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8th international conference on Data Science, Technology and applications, DATA 2019

ISBN: (纸本)9789897583773

the proceedings contain 59 papers. the topics discussed include: overcoming the drawbacks of traditional process management software;manifold learning to identify consumer profiles in real consumption data;predicting depression tendency based on image, text and behavior data from instagram;distributed and scalable platform for collaborative analysis of massive time series data sets;a community detection approach for smart-phone addiction recognition;comparative analysis of store clustering techniques in the retail industry;data mining using morlet wavelets for financial time series;and exploring robustness in a combined feature selection approach.

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Adaptive Exploration of a UAVs Swarm for distributed Targets Detection and Tracking 8th

Adaptive Exploration of a UAVs Swarm for Distributed Targets...

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8th international conference on Pattern Recognition applications and Methods (ICPRAM)

作者： Cimino, Mario G. C. A. Lega, Massimiliano Monaco, Manilo Vaglini, Gigliola Univ Pisa Dept Informat Engn I-56122 Pisa Italy Univ Naples Parthenope Dept Engn I-80143 Naples Italy

ISBN: (纸本)9789897583513

this paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. the proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. the swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. these mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. the swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.

关键词： UAV Swarm Intelligence Stigmergy Flocking Differential Evolution Target Detection Target Tracking

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Quantifying the impact of design strategies for big data cyber security analytics: An empirical investigation 20

Quantifying the impact of design strategies for big data cyb...

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

作者： Ullah, Faheem Babar, M. Ali CREST - Centre for Research on Engineering of Software Technologies University of Adelaide Australia Cyber Security Adelaide University of Adelaide Australia

ISBN: (纸本)9781728126166

Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Hadoop and Spark) for collecting, storing, and analyzing a large volume of security event data to detect cyber-attacks. the state-of-the-art uses various design strategies (e.g., feature selection and alert ranking) to help BDCA systems to achieve the desired levels of accuracy and response time. However, the use of these strategies in the state-of-the-art is not consistent, which exposes a lack of consensus on 'when to use (and not to use) these design strategies?' In this paper, we follow a systematic experimentation framework to quantify the impact of four design strategies on the accuracy and response time with respect to three contextual factors i.e., security data, machine learning model employed in the system, and the execution mode of the system. For the aimed quantification, we performed experiments on a Hadoop-based BDCA system using four security datasets, five machine learning models, and three execution modes. Our findings lead us to formulate a set of design guidelines that will help researchers and practitioners to decide when to use (and not to use) the design strategies. © 2019 IEEE.

关键词： Cybersecurity

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A cloud-agnostic queuing system to support the implementation of deadline-based application execution policies

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FUTURE GENERATION COMPUTER SYSTEMS-thE international JOURNAL OF ESCIENCE 2019年 101卷 99-111页

作者： Kiss, Tamas DesLauriers, James Gesmier, Gregoire Terstyanszky, Gabor Pierantoni, Gabriele Abu Oun, Osama Taylor, Simon J. E. Anagnostou, Anastasia Kovacs, Jozsef Univ Westminster Ctr Parallel Comp 115 New Cavemdish St London W1W 6UW England Univ Kent Sch Comp Cyber Secur Grp Canterbury Kent England Brunel Univ London Modelling & Simulat Grp Kingston Lane Uxbridge UB8 3PH Middx England MTA SZTAKI Budapest Hungary

there are many scientific and commercial applications that require the execution of a large number of independent jobs resulting in significant overall execution time. therefore, such applications typically require distributed computing infrastructures and science gateways to run efficiently and to be easily accessible for end-users. Optimising the execution of such applications in a cloud computing environment by keeping resource utilisation at minimum but still completing the experiment by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container management technologies, such as Docker Swarm or Kubernetes, while provide auto-scaling based on resource consumption, do not support job queuing and deadline-based execution policies directly. this paper presents JQueuer, a cloud-agnostic queuing system that supports the scheduling of a large number of jobs in containerised cloud environments. the paper also demonstrates how JQueuer, when integrated with a cloud application-level orchestrator and auto-scaling framework, called MiCADO, can be used to implement deadline-based execution policies. this novel technical solution provides an important step towards the cost-optimisation of batch processing and job submission applications. In order to test and prove the effectiveness of the solution, the paper presents experimental results when executing an agent-based simulation application using the open source REPAST simulation framework. (C) 2019 the Authors. Published by Elsevier B.V.

关键词： Cloud computing Container technologies Deadline-based auto-scaling JQueuer MiCADO Agent-based simulation

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Integration into a real-time platform of distributed generation 8

Integration into a real-time platform of distributed generat...

