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检索条件"任意字段=International Symposium Distributed Computing and Applications Business Engineering and Science"

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CHIUW 2018 Keynote

CHIUW 2018 Keynote

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IEEE international symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Katherine Yelick

ISBN: (纸本)9781538655566;9781538655559

In the next few years, exascale computing systems will become available to the scientific community. These systems will require new levels of parallelization, new models of memory and storage, and a variety of node architectures for processors and accelerators. In the decade that follows, we can expect more of these changes, as well as increasing levels of hardware specialization. These systems will provide simulation and analysis capabilities at unprecedented scales, and when combined with advanced physical models, mathematical and statistical methods, and computer science and abstractions, they will lead to scientific breakthroughs. Yet the full power of these systems will only be realized if there is sufficient high-level programming support that will abstract details of the machines and give programmers a natural interface for writing new science applications.

关键词： Computational modeling Program processors Electrical engineering Analytical models Mathematical model distributed processing Conferences

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A business Site Selection System Using Co-Locations and Ontologies

A Business Site Selection System Using Co-Locations and Onto...

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IEEE international Conference on Big Data and Cloud computing (BdCloud)

作者： Xuguang Bao Lizhen Wang Department of Computer Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781728111421;9781728111414

This demonstration presents a data-driven situation-specific site selection system (called D4S for short) for users who are eager to create a business (hotel, hot-pot restaurant, etc.). Given a business and some related parameters, it selects sites that may gain profits for this business. Unlike existing works, D4S uses the meaning of prevalent co-location patterns discovered from spatial data sets to learn the "symbiotic" relationships in business, which can help to select reliable sites. Furthermore, ontologies are used in order to classify the whole spatial data according to the semantic of businesses, which makes the results more accurate. The demonstration shows the high reliability of the result sites selected by D4S.

关键词： business Ontologies Reliability Spatial databases Semantics Data mining Geography

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The deployment of an enhanced model-driven architecture for business process management 18

The deployment of an enhanced model-driven architecture for ...

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22nd international Database engineering and applications symposium, IDEAS 2018

作者： McClatchey, Richard University of the West of England Bristol United Kingdom

ISBN: (纸本)9781450365277

business systems these days need to be agile to address the needs of a changing world. business modelling requires process management to be highly adaptable with the ability to support dynamic workflows, inter-application integration (potentially between businesses) and process reconfiguration. Designing in the ability to cater for evolution is critical to success. To handle change, systems need the capability to adapt as and when necessary to changes in users’ requirements. Using our implementation of a self-describing system, a so-called description-driven approach, new versions of data structures or processes can be created alongside older versions providing a log of changes to the underlying data schema and enabling the gathering of traceable ("provenance") data. The CRISTAL software, which originated at CERN for handling physics data, uses versions of stored descriptions to define data and workflows which can be evolved over time and thereby to handle evolving system needs. It has been customised for use in business as the Agilium-NG product. This paper reports on how the Agilium-NG software has enabled the deployment of an unique business process management solution that can be dynamically evolved to cater for changing user requirements. © 2018 Association for computing Machinery.

关键词： Software design

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Proceedings - 15th IEEE international Conference on Trust, Security and Privacy in computing and Communications, 10th IEEE international Conference on Big Data science and engineering and 14th IEEE international symposium on Parallel and distributed Processing with applications, IEEE TrustCom/BigDataSE/ISPA 2016

Proceedings - 15th IEEE International Conference on Trust, S...

引用

Joint 15th IEEE international Conference on Trust, Security and Privacy in computing and Communications, 10th IEEE international Conference on Big Data science and engineering and 14th IEEE international symposium on Parallel and distributed Processing with applications, IEEE TrustCom/BigDataSE/ISPA 2016

ISBN: (纸本)9781509032051

The proceedings contain 311 papers. The topics discussed include: a robust image hashing with enhanced randomness by using random walk on zigzag blocking;securing fast learning! ridge regression over encrypted big data;computational trust model for repeated trust games;formal analysis of selective disclosure attribute-based credential system in applied Pi calculus;node trust prediction framework in mobile ad hoc networks;trust enhancement over range search for encrypted data;healthcare fraud detection based on trustworthiness of doctors;integrated security for services hosted in virtual environments;a permissioned blockchain framework for supporting instant transaction and dynamic block size;trust validation of cloud IaaS: a customer-centric approach;distributed bitcoin account management;trusted Boolean search on cloud using searchable symmetric encryption;and dynamic attribute-based access control in cloud storage systems.

