{"title":"Data Science","authors":"Rathinaraja Jeyaraj, G. Pugalendhi, Anand Paul","doi":"10.1201/9780429321733-7","DOIUrl":"https://doi.org/10.1201/9780429321733-7","url":null,"abstract":"and The fundamental techniques related to data acquisition, data statistical modeling, experimental design, feature engineering, and modeling with machine learning. It explores the problems that arise in different ways of performing those tasks, the fairness and bias of machine learning models, data visualizations, and user interfaces. In addition, covers anonymization and deanonymization, conceptions of privacy from a number of perspectives","PeriodicalId":246921,"journal":{"name":"Big Data with Hadoop MapReduce","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125792139","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":"Hadoop Ecosystem","authors":"Rathinaraja Jeyaraj, G. Pugalendhi, Anand Paul","doi":"10.1201/9780429321733-4","DOIUrl":"https://doi.org/10.1201/9780429321733-4","url":null,"abstract":"","PeriodicalId":246921,"journal":{"name":"Big Data with Hadoop MapReduce","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152448","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":"Big Data","authors":"Rathinaraja Jeyaraj, G. Pugalendhi, Anand Paul","doi":"10.1201/9780429321733-1","DOIUrl":"https://doi.org/10.1201/9780429321733-1","url":null,"abstract":"During the COVID-19 outbreaking, China’s lock-down measures have played an outstanding role in epidemic prevention; many other countries have followed similar practices. The policy of social alienation and community containment was executed to reduce civic activities, which brings up numerous economic losses. It has become an urgent task for these countries to open-up, while the epidemic has almost under control. However, it still lacks sufficient literature to set appropriate open-up schemes that strike a balance between open-up risk and lock-down cost. Big data collection and analysis, which play an increasingly important role in urban governance, provide a useful tool for solving the problem. This paper explores the influence of open-up granularity on both the open-up risk and the lock-down cost. It proposes an SEIR-CAL model considering the effect of asymptomatic patients based on propagation dynamics, and offered a model to calculate the lock-down cost based on the lock-down population. A simulation experiment is then carried out based on the mass actual data of Wuhan City to explore the influence of open-up granularity. Finally, this paper proposed the evaluation score (ES) to comprehensively measure schemes with different costs and risks. The experiments suggest that when released under the non-epidemic situation, the open-up scheme with the granularity refined to the block has the optimal ES. Results indicated that the fine-grained open-up scheme could significantly reduce the lock-down cost with a relatively low open-up risk increase.","PeriodicalId":246921,"journal":{"name":"Big Data with Hadoop MapReduce","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744091","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":"Hadoop Framework","authors":"Rathinaraja Jeyaraj, G. Pugalendhi, Anand Paul","doi":"10.1201/9780429321733-2","DOIUrl":"https://doi.org/10.1201/9780429321733-2","url":null,"abstract":"","PeriodicalId":246921,"journal":{"name":"Big Data with Hadoop MapReduce","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122984748","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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