brachii with a typical number of 270.000 fibers. We have used domain decomposition to run simulations on 27.000 cores of the supercomputer HazelHen at HLRS in Stuttgart, Germany. We present details on opendihu, our software framework. Its configurability, efficient data structures and modular software architecture target usability, performance and extensibility for future models. We present good parallel weak scaling of the simulations.

关键词： Musculo-skeletal High Performance computing Multi-scale

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Hardware Implementation of A Synthetic Inertia System for Grid Stability 8

Hardware Implementation of A Synthetic Inertia System for Gr...

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international conference on Renewable Energy Research and applications

作者： Fregelius, Martin Lundin, Urban Uppsala Univ Div Elect Dept Engn Sci Box 534 SE-75121 Uppsala Sweden

ISBN: (纸本)9781728135878

the frequency in the electrical grid is, on the short time-scale, stabilized by the total rotational mass given predominantly by synchronously connected devices. these devices include the generators themselves and, on slightly longer timescales, the control system acting on turbine governors. As the inverter-connected technologies increase their share of total power, the total inertia in the grid is significantly reduced. One way to stabilize the frequency and reduce low frequency oscillations is to add synthetic inertia provided by a small energy, high power, storage devices combined with a fast control system. this paper describes a possible hardware topology for linear synthetic inertia. It uses an inverter coupled to a local energy storage unit comprised of supercapacitors. the paper presents the implementation and some selected experimental results. the system response is fast enough on a small test grid to act as inertia.

关键词： Synthetic inertia three phase inverter Super capacitors Field programmable gate array distributed generation Grid tied energy storage

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Quest Method for Organizing Cloud Processing of Airborne Laser Scanning Data 8

Quest Method for Organizing Cloud Processing of Airborne Las...

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8th IEEE Int Conf on Advanced Optoelectron and Lasers (CAOL) / Sci Workshop on Data Sci in Modern Optoelectronics and Laser Engineering (DSMOLE) / 16th Sci Workshop on Measurement Uncertainty - Scientific, Normative, Appl and Methodical Aspects (UM)

作者： Vitalii, Tkachov Mykhailo, Hunko Kharkiv Natl Univ Radio Elect Elect Comp Dept Kharkiv Ukraine

ISBN: (纸本)9781728118147

Nowadays, the processing of data obtained as a result of airborne laser scanning by a group of unmanned aerial vehicles includes up to eight different stages. From the point of view of organizing such processing, it is reasonable to use modern cloud technologies. the paper proposes a new method for processing this type of data based on the social model of completing game quests. Portions of data arrive at a computing system consisting of several clouds where, in turn, multifunctional SaaS nodes operate. Each of the nodes can process one data portion once, which allows to exclude the principle of exclusive use of one node for all stages of processing a data portion. the paper proposes an original algorithm for determining the optimal node for the next stage of processing, which takes into account: the time of data portions movement between SaaS nodes, in fact, processing and waiting in the node queue. Due to carried out experiments, it was found that the implementation of the developed quest method allows to reduce the workload of SaaS nodes by the uniform distribution of processing between all nodes, which was to an average of 30% Additionally, in the case when the number of processing nodes is less than three, the efficiency of the method decreases to zero. this paper suggests that the ques method can also be used on board of the nodes of a flying sensor network during airborne laser scanning of the area in rapidresponse systems.

关键词： cloud computing laser radar distributed processing routing mobile ad hoc networks

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New parallel algorithms for all pairwise computation on large HPC clusters 20

New parallel algorithms for all pairwise computation on larg...

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20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Tang, Tao Wu, Hao Bao, Wei Yang, Pengyi Yuan, Dong Zhou, Bing Bing Faculty of Engineering and IT University of Technology Sydney Sydney Australia School of Computer Science University of Sydney Sydney Australia School of Mathematics and Statistics University of Sydney Sydney Australia

ISBN: (纸本)9781728126166

All pairwise computation is defined as performing computation between every pair of the elements in a given dataset. It is often a necessary first step in a number of bioinformatics applications. Many of such applications require multiple terabytes of main memory and take multiple peta floating point operations to complete the computation. therefore, large HPC clusters are needed to tackle these large-scale computational problems. Conventionally designed parallel algorithms using data partitioning may have a scalability issue, i.e., for a given problem of fixed size the efficiency may decrease if the number of compute nodes is increased (Amdahl's law). In this paper we introduce a new method for parallel algorithm design. Using this method we first design an efficient one-dimensional (1D) ring algorithm and then a two-dimensional (2D) algorithm based on the 1D ring for all pairwise computation. When increasing the compute nodes, instead of reducing the block size, we make multiple copies of the original data blocks in the 1D ring and distribute them across the added compute nodes in the other dimension. By properly organizing the compute nodes the communication overhead can be reduced to a minimum in this two-dimensional setting. Experiments on a Cray XC40 HPC supercomputer show that our new algorithms are very efficient and scalable for large-scale all pairwise computation on large HPC clusters. © 2019 IEEE.

关键词： parallel algorithms

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Research on parallel computing Method of Hydrological Data Cube 1

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8th international Congress on Big Data (BigData) held as Part of the Services conference Federation (SCF)

作者： Wang, Rong Wan, Dingsheng Zhang, Zixuan Zhao, Qun Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Bank Jiangsu Nanjing 211100 Peoples R China

ISBN: (数字)9783030235512

ISBN: (纸本)9783030235512;9783030235505

Hydrological data is characterized by large data volume and high dimension. In view of non-hierarchical dimension data in hydrological data, this paper proposed Segmented parallel Dwarf Cube (SPD-Cube). Firstly, a high-dimensional cube is divided into several low-dimensional segments. then by calculating the sub-dwarf of each low-dimensional segment, the storage space required for cube materialization is greatly reduced. Finally, combining Map/Reduce technology to construct, store and query several sub-dwarfs in parallel. Experiments show that SPD-Cube not only realizes highly compressed storage of data, but also realizes efficient query by combining Map/Reduce distributed parallel architecture.

