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A Cloud-based Analytics Architecture for the Application of Online Machine Learning algorithms on Data Streams in Consumer-centric Internet of things Domains 5

A Cloud-based Analytics Architecture for the Application of ...

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5th international conference on Internet of things, Big Data and Security (IoTBDS)

作者： Zschoernig, theo Windolph, Jonah Wehlitz, Robert Franczyk, Bogdan Inst Appl Informat InfAI Goerdelerring 9 D-04109 Leipzig Germany Univ Leipzig Informat Syst Inst Grimmaische Str 12 D-04109 Leipzig Germany Wroclaw Univ Econ Business Informat Inst Ul Komandorska 118-120 PL-53345 Wroclaw Poland

ISBN: (纸本)9789897584268

the increasing number of smart devices in private households has lead to a large quantity of smart homes worldwide. In order to gain meaningful insights into their generated data and offer extended information and added value for consumers, data analytics architectures are essential. In addition, the development and improvement of machine learning techniques and algorithms in the past years has lead to the availability of powerful analytics tools, which have the potential to allow even more sophisticated insights at the cost of changed challenges and requierements for analytics architectures. However, architectural solutions, which offer the ability to deploy flexible, machine learning-based analytics pipelines on streaming data, are missing in research as well as in industry. In this paper, we present the motivation and a concept for machine learning-based data processing on streaming data for consumer-centric Internet of things domains, such as smart home. this approach was evaluated in terms of its performance and may serve as a basis for further development and discussion.

关键词： Internet of things Stream processing Online Machine Learning Kappa Architecture

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An Algorithmic Approach to Communication Reduction in parallel Graph algorithms 24

An Algorithmic Approach to Communication Reduction in Parall...

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24th international conference on parallel Architecture and Compilation (PACT)

作者： Harshvardhan Fidel, Adam Amato, Nancy M. Rauchwerger, Lawrence Texas A&M Univ Dept Comp Sci & Engn Parasol Lab College Stn TX 77843 USA

ISBN: (纸本)9781467395243

Graph algorithms on distributed-memory systems typically perform heavy communication, often limiting their scalability and performance. this work presents an approach to transparently (without programmer intervention) allow fine-grained graph algorithms to utilize algorithmic communication reduction optimizations. In many graph algorithms, the same information is communicated by a vertex to its neighbors, which we coin algorithmic redundancy. Our approach exploits algorithmic redundancy to reduce communication between vertices located on different processing elements. We employ algorithmaware coarsening of messages sent during vertex visitation, reducing both the number of messages and the absolute amount of communication in the system. To achieve this, the system structure is represented by a hierarchical graph, facilitating communication optimizations that can take into consideration the machine's memory hierarchy. We also present an optimization for small-world scale-free graphs wherein hub vertices (i.e., vertices of very large degree) are represented in a similar hierarchical manner, which is exploited to increase parallelism and reduce communication. Finally, we present a framework that transparently allows fine-grained graph algorithms to utilize our hierarchical approach without programmer intervention, while improving scalability and performance. Experimental results of our proposed approach on 131, 000+ cores show improvements of up to a factor of 8 times over the non-hierarchical version for various graph mining and graph analytics algorithms.

关键词： parallel graph processing graph analytics big data

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A parallel implementation of an XDraw viewshed algorithm with spark 21

A parallel implementation of an XDraw viewshed algorithm wit...

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21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Jianbo, Zhang Caikun, Chen Tingnan, Liang Hao, Xia Simin, Zhou Faculty of Information Engineering China University of Geosciences Wuhan China

ISBN: (纸本)9781728120584

Viewshed analysis is an indispensable part of digital terrain analysis and is widely used in many application domains. High-resolution raster DEM data bring significant computational challenges to the existing viewshed analysis algorithms, which are computationally intensive and require a large memory space and massive computing power. the viewshed analysis process can be accelerated through the use of Apache Spark. In this article, we present both a tile-based raster data storing strategy and an equal-volume computing strategy for distributed viewshed computation using Spark. the parallel implementation of the XDraw algorithm mainly consists of three parts: (1) partitioning a raster DEM file into square tile sets and reorganizing these tile sets to prevent tile overlap across data divisions of HDFS, (2) subdividing the DEM into multiple equal-volume data sectors according to the viewpoint position, and (3) retrieving the corresponding tile sets of each sector to perform the XDraw algorithm independently and efficiently. Experiments with realworld datasets show that the two proposed strategies can achieve higher speed-up and efficiency for XDraw viewshed analysis as the raster DEM data volume is dramatically increased. © 2019 IEEE.

