Large Scale and Big Data最新文献

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Large-Scale Network Traffic Analysis for Estimating the Size of IP Addresses and Detecting Traffic Anomalies 大规模网络流量分析,用于估算IP地址大小和发现流量异常
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-15
Ahmed A. Metwally, F. Soldo, Matt Paduano, Meenal Chhabra
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引用次数: 1
Algebraic Optimization of RDF Graph Pattern Queries on MapReduce MapReduce上RDF图模式查询的代数优化
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-7
Kemafor Anyanwu, P. Ravindra, Hyeongsik Kim
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引用次数: 2
Distributed Programming for the Cloud: Models, Challenges, and Analytics Engines 面向云的分布式编程:模型、挑战和分析引擎
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-2
Mohammad Hammoud, M. Sakr
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引用次数: 3
iMapReduce: Extending MapReduce for Iterative Processing iMapReduce:扩展MapReduce进行迭代处理
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-4
Yanfeng Zhang, Qixin Gao, Lixin Gao, Cuirong Wang
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引用次数: 2
CloudDB AutoAdmin: A Consumer-Centric Framework for SLA Management of Virtualized Database Servers CloudDB AutoAdmin:一个以用户为中心的虚拟化数据库服务器SLA管理框架
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-12
S. Sakr, Liang Zhao, Anna Liu
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引用次数: 2
Security in Big Data and Cloud Computing: Challenges, Solutions, and Open Problems 大数据和云计算中的安全:挑战、解决方案和开放问题
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-20
Ragib Hasan
{"title":"Security in Big Data and Cloud Computing: Challenges, Solutions, and Open Problems","authors":"Ragib Hasan","doi":"10.1201/b17112-20","DOIUrl":"https://doi.org/10.1201/b17112-20","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123898119","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: 0
An Overview of Large-Scale Stream Processing Engines 大规模流处理引擎概述
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-13
Radwa El Shawi, S. Sakr
{"title":"An Overview of Large-Scale Stream Processing Engines","authors":"Radwa El Shawi, S. Sakr","doi":"10.1201/b17112-13","DOIUrl":"https://doi.org/10.1201/b17112-13","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123718669","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: 0
Recommending Environmental Big Data Using Semantically Guided Machine Learning 推荐使用语义引导机器学习的环境大数据
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-16
R. Dutta, Ahsan Morshed, J. Aryal
{"title":"Recommending Environmental Big Data Using Semantically Guided Machine Learning","authors":"R. Dutta, Ahsan Morshed, J. Aryal","doi":"10.1201/b17112-16","DOIUrl":"https://doi.org/10.1201/b17112-16","url":null,"abstract":"In information technology, Big Data is a collection of data sets so large and complex \u0000that it becomes difficult to process using on-hand database management tools \u0000or traditional data-processing applications. The trend to larger data sets is \u0000due to the additional information derivable from analysis of a single large set of \u0000related data, as compared with separate smaller sets with the same total amount of \u0000data. Scientists regularly encounter limitations due to large data sets \u0000in many areas, including meteorology, genetics, complex physics simulations, and \u0000environmental research. Wireless technology-based automated data gathering \u0000from the large environmental sensor networks have increased the quantity of sensor \u0000data available for analysis and sensor informatics. Next-generation environmental \u0000monitoring, natural resource management, and agricultural decision support systems \u0000are becoming heavily dependent on very large scale multiple sensor network deployments, \u0000massive-scale accumulation, harmonization, web-based Big Data integration \u0000and interpretation of Big Data. With large amount of the data availability, the complexity \u0000of data has also increased hence regular maintenance of large-scale sensor \u0000are becoming a difficult challenge. Uncertainty factors in the environmental monitoring \u0000processes are more evident than before due to current technological transparency \u0000achieved by most recent advanced communication technologies. \u0000The other challenges include capture, storage, search, sharing, analysis, and visualization. \u0000Data availability from a particular environmental sensor web is often very \u0000limited and data quality is subsequently very poor. This practical limitation could be \u0000due to difficult geographical location of the sensor node or sensor station, extreme \u0000environmental conditions, communication network failure, and lastly technical failure \u0000of the sensor node. Data uncertainty from a sensor network makes the network \u0000unreliable and inefficient. This inefficiency leads to failure of natural resource management \u0000systems such as agricultural water resource management, weather forecast, \u0000crop management including irrigation scheduling and natural resource-based \u0000crop business model systems. The ultimate challenge in environmental forecasting \u0000and decision support systems, is to overcome the data uncertainty and make the \u0000derived output more accurate. It is evident that there is a need to capture and integrate \u0000environmental knowledge from various independent sources including sensor \u0000networks, individual sensory system, large-scale environmental simulation models, \u0000and historical environmental data for each of the independent \u0000sources). It is not good enough to produce efficient decision support system using a \u0000single data source. So there is an urgent requirement for on demand complementary \u0000knowledge integration where different sources of environmental sensor data could \u0000be used to complement each other automatically.","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131327746","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
Toward Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud 面向云中经济和绿色MapReduce计算的最佳资源配置
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-18
Keke Chen, Shumin Guo, James Powers, F. Tian
{"title":"Toward Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud","authors":"Keke Chen, Shumin Guo, James Powers, F. Tian","doi":"10.1201/b17112-18","DOIUrl":"https://doi.org/10.1201/b17112-18","url":null,"abstract":"Running MapReduce programs in the cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge or job finish time for a specific job? An important step towards this ultimate goal is modeling the cost of MapReduce program. In this chapter, we study the whole process of MapReduce processing and build 1","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129634738","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
Network Performance Aware Graph Partitioning for Large Graph Processing Systems in the Cloud 云中大型图处理系统的网络性能感知图分区
Large Scale and Big Data Pub Date : 1900-01-01 DOI: 10.1201/b17112-8
Rishan Chen, Xuetian Weng, Bingsheng He, Byron Choi, Mao Yang
{"title":"Network Performance Aware Graph Partitioning for Large Graph Processing Systems in the Cloud","authors":"Rishan Chen, Xuetian Weng, Bingsheng He, Byron Choi, Mao Yang","doi":"10.1201/b17112-8","DOIUrl":"https://doi.org/10.1201/b17112-8","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117187809","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: 2
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