关键词： Hydrological data Dwarf cube Map/Reduce

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Integration of ParaSCIP solvers running on several clusters on the base of Everest cloud platform 8

Integration of ParaSCIP solvers running on several clusters ...

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8th international Young Scientists conference on Computational Science (YSC)

作者： Smirnov, Sergey Voloshinov, Vladimir Russian Acad Sci Inst Informat Transmiss Problems Bolshoy Karemy Per 19-1 Moscow 127051 Russia

Software integration of optimization problems' solvers leveraging power of heterogeneous computing environments is a great challenge of last decades. Last several years we have been developing coarse-grained parallelization approaches to speed up Branch-and-Bound (BnB) algorithm for discrete and global optimization problems by exchange of BnB-incumbents in a heterogeneous environment containing standalone servers and clusters via Everest software toolkit, http //***. this approach have been implemented as DDBNB Everest-application (Domain Decomposition BnB), https://***/distcomp/ddbnb. the current implementation is based on two solvers (and their open API to get/put incumbents): SCIP, https://*** and CBC, https ://***/coin-or/Cbc. Recently we began to use ParaSCIP solver, https : //ug. zib. de, - parallel implementation of BnB-algorithm in SCIP based on MPI. ParaSCIP demonstrates an advantages of fine-grained parallelization in homogeneous computing environment, i.e. HPC-clusters. By now we have access to three clusters from Russian Top50 where ParaSCIP have been installed. In the article several ways to involve ParaSCIP processes running on different clusters in solving common optimization problem are discussed. (C) 2019 the Authors. Published by Elsevier Ltd.

关键词： branch-and-bound domain decomposition distributed global optimization message passing interface

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Building Large-Scale Deep Learning System for Entity Recognition in E-Commerce Search 19

Building Large-Scale Deep Learning System for Entity Recogni...

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12th IEEE/ACM conference on Utility and Cloud computing (UCC) / 6th IEEE/ACM international conference on Big Data computing, applications and technologies (BDCAT)

作者： Wen, Musen Vasthimal, Deepak Kumar Lu, Alan Wang, Tian Guo, Aimin eBay Inc San Jose CA 95125 USA

ISBN: (纸本)9781450370165

Named-Entity-Recognition (NER) or Item Aspect Recognition task is fundamental to e-commerce marketplace. A structured listing (i.e. item aspect and catalog) is critical to the success of the marketplace - it helps the seller to put their listings to the right catalog and aspects so that the buyers can easily find the most relevant and accurate listings they are looking for. For e-commerce search engine, item aspects (brand, color, size, texture, etc.) should be automatically recognized from users' shopping queries in order to accurate understand their shopping intent. An accurate query and item aspect understanding helps to match seller's listing to buyer's purchase intend. However, in practice, this still remains challenge in e-commerce marketplace due to a couple reasons, e.g. the sparsity of the data-hundreds of millions of item aspect-name and aspect-value pairs (e.g. brand=Nike);noisy (low quality), not scalable but expensive label data that are obtained via human labeling [12] effort;the lack of context in generally very short search queries;the disperse of context and dis-orderedness in listing title or descriptions, etc. among others. All those imposes a big challenge that affect the performance of aspect recognition for e-commerce practice. In this paper, we introduce an end-to-end machine learning system to build an effective aspect recognition system for search engine that leverages the generated label data from different legacy systems and effort, without any extra human-label effort (and thus no incurred cost). the framework is constructed with multiple machine learning components that are built to optimize the search relevance and conversion as an end goal. We show that the proposed aspect recognition machine learning system improved search results quality and relevance greatly compared to the existing already strong baseline system for e-commerce search.

关键词： name-entity-recognition aspect recognition e-commerce neural networks text mining deep learning big data artificial intelligence parallel processing distributed computation

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Using Biomass Gasification for Small Scale Power Generation Systems: Specifications of the Conceptual Framework 8

Using Biomass Gasification for Small Scale Power Generation ...

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international conference on Renewable Energy Research and applications

作者： Resende, F. O. Silva, V. F. Mendonca, M. L. Barbosa, A. C. Brito, P. Azevedo, J. C. Almeida, A. Gomes, H. T. Univ Aveiro Inst Telecommun Sch Technol & Management Agueda Agueda Portugal Polytech Inst Braganca CIMO Res Ctr Mt Sch Technol & Management Braganca Portugal Polytech Inst Braganca Agr Super Sch Braganca Portugal

ISBN: (纸本)9781728135878

the development of small-scale power generation units based on biomass gasification is an effective mean to meet the growth interest of deployment of local power generation exploiting endogenous renewable energy sources. However, significant research and development activities are required towards the deployment of cost-effective solutions suitable to be used in several applications and with different biomass feedstock. For this purpose, a flexible experimental setup is required to be developed. this paper proposes a critical review of the current state of the art of the available technologies suitable for small-scale power generation using biomass gasification. the main guidelines to develop cost-effective solutions are identified and the conceptual framework of the experimental setup is proposed. Also, the operational specifications are presented.

关键词： Biomass gasification syngas distributed generation renewable energy systems grid integration

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