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Dealing with massive data with a distributed expectation propagation particle filter for object tracking

Dealing with massive data with a distributed expectation pro...

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international conference on Information Fusion

作者： Allan De Freitas Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Target tracking in distributed networks faces the challenge in coping with large volumes of distributed data which requires efficient methods for real time applications with minimal communication overhead. the complexity considered in this paper is when each sensor in a distributed network observes a large number of measurements which are all required to be processed at each time step. the particle filter has been widely used for localisation and tracking in distributed networks with a small number of measurements [1]. this paper goes beyond the current state-of-the-art and presents a novel particle filter approach, combined with the expectation propagation framework, that is capable of dealing with the challenges presented by a large volume of measurements in a distributed network. In the proposed algorithm, the measurements are processed in parallel at each sensor node in the network and the communication overhead is minimised substantially. We show results with large improvements in communication overhead, with a negligible loss in tracking performance, compared with the standard centralised particle filter.

关键词： Atmospheric measurements Particle measurements Time measurement Current measurement distributed databases Particle filters Estimation

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Optimization of complex SPARQL Analytical queries 19

Optimization of complex SPARQL Analytical queries

引用

19th international conference on Extending Database Technology, EDBT 2016

作者： Ravindra, Padmashree Kim, Hyeongsik Anyanwu, Kemafor Microsoft Corporation Redmond United States North Carolina State University Raleigh United States

ISBN: (纸本)9783893180707

Analytical queries are crucial for many emerging Semantic Web applications such as clinical-trial recruiting in Life Sciences that incorporate patient and drug profile data. Such queries compare aggregates over multiple groupings of data which pose challenges in expression and optimization of complex grouping-aggregation constraints. While these challenges have been addressed in relational models, the semi-structured nature of RDF introduces additional challenges that need further investigation. Each grouping required in an RDF analytical query maps to a graph pattern subquery with related groups leading to overlapping graph patterns within the same query. the resulting algebraic expressions for such queries contain large numbers of joins, groupings and aggregations, posing significant challenges for present-day optimizers. In this paper, we propose an approach for supporting efficient and scalable RDF analytics that follows the well known technique of simplifying algebraic expressions of RDF analytical queries in a way that enables better optimization. Specifically, the approach is based on a refactoring of analytical queries expressed in the relational-like SPARQL algebra based on a new set of logical operators. this refactoring achieves shared execution of common subexpressions that enables parallel evaluation of groupings as well aggregations, leading to reduced I/O and processing costs, particularly beneficial for scale-out processing on distributed Cloud systems. Experiments on real-world and synthetic benchmarks confirm that such a rewriting can achieve up to 10X speedup over relational-style SPARQL query plans executed on popular Cloud systems. © 2016, Copyright is with the authors.

关键词： distributed cloud

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Adaptive MaxEnt modeling of distributed decision fusion without knowledge of prior probabilities of local decisions

Adaptive MaxEnt modeling of distributed decision fusion with...

引用

international conference on Information Fusion

作者： Ivan Kadar Kenneth Hintz Interlink Systems Sciences Inc. Lake Success NY USA Dept. of Electrical & Computer Engineering George Mason University Fairfax VA U.S.A.

We introduce the method of the Maximum Entropy (MaxEnt) model for fusing local decisions in a distributed multiple sensor system. the fusion center receives local binary decisions in the usual parallel architecture. No assumptions are made about knowing any local decision rules. Our approach is based on the concept of machine learning, wherein the MaxEnt parametric model is used for supervised classification and prediction serving as the central (global) decision rule. therefore, the system is able to learn the detection performance of the sensors as a function of time without prior knowledge of the actual probabilities of local decisions, only requiring an initial set of random training data. thus it is demonstrated that the system is adaptive and can learn contextual changes of the sensors. Furthermore, we provide simulation results comparing the MaxEnt fusion center performance with published results using both the Bayesian formulation and Neyman-Pearson criterion and with MaxEnt achieving the best, realistic detection performance demonstrating the effectiveness of the method.

关键词： Entropy Training data Adaptive systems Data models Bayes methods Adaptation models

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Evaluation of an MPI-Based Implementation of the Tascell Task-parallel Language on Massively parallel systems

Evaluation of an MPI-Based Implementation of the Tascell Tas...