关键词： Sparks Partitioning algorithms Distributed databases Graphics processing units Geology parallel processing Approximation algorithms

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HPPC 2010: 5th Workshop on Highly parallel processing on a Chip

HPPC 2010: 5th Workshop on Highly Parallel Processing on a C...

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17th international Euro-Par conference on parallel processing

作者： Forsell, Martti Traff, Jesper Larsson Tech Res Ctr Finland VTT Oulu Finland Univ Vienna Fac Comp Sci Dept Comp Sci Vienna Austria

ISBN: (纸本)9783642297373;9783642297366

Despite the processor industry having more or less successfully invested already 10 years to develop better and increasingly parallel multicore architectures, both software community and educational institutions appear still to rely on the sequential computing paradigm as the primary mechanism for expressing the (very often originally inherently parallel) functionality, especially in the arena of general purpose computing. In that respect, parallel programming has remained a hobby of highly educated specialists and is still too often being considered as too difficult for the average programmer. Excuses are various: lack of education, lack of suitable easy-to-use tools, too architecture-dependent mechanisms, huge existing base of sequential legacy code, steep learning curves, and inefficient architectures. It is important for the scientific community to analyze the situation and understand whether the problem is with hardware architectures, software development tools and practices, or both. Although we would be tempted to answer this question (and actually try to do so elsewhere), there is strong need for wider academic discussion on these topics and presentation of research results in scientific workshops and conferences.

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Molecular Nano Communication Networks: architectures, Protocols and Technologies 5

Molecular Nano Communication Networks: Architectures, Protoc...

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5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Abbas, Ash Mohammad Aligarh Muslim Univ Zakir Husain Coll Engn & Technol Dept Comp Engn Aligarh 202002 Uttar Pradesh India

ISBN: (纸本)9781665476478

A significant interest is created in molecular nano communication networks (MNCNs) where molecules transport information among nano transceivers. In this paper, we present a survey of state-of-the-art research related to MNCNs. We point out issues and challenges related to MNCNs. We focus on the research related to architectures, protocols and technologies for MNCNs. Also, we describe an overview of models of MNCNs pertaining to analysis of energy efficiency, capacity, design of receivers, and the effect of noise. Further, we describe schemes related to bio-nano networks.

关键词： Molecular nano communication networks protocols architectures models of networks bio-nano networks

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High frequency electromagnetic fields analysis with the use of the parallel FDTD method

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5th international conference on parallel processing and Applied Mathematics

作者： Walendziuk, W Forenc, J Jordan, A Bialystok Tech Univ Fac Elect Engn PL-15351 Bialystok Poland Polish Japanese Inst Informat Technol PL-02008 Warsaw Poland

In the paper the parallel algorithms of the Finite-Difference Time-Domain method are presented. those algorithms are based on the space domain decomposition. In the presented work, communications among computation nod... 详细信息

ISBN: (纸本)3540219463

关键词： parallel algorithms

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Pattern-based Automatic parallelization of Representative-based Clustering algorithms 5

Pattern-based Automatic Parallelization of Representative-ba...

