Ahmed A. Metwally, F. Soldo, Matt Paduano, Meenal Chhabra
{"title":"Large-Scale Network Traffic Analysis for Estimating the Size of IP Addresses and Detecting Traffic Anomalies","authors":"Ahmed A. Metwally, F. Soldo, Matt Paduano, Meenal Chhabra","doi":"10.1201/b17112-15","DOIUrl":"https://doi.org/10.1201/b17112-15","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"32 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":"114945775","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}
{"title":"Algebraic Optimization of RDF Graph Pattern Queries on MapReduce","authors":"Kemafor Anyanwu, P. Ravindra, Hyeongsik Kim","doi":"10.1201/b17112-7","DOIUrl":"https://doi.org/10.1201/b17112-7","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"47 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":"115039707","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}
{"title":"Distributed Programming for the Cloud: Models, Challenges, and Analytics Engines","authors":"Mohammad Hammoud, M. Sakr","doi":"10.1201/b17112-2","DOIUrl":"https://doi.org/10.1201/b17112-2","url":null,"abstract":"","PeriodicalId":448182,"journal":{"name":"Large Scale and Big Data","volume":"12 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":"115843201","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}
{"title":"CloudDB AutoAdmin: A Consumer-Centric Framework for SLA Management of Virtualized Database Servers","authors":"S. Sakr, Liang Zhao, Anna Liu","doi":"10.1201/b17112-12","DOIUrl":"https://doi.org/10.1201/b17112-12","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":"124134965","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}
{"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}
{"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}
In information technology, Big Data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data-processing applications. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared with separate smaller sets with the same total amount of data. Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genetics, complex physics simulations, and environmental research. Wireless technology-based automated data gathering from the large environmental sensor networks have increased the quantity of sensor data available for analysis and sensor informatics. Next-generation environmental monitoring, natural resource management, and agricultural decision support systems are becoming heavily dependent on very large scale multiple sensor network deployments, massive-scale accumulation, harmonization, web-based Big Data integration and interpretation of Big Data. With large amount of the data availability, the complexity of data has also increased hence regular maintenance of large-scale sensor are becoming a difficult challenge. Uncertainty factors in the environmental monitoring processes are more evident than before due to current technological transparency achieved by most recent advanced communication technologies. The other challenges include capture, storage, search, sharing, analysis, and visualization. Data availability from a particular environmental sensor web is often very limited and data quality is subsequently very poor. This practical limitation could be due to difficult geographical location of the sensor node or sensor station, extreme environmental conditions, communication network failure, and lastly technical failure of the sensor node. Data uncertainty from a sensor network makes the network unreliable and inefficient. This inefficiency leads to failure of natural resource management systems such as agricultural water resource management, weather forecast, crop management including irrigation scheduling and natural resource-based crop business model systems. The ultimate challenge in environmental forecasting and decision support systems, is to overcome the data uncertainty and make the derived output more accurate. It is evident that there is a need to capture and integrate environmental knowledge from various independent sources including sensor networks, individual sensory system, large-scale environmental simulation models, and historical environmental data for each of the independent sources). It is not good enough to produce efficient decision support system using a single data source. So there is an urgent requirement for on demand complementary knowledge integration where different sources of environmental sensor data could be used to complement each other automatically.
{"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}
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
{"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}
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}
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