{"title":"云上的大数据分析综述","authors":"Rayan Dasoriya","doi":"10.1109/ICCE-ASIA.2017.8307833","DOIUrl":null,"url":null,"abstract":"As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, known as Big Data, is now used by various organizations to extract valuable information which can be analysed computationally to reveal patterns, trends and associations revealing the human interaction and behaviour for making various industrial decisions. Due to the large volume of data, it is stored in the cloud and all the analysis is done over Big Data over cloud since it is not possible for traditional systems to store such large amount of data. But the data must be optimized, integrated, secured and visualized to make any effective decision. Analysing of the large volume of data is not beneficial always unless it is analysed properly. The existing techniques are insufficient to analyse the Big Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in. Using these services, people become widely vulnerable to exposure. That is, it becomes possible to collect more data than it is required which may lead to data leakage and hence security concerns. Results can be analysed in a better way by visuals like graphs, charts etc. and it helps in faster decision making. It also includes MapReduce Algorithm which will help in maintaining a log of user's activities in the cloud and show the frequently used services. This paper proposes a scheme for making Big Data Analytics more accurate, efficient and beneficial.","PeriodicalId":202045,"journal":{"name":"2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A review of big data analytics over cloud\",\"authors\":\"Rayan Dasoriya\",\"doi\":\"10.1109/ICCE-ASIA.2017.8307833\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, known as Big Data, is now used by various organizations to extract valuable information which can be analysed computationally to reveal patterns, trends and associations revealing the human interaction and behaviour for making various industrial decisions. Due to the large volume of data, it is stored in the cloud and all the analysis is done over Big Data over cloud since it is not possible for traditional systems to store such large amount of data. But the data must be optimized, integrated, secured and visualized to make any effective decision. Analysing of the large volume of data is not beneficial always unless it is analysed properly. The existing techniques are insufficient to analyse the Big Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in. Using these services, people become widely vulnerable to exposure. That is, it becomes possible to collect more data than it is required which may lead to data leakage and hence security concerns. Results can be analysed in a better way by visuals like graphs, charts etc. and it helps in faster decision making. It also includes MapReduce Algorithm which will help in maintaining a log of user's activities in the cloud and show the frequently used services. This paper proposes a scheme for making Big Data Analytics more accurate, efficient and beneficial.\",\"PeriodicalId\":202045,\"journal\":{\"name\":\"2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-ASIA.2017.8307833\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-ASIA.2017.8307833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, known as Big Data, is now used by various organizations to extract valuable information which can be analysed computationally to reveal patterns, trends and associations revealing the human interaction and behaviour for making various industrial decisions. Due to the large volume of data, it is stored in the cloud and all the analysis is done over Big Data over cloud since it is not possible for traditional systems to store such large amount of data. But the data must be optimized, integrated, secured and visualized to make any effective decision. Analysing of the large volume of data is not beneficial always unless it is analysed properly. The existing techniques are insufficient to analyse the Big Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in. Using these services, people become widely vulnerable to exposure. That is, it becomes possible to collect more data than it is required which may lead to data leakage and hence security concerns. Results can be analysed in a better way by visuals like graphs, charts etc. and it helps in faster decision making. It also includes MapReduce Algorithm which will help in maintaining a log of user's activities in the cloud and show the frequently used services. This paper proposes a scheme for making Big Data Analytics more accurate, efficient and beneficial.