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international conference on Renewable Energy Research and applications

作者： Lojano, Angel Morales, Diego Medina, Ricardo Gonzalez, Javier Catholic Univ Cuenca Cuenca Ecuador Catholic Univ Cuenca Postgrad Unit Cuenca Ecuador

ISBN: (纸本)9781728135878

Currently, in the Ecuadorian electricity market, it has not been possible to verify the existence of an increase in distributed generation, since there is no information to prove it, the Electricity Regulatory and Control Agency of the country, approved regulation 003 / 2018, which addresses and encourages the incorporation of distributed generation, with which an increase in distributed electricity generation can be presented. Having old and non-repowered radio distribution networks, causes inconvenience if there is a massive insertion of distributed generation. therefore, these electrical networks must be monitored and simulated in real time, in order to verify the capacity that can support the insertion of this generation, the use of Smart Grids are in full development and facilitate the study of these new technologies. From the literature review on related topics, it has been verified that there are hardware and software modeling, which perform real-time simulation for distribution networks, this paper proposes to simulate and monitor in real time the insertion of distributed generation in the network of distribution, using the Matlab-Simulink environment, which will be integrated with RT-LAB. Which are simulation platforms, which allow to integrate models based on software and hardware.

关键词： Simulacion tiempo Real RT-LAB Matlab-Simulink Redes inteligentes Generacion Distribuida

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A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

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

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

the performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： distributed parameter control systems

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A parallel uncertain frequent itemset mining algorithm with spark 20

A parallel uncertain frequent itemset mining algorithm with ...

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

作者： Ding, Jiaman Li, Haibin Wang, Yuanyuan Jia, Lianyin You, Jinguo Yang, Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781728126166

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability. © 2019 IEEE.

关键词： Data mining

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A Spark-based distributed Whale Optimization Algorithm for Feature Selection 10

A Spark-based Distributed Whale Optimization Algorithm for F...

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10th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and applications (IDAACS)

作者： Chen, Hongwei Hu, Zhou Han, Lin Hou, Qiao Ye, Zhiwei Yuan, Jiansen Zeng, Jun Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781728140698

With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. the traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. the traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. this paper proposed a feature selection method based on Whale Optimization Algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. therefore, this paper combined the Whale Optimization Algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed Whale Optimization Algorithm. the results showed that the excellent result search ability of the Whale Optimization Algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.

关键词： feature selection Whale Optimization Algorithm Spark platform parallel computing

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Real-time stream data processing at scale 20

Real-time stream data processing at scale

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

作者： Hoseinyfarahabady, M. Reza Jannesari, Ali Bao, Wei Tari, Zahir Zomaya, Albert Y. University of Sydney Center for Distributed and High Performance Computing School of Computer Science NSW Australia Department of Computer Science Iowa State University IA United States RMIT University School of Science MelbourneVIC Australia

ISBN: (纸本)9781728126166

A typical scenario in a stream data-flow processing engine is that users submit continues queries in order to receive the computational result once a new stream of data arrives. the focus of the paper is to design a dynamic CPU cap controller for stream data-flow applications with real-time constraints, in which the result of computations must be available within a short time period, specified by the user, once a recent update in the input data occurs. It is common that the stream data-flow processing engine is deployed over a cluster of dedicated or virtualized server nodes, e.g., Cloud or Edge platform, to achieve a faster data processing. However, the attributes of incoming stream data-flow might fluctuate in an irregular way. To effectively cope with such unpredictable conditions, the underlying resource manager needs to be equipped with a dynamic resource provisioning mechanism to ensure the real-time requirements of different applications. the proposed solution uses control theory principals to achieve a good utilization of computing resources and a reduced average response time. the proposed algorithm dynamically adjusts the required quality of service (QoS) in an environment when multiple stream & data-flow processing applications concurrently run with unknown and volatile workloads. Our study confirms that such a unpredictable demand can negatively degrade the system performance, mainly due to adverse interference in the utilization of shared resources. Unlike prior research studies which assumes a static or zero correlation among the performance variability among consolidated applications, we presume the prevalence of shared-resource interference among collocated applications as a key performance-limiting parameter and confront it in scenarios where several applications have different QoS requirements with unpredictable workload demands. We design a low-overhead controller to achieve two natural optimization objectives of minimizing QoS violation amount an

关键词： Quality of service

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A Low-Cost, High-Performance Implementation of RSA Algorithm Using GPGPU

A Low-Cost, High-Performance Implementation of RSA Algorithm...

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Springer international conference on Emerging technologies in Data Mining and Information Security (IEMIS)

作者： Srivatsa, P. S. Sasaank Rao, P. V. R. R. Bhogendra Keshav Mem Inst Technol Hyderabad India Def Res & Dev Lab Hyderabad Kanchanbagh India

ISBN: (纸本)9789811319518;9789811319501

Information security has been of great use in various civilian and defence domains. However, the arduous procedures involved in securely establishing a common key lead to asymmetric-key encryption. the high computational needs of asymmetric-key encryption process made them impractical to be used for real-world applications. the proliferation of low-cost, high-performance computing through GPGPUs made many applications with high-performance requirements feasible to implement and use in real-world applications. A successful attempt has been made to implement RSA algorithm using GPGPU and the results are presented in this paper.

关键词： parallel and distributed computing Heterogeneous computing GPGPU computing OpenCL Asymmetric-key encryption

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