2017 IEEE 13th International Conference on e-Science (e-Science)最新文献_第3页

Massive OceanColor Data Processing and Analysis System: TuPiX-OC 海量OceanColor数据处理与分析系统:TuPiX-OC

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.66

Jung-Ho Um, Sunggeun Han, Hyunwoo Kim, Kyongseok Park

{"title":"Massive OceanColor Data Processing and Analysis System: TuPiX-OC","authors":"Jung-Ho Um, Sunggeun Han, Hyunwoo Kim, Kyongseok Park","doi":"10.1109/eScience.2017.66","DOIUrl":"https://doi.org/10.1109/eScience.2017.66","url":null,"abstract":"Satellite image data generated from remote sensors around the world have different resolutions and are processed at varying levels from Level 0 to Level 3, with each level containing vast amounts of information. Due to the problem of data size, many ocean science researchers use L3 images, which have a spatial resolution of 4 km or 9 km. However, in order to overcome problems such as red tides or to analyze the marine ecosystem based on ocean color satellite research, researchers must generate data by changing the parameters of images at various levels. There is also a need for immediate access to satellite image information using analytical and visualization tools. Considering those requirements, TuPiX-OC (Turning PiXels into knowledge and science-OceanColor) provides an environment to design and execute algorithms for data processing and analysis of satellite image data by data type. TuPix-OC, which has a distributed architecture, is an analytical platform that supports data import, level conversion, DB integration, analysis and processing, and visualization. TuPiX-OC stores satellite data in a massive storage device, and provides an online platform for satellite data conversion/analysis/ visualization. For satellite data processing, TuPiX-OC converts NASA-provided binary files into files that can be analyzed by users. Moreover, TuPiX-OC includes various algorithms for satellite data selection and utilization of satellite images. Preliminary Experiments of TuPiX-OC's satellite image data processing capability showed that it was able to process 35 times as many images as the open source software SeaDAS.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133150808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Review of Privacy and Consent Management in Healthcare: A Focus on Emerging Data Sources 医疗保健中的隐私和同意管理综述:关注新兴数据源

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.84

M. R. Asghar, T. Lee, M. Baig, E. Ullah, G. Russello, G. Dobbie

引用次数: 25

A Novel Data Mining Testbed for User Centred Modelling and Personalisation of Digital Library Services 一种以用户为中心的数字图书馆服务建模与个性化的新型数据挖掘试验台

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.58

M. Almaghrabi, G. Chetty

引用次数: 1

Online Decision-Making Using Edge Resources for Content-Driven Stream Processing 使用边缘资源进行内容驱动流处理的在线决策

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.52

E. G. Renart, Daniel Balouek-Thomert, Xuan Hu, J. Gong, M. Parashar

{"title":"Online Decision-Making Using Edge Resources for Content-Driven Stream Processing","authors":"E. G. Renart, Daniel Balouek-Thomert, Xuan Hu, J. Gong, M. Parashar","doi":"10.1109/eScience.2017.52","DOIUrl":"https://doi.org/10.1109/eScience.2017.52","url":null,"abstract":"The Internet of Things (IoT) describes the emerging paradigm that connects sensors, often located at the edge of the network, to stream processing engines located at the core of the network to enable online data-driven monitoring, management, and control. As IoT applications require increasing volumes of streaming data to be processed by complex workflows in a timely manner, it is becoming important to also leverage resources closer to the edge. Furthermore, the topology of these workflows and where theyare executed is determined not only by application objectives and available resources, but also by the content of the data streams, however, current stream processing engines do not provide this flexibility. In this paper, we present a programming framework that enables applications to specify data-driven, location- and resource-aware processing of data streams. Specifically, it provides abstractions for specifying where and how a data stream is processed based on its content, spatial and temporal characteristics. We also present an implementation of the framework using an event-driven runtime, where events are associatively described. Finally, we demonstrate the effectiveness of the solution by an evaluation of scalability and performance using a disaster response application usecase.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116385257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Adapting Enterprise Architecture for eScience 为eScience调整企业架构

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.55

Richard Palmer, Kheeran Dharmawardena, H. Holewa

引用次数: 0

Mass Digitization of Individual Pinned Insects Using Conveyor-Driven Imaging 利用传送带驱动成像技术对单个被钉昆虫进行大规模数字化

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.85

Riitta Tegelberg, J. Kahanpaa, Janne Karppinen, T. Mononen, Zhe Wu, H. Saarenmaa

引用次数: 9

Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters 开发一个志愿者计算项目来进化卷积神经网络及其超参数