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5th IEEE international conference on Data Science and Advanced Analytics (IEEE DSAA)

作者： Islam, Saiyedul Balasubramaniam, Sundar Gupta, Shruti Brajesh, Shikhar Badlani, Rohan Labhishetty, Nitin Baid, Abhinav Goyal, Poonam Goyal, Navneet Birla Inst Technol & Sci Pilani Dept Comp Sci & Informat Syst Adv Data Analyt & Parallel Technol Lab Pilani Campus Pilani Rajasthan India BITS Pilani Pilani Rajasthan India

ISBN: (纸本)9781538650905

Ease of programming and optimal parallel performance have historically been on the opposite side of a tradeoff, forcing the user to choose. With the advent of the Big Data era and rapid evolution of sequential algorithms, the data analytics community can no longer afford the tradeoff. We observed that several clustering algorithms often share common traits-particularly, algorithms belonging to same class of clustering exhibit significant overlap in processing steps. Here, we present our observation on domain patterns in Representative-based clustering algorithms and how they manifest as clearly identifiable programming patterns when mapped to a Domain Specific Language (DSL). We have integrated the signatures of these patterns in the DSL compiler for parallelism identification and automatic parallel code generation. Our experiments on different state-of-the-art parallelization frameworks shows that our system is able to achieve near-optimal speedup while requiring a fraction of the programming effort, making it an ideal choice for the data analytics community.

关键词： Clustering Domain Specific Language parallelizing Compiler High Performance Computing Programming Patterns

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CONTRIBUTION OF thE POLARIZATION DIVERSITY IN HF DIRECTION FINDING SYSTEMS

CONTRIBUTION OF THE POLARIZATION DIVERSITY IN HF DIRECTION F...

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5th SPIE conference on Advanced Signal processing - algorithms, architectures, and Implementations

作者： ERHEL, Y EDJEOU, A BERTEL, L CTR RECH ECOLES COETQUIDAN F-56381 GUERFRANCE

ISBN: (纸本)0819416207

the aim of the paper is to estimate the contribution of the polarization diversity in high frequency (3 - 30 MHz) direction finding systems. We first describe the peculiarities of H.F. propagation and the resulting signal model involved in computer simulations. Next, we analyze the behavior of some particular direction finding systems using linear and circular geometries and polarization diversity. Some algorithms (non linear frequential analysis, M.U.S.I.C.) are tested in several conditions (narrowband or broadband signals, polarization filtering reiterated or no, sub-sampling). theoretical and experimental results show that polarization diversity based upon the knowledge of the antenna complex responses improves greatly the efficiency of direction finding.

关键词： DIRECTION FINDING POLARIZATION ARRAY processing HIGH RESOLUTION algorithms HF PROPAGATION

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Data management approach for handling large compressed arrays in high performance computing

Data management approach for handling large compressed array...

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Proceedings of the 5th Symposium on the Frontiers of Massively parallel Computation

作者： Seamons, Kent E. Winslett, Marianne Univ of Illinois Urbana United States

the scalability of parallel architectures, data sets and algorithms is often hindered by poor parallel input/output performance. However, recent studies show that for input/output of large arrays, the storage of arrays by subarray divisions, also known as chunking, is capable of improving input/output performance. A new method for increasing the performance advantages of chunking by combining it with data compression is presented. the method is tested on the Intel iPSC/860.

关键词： parallel processing systems

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Spatiotemporal Classification Using Neuroscience-Inspired Dynamic architectures 5

Spatiotemporal Classification Using Neuroscience-Inspired Dy...

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5th Annual international conference on Biologically Inspired Cognitive architectures (BICA)

作者： Schuman, Catherine D. Birdwell, J. Douglas Dean, Mark E. Univ Tennessee Dept EECS Knoxville TN 37996 USA

We discuss a neuroscience-inspired dynamic architecture (NIDA) and associated design method based on evolutionary optimization. NIDA networks designed to perform anomaly detection tasks and control tasks have been shown to be successful in previous work. In particular, NIDA networks perform well on tasks that have a temporal component. We present methods for using NIDA networks on classification tasks in which there is no temporal component, in particular, the handwritten digit classification task. the approach we use for both methods produces useful subnetworks that can be combined to produce a final network or combined to produce results using an ensemble method. We discuss how a similar approach can be applied to other problem types.

关键词： neuromorphic computing neuroscience-inspired architectures discrete-event systems neural networks evolutionary algorithms spatiotemporal information processing handwritten digit classification

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