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A Co-Location-Based Approach for business Site Selection Using Ontologies

A Co-Location-Based Approach for Business Site Selection Usi...

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IEEE international Conference on Big Data and Cloud computing (BdCloud)

作者： Xuguang Bao Lizhen Wang Department of Computer Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781728111421;9781728111414

Site selection is one of the most crucial and important decisions made by any business. Such a decision depends on various factors of sites, including socioeconomic, geography, ecology, as well as specific requirements of businesses. The existing approaches for site selection (commonly used by economists) are manual, subjective and not scalable. This paper presents a data-driven situation-specific site selection approach (called D3SA for short) for users who are eager to create a business (hotel, hot-pot restaurant, etc.). Given a business and some related parameters, it recommends suggested sites that can satisfy users' requirements. Unlike existing works, we consider using prevalent co-location patterns discovered from spatial data sets to select the sites. Meanwhile, a novel co-location generation algorithm is pro-posed to discover co-location patterns efficiently. Furthermore, ontologies are used in order to classify the whole spatial data according to the semantic of businesses to assist to find the "best" co-location pattern. The experimental results evaluated based on both a real POI (Points of Interests) dataset in Beijing and ex-tended synthetic data sets show the effectiveness and efficiency of D3SA.

关键词： Ontologies Spatial databases Data mining Geography Companies

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A case study in higher education domain based on a prototype for business process models improvement: BPMoQualAssess 18

A case study in higher education domain based on a prototype...

引用

18th international Conference on Parallel and distributed computing, applications and Technologies, PDCAT 2017

作者： Kahloun, Fouzia Ghannouchi, Sonia Ayachi Laboratory RIADI-GDL ENSI Mannouba Tunisia High Institute on Management of Sousse Sousse Tunisia

ISBN: (纸本)9781538631515

business process modeling is essential for improving and automating business processes and for comparing them. Furthermore, evaluating their quality must be based on a set of measures. In this context, our contribution in this paper consists in proposing a prototype for Process model assessment and applying it on a real business process model in the higher education. Our prototype is based on four main steps (modeling, measurement, evaluation and improvement). It takes as input a BPMN model for which some measures are applied to define its level of quality. After that, we compare the obtained measures with the threshold values presented in literature with the purpose of evaluating the considered BPMN model level of quality. Finally, based on guidelines applied to each exceeding measure, we help the designer to improve his/her model. © 2017 IEEE.

关键词： Systems engineering

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On-Demand Routing for Chaining Multiple Functions in ICN-Based In-Network Processing

On-Demand Routing for Chaining Multiple Functions in ICN-Bas...

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IEEE Workshop on Local and Metropolitan Area Networks (LANMAN)

作者： Yusaku Hayamizu Akira Nagata Miki Yamamoto Graduate School of Science and Engineering Kansai University Osaka Japan iD Corporation Fukuoka Japan Faculty of Engineering Science Kansai University Osaka Japan

ISBN: (纸本)9781538645345

ICN (Information-Centric Networking) originally innovated for efficient content distribution, is now discussed to be applied to edge computing in IoT (Internet of Things) environment. In this paper, we focus on more flexible network processing environment, in-network processing, which is realized with ICN architecture. In our assumed environment, multiple functions are executed on different routers widely distributed in a whole network and the end-to-end optimal route for any data processing should be selected to satisfy various IoT applications' requirement. Our proposal, an on-demand routing method efficiently chains data and multiple functions compared to an existing proactive routing method. Also, our method reactively caches routing information in the network and realize scalable routing for ICN-based in-network processing.

关键词： Routing Data processing Scalability Internet of Things Proposals Cloud computing Electronic mail

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Projective Hard Thresholding Pursuit for Nonnegative Sparse Recovery 15

Projective Hard Thresholding Pursuit for Nonnegative Sparse ...

