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17th IEEE International Conference on Information Reuse and Integration

作者： Yan, Yilin Pouyanfar, Samira Tian, Haiman Guan, Sheng Ha, Hsin-Yu Chen, Shu-Ching Shyu, Mei-Ling Hamid, Shahid Univ Miami Dept Elect & Comp Engn Coral Gables FL 33124 USA Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA Florida Int Univ Dept Finance Miami FL 33199 USA

ISBN: (纸本)9781509032075

Catastrophes have caused tremendous damages in human history and triggered record high post-disaster relief from the governments. The research of catastrophic modeling can help estimate the effects of natural disasters like hurricanes, floods, surges, and earthquakes. In every Atlantic hurricane season, the state of Florida in the United States has the potential to suffer economic and human losses from hurricanes. The Florida Public Hurricane Loss Model (FPHLM), funded by the Florida Office of Insurance Regulation, has assisted Florida and the residential insurance industry for more than a decade. How to process big data for historical hurricanes and insurance companies remains a challenging research topic for cat models. In this paper, the FPHLMs novel integrated domain knowledge assisted big data processing system is introduced and its effectiveness of data processing error prevention is presented.

关键词： big data processing Catastrophe Modeling Pre-processing Post-processing Florida Public Hurricane Loss Model (FPHLM)

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Blood Pressure Monitoring on the Cloud System in Elderly Community Centres A data Capturing Platform for Application Research in Public Health 7

Blood Pressure Monitoring on the Cloud System in Elderly Com...

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7th International Conference on Cloud Computing and big data (CCBD)

作者： Tsoi, Kelvin K. F. Yip, Benjamin Au, Doreen W. H. Kuo, Yong-Hong Wong, Samuel Y. S. Woo, Jean Meng, Helen M. L. Chinese Univ Hong Kong Stanley Ho Big Data Decis Analyt Res Ctr Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Jockey Club Inst Ageing Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Jockey Club Sch Publ Hlth & Primary Care Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Med & Therapeut Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509035557

Technology on the cloud frameworks in healthcare data management and analytics has opened new horizons for public health research. Hypertension is a significant modifiable risk factor for cardiovascular diseases. Nowadays, telemonitoring blood pressure (BP) has been suggested as an effective tool for BP control. However, elderly people always have difficulties when using electronic health monitoring devices at home. BP data capturing with cloud technology in elderly community centres under guidance and with healthcare provider alert function is a pioneer. In this study, the infrastructure of data collection is constructed on the cloud to capture behavioral data on BP meter use and BP readings. BP data will be generated by the daily use of BP measurement and uploaded to the cloud. All personal characteristics, electronic health records, BP data and call log with nurse can be encrypted and store on the cloud. The remote platform on the cloud can provide efficient analytic performance on huge volume of data with high velocity of data creation in a population-based study. data mining on the BP measurement will help to better understand the ways to control hypertension. This platform will also be potentially used in other epidemiological studies in public health.

关键词： Cloud Platform Hypertension Blood Pressure big data processing

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Communication Scheduling Scheme Based on big-data Regression Analysis and Genetic Algorithm for Cyber-Physical Factory Automation

Communication Scheduling Scheme Based on Big-Data Regression...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Chen, Chao-Chun Chen, Chao-Lieh Ciou, Che-Yang Liu, Jia-Xuan Natl Cheng Kung Univ Inst Mfg Info & Sys Tainan Taiwan Natl Cheng Kung Univ Dept Comp Sci & Info Engn Tainan Taiwan Natl Kaohsiung First Univ Sci & Technol FirstTech Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781509018970

In the Industry 4.0 era, enterprises are eager to add intelligent and cyber-physical technologies to further enhance the factory automation. However, in cyber-physical factory environment, more than hundreds of or thousands of IoT devices could send data at the same time, which affect the completeness of data collection and also diminish the consequent decision correctness. In this work, we proposed a novel communication scheduling scheme based on big-data regression analysis and genetic algorithm for IoT-enabling devices to collect data in cyber-physical factory automation. The basic idea is to discover collection behaviors of IoT devices and apply the extracted behavior in finding optimal communication schedules. Our proposed scheme asks each IoT device moderately utilize the network bandwidth with their in-memory buffer for maximizing the global benefit, rather than only self benefit. Then we conducted experiments to verify and analyze the proposed scheme. The results of the experiments indicate that our proposed scheme successfully achieve the long-term data collection in scenarios of 200 IoT devices working together. This work provides developers useful experiences for creating manufacturing systems of cyber-physical factory automation.