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international conference on parallel Processing Workshops (ICPPW)

作者： Daisuke Muraoka Masahiro Yasugi Tasuku Hiraishi Seiji Umatani Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Kyushu Institute of Technology Academic Center for Computing and Media Studies Kyoto University Graduate School of Infomatics Kyoto University

ISBN: (纸本)9781509028269

Tascell is a task parallel language that supports distributed memory environments. the conventional implementation of Tascell realizes inter-node communication with TCP/IP communication via Tascell servers. this implementation is suitable for dynamic addition of computation nodes and wide-area distributed environments. On the other hand, in supercomputer environments, TCP/IP may not be available for inter-node communication and there may be no appropriate places for deploying Tascell servers. In this study, we have developed a server-less implementation of Tascell that realizes inter-node communication with MPI communication in order to evaluate its performance on massively parallel systems. It performs well on four Xeon Phi coprocessors (with 456 workers) and the K computer, for instance, our 19-queens solver achieves a 4615-fold speedup relative to a serial implementation with 7168 workers on the K computer. Our server-less implementation realizes deadlock freedom, although it only requires the two-sided communication paradigm and the MPI_thREAD_FUNNELED support level. On Xeon Phi coprocessors, we compare our implementation with other implementations that employ TCP/IP or the MPI_thREAD_MULTIPLE support level.

关键词： Message systems Servers System recovery Computers Coprocessors TCPIP parallel processing

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Analyzing the Effects of Reordering Work List Items for Selected Control Flow Patterns 19

Analyzing the Effects of Reordering Work List Items for Sele...

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IEEE 19th international Enterprise distributed Object computing (EDOCW)

作者： Pflug, Johannes Rinderle-Ma, Stefanie Univ Vienna Res Grp Workflow Syst & Technol Vienna Austria

ISBN: (纸本)9781467393317

Efficient resource management is an important requirement for many process-oriented applications. Typically, work items are assigned to resources through their work lists. there are many reasons for reordering work items in a resource's work list. For process scheduling, for example, swapping process instances constitutes a mean to keep due times. At the same time, reducing the throughput time of the global process is typically not the primary goal. For process optimization, in turn, the implications of reordering work items on the overall temporal performance of the process might be crucial. In this paper, we investigate how reordering work items affects performance parameters that are typically associated with a first-in-first-out processing mechanism at resources. the analysis is conducted for single process tasks and for typical control flow patterns such as sequence as well as parallel and alternative branchings. It is shown that the implications on the global throughput time are less than expected, while the effects on instance-based parameters strongly depend on the control-flow pattern in which the reordering mechanism is implemented. the results are supported by means of a simulation.

关键词： information systems resource allocation scheduling alternative branchings control flow patterns first-in-first-out processing mechanism instance based parameters parallel branchings process optimization process scheduling resource management work list items reordering mechanism Delays Electronic mail Engines Optimization Process control throughput Yttrium Business Process Analysis Business Process Management Business Process Optimization Work List Management management of resources FLOW PATTERNS Process optimization Business process management electronic mail throughput time Engines Process control Rare earths yttrium throughput Computer based information systems Resource utilization delay

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PASSENGERS' CHOICES ON AIRPORT DROP-OFF SERVICE: A DECISION FORECAST BASED ON SOCIAL LEARNING AND MACHINE LEARNING

PASSENGERS' CHOICES ON AIRPORT DROP-OFF SERVICE: A DECISION ...

引用

2016 4th IEEE international conference on Cloud computing and Intelligence systems（IEEE CCIS2016）

作者： Zhongqiang Wang Jian Peng Yu Chen Limiao Ning College of Computer Science Sichuan University

ISBN: (纸本)9781509012572

Airport drop-off service provided by airlines is a chauffeur-driven service(i.e. Uber and Di Di) as an emerging travel choice for travelers. More and more passenger enjoy the drop-off service. In practice, we find an interesting question: if a passenger has ever choice the drop-off service, whether they are willing to recommend this service to other traveler? Although the acknowledgment that social learning is related to travel decision is promoted, quantitative analysis about how social learning shape and impact the decision of passengers is still limited. We study and estimate a diffusion probability between different passengers by proposing a CCM(Co-travel Link Cascade Model) based on a modified EM iterative algorithm. then, we segment passengers into three types(Influenced, Unchecked and Immune). the three types of passengers are predicated by approaches of IC-like model, Random Forest model and probabilistic model, respectively. In addition, we also design a parallel implementation of our proposed algorithm in the Apache Spark distributed data processing environment. Experimental results on a real aviation data set demonstrate that CCM can efficiently infer the decision of travelers.

关键词： Airport drop-off service Social influence EM algorithm Machine learning parallel algorithm

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GPU-PSO : parallel Particle Swarm Optimization approaches on Graphical Processing Unit for Constraint Reasoning: Case of Max-CSPs 19th

GPU-PSO : Parallel Particle Swarm Optimization approaches on...