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.14

Travis Desell

{"title":"Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters","authors":"Travis Desell","doi":"10.1109/eScience.2017.14","DOIUrl":"https://doi.org/10.1109/eScience.2017.14","url":null,"abstract":"This work presents improvements to a neuroevolution algorithm called Evolutionary eXploration of Augmenting Convolutional Topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). While EXACT has multithreaded and parallel implementations, it has also been implemented as part of a volunteer computing project at the Citizen Science Grid to provide truly large scale computing resources through over 5,500 volunteered computers. Improvements include the development of a new mutation operator, which increased the evolution rate by over an order of magnitude and was also shown to be significantly more reliable in generating new CNNs than the traditional method. Further, EXACT has been extended with a simplex hyperparameter optimization (SHO) method which allows for the co-evolution of hyperparameters, simplifying the task of their selection while generating smaller CNNs with similar predictive ability to those generated with fixed hyperparameters. Lastly, the backpropagation method has been updated with batch normalization and dropout. Compared to previous work, which only achieved prediction rates of 98.32% on the MNIST handwritten digits testing data after 60,000 evolved CNNs, these new advances allowed EXACT to achieve prediction rates of 99.43% within only 12,500 evolved CNNs - rates which are comparable to some of the best human designed CNNs.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121543769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Supporting Data-Driven Workflows Enabled by Large Scale Observatories 支持大规模天文台启用的数据驱动工作流

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.95

A. Zamani, Moustafa AbdelBaky, Daniel Balouek-Thomert, I. Rodero, M. Parashar

引用次数: 8

SELFIE: Self-Aware Information Extraction from Digitized Biocollections 自拍:从数字化生物馆藏中提取自我意识信息

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.19

I. Alzuru, Andréa M. Matsunaga, Maurício O. Tsugawa, J. Fortes

{"title":"SELFIE: Self-Aware Information Extraction from Digitized Biocollections","authors":"I. Alzuru, Andréa M. Matsunaga, Maurício O. Tsugawa, J. Fortes","doi":"10.1109/eScience.2017.19","DOIUrl":"https://doi.org/10.1109/eScience.2017.19","url":null,"abstract":"Biological collections store information with broad societal and environmental impact. In the last 15 years, after worldwide investments and crowdsourcing efforts, 25% of the collected specimens have been digitized; a process that includes the imaging of text attached to specimens and subsequent extraction of information from the resulting image. This information extraction (IE) process is complex, thus slow and typically involving human tasks. We propose a hybrid (Human-Machine) information extraction model that efficiently uses resources of different cost (machines, volunteers and/or experts) and speeds up the biocollections' digitization process, while striving to maintain the same quality as human-only IE processes. In the proposed model, called SELFIE, self-aware IE processes determine whether their output quality is satisfactory. If the quality is unsatisfactory, additional or alternative processes that yield higher quality output at higher cost are triggered. The effectiveness of this model is demonstrated by three SELFIE workflows for the extraction of Darwin-core terms from specimens' images. Compared to the traditional human-driven IE approach, SELFIE workflows showed, on average, a reduction of 27% in the information-capture time and a decrease of 32% in the required number of humans and their associated cost, while the quality of the results was negligibly reduced by 0.27%.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"423 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131707838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Task-Based Budget Distribution Strategies for Scientific Workflows with Coarse-Grained Billing Periods in IaaS Clouds IaaS云中具有粗粒度计费周期的科学工作流的基于任务的预算分配策略

2017 IEEE 13th International Conference on e-Science (e-Science) Pub Date : 2017-10-01 DOI: 10.1109/eScience.2017.25

M. Hilman, M. A. Rodriguez, R. Buyya

{"title":"Task-Based Budget Distribution Strategies for Scientific Workflows with Coarse-Grained Billing Periods in IaaS Clouds","authors":"M. Hilman, M. A. Rodriguez, R. Buyya","doi":"10.1109/eScience.2017.25","DOIUrl":"https://doi.org/10.1109/eScience.2017.25","url":null,"abstract":"The use of cloud computing, particularly of Infrastructure as a Service clouds, for the execution of largescale scientific workflows has been a topic of interest in recent years. These environments offer on-demand access to all of the infrastructure required for the deployment of workflows, allowing users to pay only for what they use. This leads to schedulers having to find a trade-off between two conflicting quality of service requirements: time and cost. The majority of research in this area has focused on developing scheduling algorithms that have as objective minimizing the infrastructure cost while meeting a deadline constraint. Few algorithms, however, have addressed the problem of minimizing the execution time of the workflow while meeting a budget constraint. This paper focuses on the latter case. We propose a budget-distribution algorithm that assigns a portion of the overall workflow budget to the individual tasks. This task-level budget then guides the dynamic scheduling process and is continuously refined to reflect any unexpected costs. When compared to the state-of-the-art algorithm, the performance evaluation results demonstrate that in 88% of the cases, our proposal achieves equal or better performance in terms of meeting the budget constraint and achieves lower execution times in 84% of the cases.","PeriodicalId":137652,"journal":{"name":"2017 IEEE 13th International Conference on e-Science (e-Science)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8