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15th IEEE international symposium on Parallel and distributed Processing with applications (ISPA) / 16th IEEE international Conference on Ubiquitous computing and Communications (IUCC)

作者： Zhang, Xiaoya Jiang, Hao Sun, Tao Cheng, Lizhi Huang, Chun Yang, Canqun Natl Univ Def Technol Coll Sci Changsha 410072 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410072 Hunan Peoples R China UCLA Dept Math 601 Westwood Plz Los Angeles CA 90095 USA Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410072 Hunan Peoples R China

ISBN: (纸本)9781538637906

The data in scientific and engineering computations is usually gifted physical meaning in the presence of nonnegativity. For high performance and effectiveness, it is necessary and beneficial to consider the prior nonnegativity in processing the nonnegative information. In this paper, we propose a Projective Hard Thresholding Pursuit (PHTP) method for the nonnegative sparse signal recovery. It reconstructs sparse signal by combining the nonnegative projection with HTP. Theoretically, we prove that the proposed algorithm can find all nonnegative s-sparse signals provided the sensing matrix has suitable restricted isometry property. Moreover, We extend this result to its fast version. Besides, we verify PHTP's convergence with the measurement error being nonzero. Numerical experiments demonstrate that PHTP outperforms HTP and Nonnegative Least Squares(NNLS) for nonnegative sparse recovery and denoising.

关键词： Hard thresholding pursuit sparse recovery restricted isometry property nonnegative constraint

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Online adaptation and energy minimization for hardware recurrent spiking neural networks

Online adaptation and energy minimization for hardware recur...

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作者： Liu, Yu Jin, Yingyezhe Li, Peng Department of Electrical and Computer Engineering Texas A and M University 400 Bizzell St College StationTX77843 United States

The Liquid State Machine (LSM) is a promising model of recurrent spiking neural networks that provides an appealing brain-inspired computing paradigm for machine-learning applications such as pattern recognition. Moreover, processing information directly on spiking events makes the LSM well suited for cost- and energy-efficient hardware implementation. In this article, we systematically present three techniques for optimizing energy efficiency while maintaining good performance of the proposed LSM neural processors from both an algorithmic and hardware implementation point of view. First, to realize adaptive LSM neural processors, thus boost learning performance, we propose a hardware-friendly Spike-Timing Dependent Plastic (STDP) mechanism for on-chip tuning. Then, the LSM processor incorporates a novel runtime correlation-based neuron gating scheme to minimize the power dissipated by reservoir neurons. Furthermore, an activity-dependent clock gating approach is presented to address the energy inefficiency due to the memory-intensive nature of the proposed neural processors. Using two different real-world tasks of speech and image recognition to benchmark, we demonstrate that the proposed architecture boosts the average learning performance by up to 2.0% while reducing energy dissipation by up to 29% compared to a baseline LSM with little extra hardware overhead on a Xilinx Virtex-6 FPGA. © 2018 ACM.

关键词： Energy efficiency

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Stochastic Delay Forecasts for Edge Traffic engineering via Bayesian Networks 16

Stochastic Delay Forecasts for Edge Traffic Engineering via ...

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IEEE 16th international symposium on Network computing and applications (NCA)

作者： Hogan, Mary Esposito, Flavio St Louis Univ Dept Comp Sci St Louis MO 63103 USA

ISBN: (纸本)9781538614655

Traffic engineering at network edges is challenging given the latency-sensitive nature of all applications that need to be supported. End-to-end delay estimation and forecasts were essential traffic engineering tools even before the mobile edge computing paradigm pushed the cloud closer to the end user. In this paper, we model the path selection problem for edge traffic engineering using a risk minimization technique inspired by portfolio theory in economics, and we use machine learning to estimate path selection risks. In particular, using real latency time series measurements, both existing and collected with and without the GENI testbed, we compare four short-horizon latency estimation techniques, commonly used by the finance community to estimate prices of volatile financial instruments. Our results suggest that a Bayesian Network approach may lead to good latency (peak) estimation performance, as long as there are dependencies among the time series path latency measurements.

关键词： Portfolios Bayes methods Autoregressive processes Computational modeling Random variables Optimization Throughput

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