关键词： cyber-physical manufacturing system big data processing intelligent technology scheduling optimization

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Greening big data Networks: Volume Impact 18

Greening Big Data Networks: Volume Impact

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18th International Conference on Transparent Optical Networks (ICTON)

作者： Al-Salim, Ali M. Ali, Howraa M. Mohammad Lawey, Ahmed Q. El-Gorashi, Taisir Elmirghani, Jaafar M. H. Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

ISBN: (纸本)9781509014675

Tremendous volumes generated by big data applications are starting to overwhelm data centers and networks. Traditional research efforts have determined how to process these vast volumes of data inside datacenters. Nevertheless, slight attention has addressed the increase in power consumption resulting from transferring these gigantic volumes of data from the source to destination (datacenters). An efficient approach to address this challenge is to progressively processing large volumes of data as close to the source as possible and transport the reduced volume of extracted knowledge to the destination. In this article, we examine the impact of processing different big data volumes on network power consumption in a progressive manner from source to datacenters. Accordingly, a noteworthy decrease for data transferred is achieved which results in a generous reduction in network power consumption. We consider different volumes of big data chunks. We introduce a Mixed Integer Linear Programming model (MILP) to optimize the processing locations of these volumes of data and the locations of two datacenters. The results show that serving different big data volumes of uniform distribution yields higher power saving compared to the volumes of chunks with fixed size. Therefore, we obtain an average network power saving of 57%, 48%, and 35% when considering the volumes of 10 - 220 (uniform) Gb, 110 Gb, and 50 Gb per chunk, respectively, compared to the conventional approach where all these chunks are processed inside datacenters only.

关键词： big data processing IP/WDM MILP power consumption

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Factory: Master Node High-Availability for big data Applications and Beyond 16th

Factory: Master Node High-Availability for Big Data Applicat...

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16th International Conference on Computational Science and Its Applications (ICCSA)

作者： Gankevich, Ivan Tipikin, Yuri Korkhov, Vladimir Gaiduchok, Vladimir Degtyarev, Alexander Bogdanov, Alexander St Petersburg State Univ Dept Comp Modelling & Multiprocessor Syst Univ Skaia Emb 7-9 St Petersburg 199034 Russia

ISBN: (纸本)9783319421087

Master node fault-tolerance is the topic that is often dimmed in the discussion of big data processing technologies. Although failure of a master node can take down the whole data processing pipeline, this is considered either improbable or too difficult to encounter. The aim of the studies reported here is to propose rather simple technique to deal with master-node failures. This technique is based on temporary delegation of master role to one of the slave nodes and transferring updated state back to the master when one step of computation is complete. That way the state is duplicated and computation can proceed to the next step regardless of a failure of a delegate or the master (but not both). We run benchmarks to show that a failure of a master is almost "invisible" to other nodes, and failure of a delegate results in recomputation of only one step of data processing pipeline. We believe that the technique can be used not only in big data processing but in other types of applications.

关键词： Parallel computing big data processing Distributed computing Backup node State transfer Delegation Cluster computing Fault-tolerance

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Max-flow min-cut algorithm with application to road networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第11期29卷

作者： Ramesh, Varun Nagarajan, Shivanee Jung, Jason J. Mukherjee, Saswati Anna Univ Dept Comp Sci & Engn Madras Tamil Nadu India Chung Ang Univ Dept Comp Sci Engn Seoul South Korea Anna Univ Dept Informat Sci & Technol Chennai Tamil Nadu India

The max-flow min-cut problem is one of the most explored and studied problems in the area of combinatorial algorithms and optimization. In this paper, we solve the max-flow min-cut problem on large random lognormal graphs and real-world datasets using the distributed Edmonds-Karp algorithm. The algorithm is deployed on a stand-alone cluster using Spark. In our experiments, we compare the runtime between a single-machine implementation and cluster implementation. We analyze the impact of communication cost on runtime for large lognormal graphs. Further, we record the runtime of the algorithm for various real-world datasets having similar average outdegree and number of edges but different diameters. The observations indicate that for such a set of similar graphs, runtime increases slowly with an increase in diameter. Additionally, we apply this model on large urban road networks to evaluate the minimum number of sensors required for surveillance of the entire network. To validate the feasibility of this extension, we tested the model with large road network datasets having more than 2.7million edges. Thus, we believe that our model can enhance the safety of today's dynamic large urban road networks.