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19th international conference on Knowledge Based and Intelligent Information and Engineering systems (KES)

作者： Dali, Narjess Bouamama, Sadok Univ Manouba Natl Sch Comp Sci COSMOS Lab Manouba Tunisia

Constraint Satisfaction Problems (CSPs) occur now in different domains. Several methods are used to solve them. In particular, Particle Swarm Optimization (PSO) allows to solve efficiently CSPs by significantly reducing the calculation time to explore the search space of solutions. However, this metaheuristic is excessively costing when facing large instances. In this paper we address the Maximal Constraint Satisfaction Problems (Max-CSPs). We introduce a new resolution approach that allows solving efficiently the Max-CSPs even with large instances. Our purpose is to implement a PSO based method by using the GPU architecture as a parallel computing framework. In particular, we focus on the implementation of two parallel novel approaches. the first one is a parallel GPU-PSO for Max-CSPs (GPU-PSO) and the second one is a GPU distributed PSO for Max-CSPs (GPU-DPSO). Our experimental results show the efficiency of the two proposed approaches and their ability to exploit GPU architecture. (C) 2015 the Authors. Published by Elsevier B.V.

关键词： particle swarm optimization Maximal Constraint satisfaction problems GPU-computing

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distributed system for satellite trajectory estimation 19

Distributed system for satellite trajectory estimation

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19th international conference on System theory, Control and computing (ICSTCC)

作者： Barbulescu, Lucian Florentin Bazavan, Petru Calin Dabu, Claudia Maria Univ Craiova Fac ACE Dept CIT Craiova Romania Univ Craiova Fac MNS Dept Informat Craiova Romania CS Romania SA Craiova Romania

ISBN: (纸本)9781479984817

the near-Earth space surveillance requires accurate and fast orbit predictions for the multitude of objects that are present around our planet. this prediction is performed by means of orbit propagators which offer solutions to the satellite equations of motion. Based on the method of computation those propagators are split into three categories: analytical, numerical and semi-analytical, each with its own advantages and disadvantages. Today a large number of propagators are implemented in the form of software libraries, some proprietary and some freeware or even open-source. In this paper we have taken a semi-analytical propagator from an open-source Java library and used it in a distributed environment in order to compute the position of a satellite during a large time interval.

关键词： parallel and distributed computing Java orbital state computation satellite propagation

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Minimal Sufficient Information about the Scientific Workflows to Create Reproducible Experiment 19

Minimal Sufficient Information about the Scientific Workflow...

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19th IEEE international conference on Intelligent Engineering systems (INES)

作者： Banati, Anna Kacsuk, Peter Kozlovszky, Miklos MTA SZTAKI H-1518 Budapest Hungary Univ Westminster London W1W 6UW England Obuda Univ John von Neumann Fac Informat Biotech Lab Becsi Str 96-b H-1034 Budapest Hungary

ISBN: (纸本)9781467379397

the reproducibility of an in-silico experiment is a great challenge because of the parallel and distributed environment and the complexity of the scientific workflows. In order to solve such problems on one hand provenance data has to be captured about the dataflow, the ancestry of the results and the environment of the execution, on the other hand description data has to be collected from the scientist and stored about the essential details, the types and samples of input/output data, and the operation of the experiment. the ultimate goal of our work is to propose a minimal dataset for recording and reporting scientific workflow based experiment, which will facilitate the reproducibility of such experiments, the public repositories and enable to share and reuse the scientific results. One part of the dataset can be filled in manually by the scientist, certain part can be filled in automatically by the system and other part can be filled in from provenance data.

关键词： data handling parallel processing workflow management software distributed environment hand description data hand provenance data in-silico experiment parallel environment scientific workflows Artificial intelligence conferences Databases Documentation Hardware Libraries Workflow management software Workflow management software data processing parallel PROCESSING (COMPUTERS) Artificial Intelligence Computer hardware Libraries Database Workflow

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distributed control of voltage supply system used in electric vehicles 19

Distributed control of voltage supply system used in electri...

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19th international conference on System theory, Control and computing (ICSTCC)

作者： Alexa, Ioan-Alex Onea, Alexandru Gh Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781479984817

the actual trend in automotive is to reduce as much as possible pollution caused by motor vehicle emissions. this could be achieved by using electrical propulsion systems which usually contains: electrical machine, three phase voltage source inverter (VSI), voltage supply system. the main problem of the full electric vehicles represents the autonomy due to low energy capacity of batteries. this issue could be solved by using a hydrogen fuel cell in parallel with a battery pack to extend the range of the electrical vehicles. this paper presents a voltage supply system which connects to a DC bus a battery, a fuel cell, and a supercapacitor using DCDC converters. the focus of this work is on keeping controlling the voltage level of DC bus and on energy distribution between elements of voltage supply system. the proposed algorithm is validated using a close loop model which contains simulation of: fuel cell, battery pack, supercapacitor, DCDC converter, distributed control strategy of voltage supply system, DC Link capacitor, VSI, electrical machine, vehicle dynamics, and driver. the model is developed in Matlab/Simulink.

关键词： voltage supply system electric vehicle distributed control fuel cell supercapacitor

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