关键词： big data processing distributed systems max-flow min-cut algorithm Pregel

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Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

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SENSORS 2016年第7期16卷 1-11页

作者： Prochazka, Ales Schatz, Martin Vysata, Oldrich Valis, Martin Univ Chem & Technol Dept Comp & Control Engn Prague 16628 6 Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16636 6 Czech Republic Charles Univ Prague Fac Med Hradec Kralove Dept Neurol Hradec Kralove 50005 Czech Republic

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (***). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

关键词： MS Kinect data acquisition image and depth sensors computational intelligence human-machine interaction breathing analysis neurological disorders visualization big data processing

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Meta-model Evolution with Self-Adaptation: An Empirical Development Approach for Distributed Parallel Computing Framework 15

Meta-model Evolution with Self-Adaptation: An Empirical Deve...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on big data Science and Engineering / 14th IEEE Int Symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/bigdataSE/ISPA)

作者： Wu, Renke Huang, Linpeng Yu, Peng Shen, Kai Shi, Qiuwei Zhou, Haojie Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China Jiangnan Inst Comp Technol State Key Lab Mathemat Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781509032051

Distributed parallel computing platform performs well in processing big data. However, due to the platform's complexity and distributed characteristics, it is hard to design and achieve. For example, during the platform's design phase, variations are unpredictable. To address these issues, a highly structured object-oriented framework for systematic modeling, which has high flexibility, reusability and maintainability, is needed. In this paper, we present and implement an empirical development approach to achieve the specific modeling framework, based on meta-model evolution with hot-spots (variable parts) recognition. Our work expands this idea into the following directions. Firstly, we define a generic and stable meta-model. We give out concept models, object models and dynamic models of main objects. General data partitioning, job division and inter-node communication mechanism are designed. Secondly, based on the meta-model, we complete framework's incremental adaptation using an iterative process according to design variations. Practical experience of our empirical case study shows, the approach gets a relatively mature framework for design theory instruction. The integrated framework clearly expresses main objects and their relationship. This work promotes the development of parallel computing framework to have a qualitative leap, and eventually have more extensive application and popularization of the established framework.

关键词： big data processing platform distributed parallel computing development approach modeling meta-model evolution

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AEGEUS: An online partition skew mitigation algorithm for mapreduce 16

AEGEUS: An online partition skew mitigation algorithm for ma...

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1st International Conference on Informatics and Analytics (ICIA)

作者： Kumaresan, Vimalkumar Baskaran, R. Anna Univ Coll Engn Guindy Madras 600025 Tamil Nadu India

ISBN: (纸本)9781450347563

This paper investigates the partition skew problem at reduce phase in the MapReduce jobs. Our studies with the Hadoop addresses this problem in both offline and online manner. Offline is a heuristics based approach which has to wait for the completion of map tasks and involves computation overhead to estimate the partition size. In another approach, they distribute the overloaded tasks across other nodes that needed extra split and merge operation. These extra operations, in turn, hamper the performance of the system. In this paper, we propose Aegeus, an on-line streaming based skew mitigation approach for MapReduce jobs which do not have long waiting time and extra operations for addressing the skew problem. Aegeus predicts the partition size of the each map tasks and creates the resource specification based on its requirement even before the completion of map phase. Hence, the proposed system can create the container based on the workload which can improve the overall job completion time and system performance. We evaluated Aegeus by using benchmark datasets and, compare its performance with naive Hadoop. Based on our observation, Aegeus outperforms naive Hadoop by 42% by maximizing the overall performance of the application and system.

关键词： MapReduce partitioning skew cloud computing big data processing online load balancing algorithms

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A big data inspired chaotic solution for fuzzy feedback linearization model in cyber-physical systems

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AD HOC NETWORKS 2015年 35卷 97-104页

作者： Liu, Lei Zhao, Shulin Yu, Zhilou Dai, Hongjun Shandong Univ Dept Comp Sci & Technol Jinan 250101 Peoples R China Inspur Inc Ctr Technol Jinan Peoples R China

Cyber-Physical System (CPS) that integrates computational and physical capabilities has emerged as a promising topic. It interacts with physical world and humans through ad hoc communications. In contemporary, with the fast development of CPS theory and applications, CPS generates a large volume of data, which may lead its control into a chaotic status. Hence, there is a pressing demand to solve the chaotic status CPS control. To this end, the chaotic time series prediction algorithm is employed to resolve the chaotic status featured by a fuzzy feedback linearization model. Modeling the CPS under big data without taking chaotic features into account may lead to unexpected results. This is because chaotic CPS is dramatically sensitive to small disturbances or minor changes of initiators. This paper developed a CPS model in light of fuzzy feedback linearization. Further, the chaotic time prediction algorithm is applied to solve the chaotic control problem in CPS. The developed algorithm takes both tracking control problem and synchronization control problem into account. The numerical results suggest that the developed method is feasible and efficient in tracking control and synchronization of two chaotic CPS. (C) 2015 Elsevier B.V. All rights reserved.

关键词： big data processing Cyber-physical system Fuzzy feedback linearization Chaotic time series prediction

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