{"title":"面向地震大数据处理的Hadoop联合安全平台","authors":"Shiladitya Bhattacharjee, Lukman A. B. Rahim","doi":"10.1109/ICCOINS49721.2021.9497221","DOIUrl":null,"url":null,"abstract":"The importance of seismic big data exploration, especially in gas and oil industries, is indispensable. The processing of such complex data becomes more critical when its size is extremely large. These days the dispose of seismic big data over the network is notably common. Hence, the security of this huge complex data is equally important during its transportation over an insecure channel. Consequently, the application of any security algorithm on complex big seismic data makes it impractical for adopting it in any industry. Numerous researches have been conducted to resolve these issues. However, any unified solution has not been proclaimed by the exiting related studies. Therefore, this research work affirms a unique unified platform that uses the integration of Hadoop and Hive for parallel processing and advanced indexing for faster execution of large complex data. At the same time, it uses a low complex elliptic curve cryptography (ECC) to ensure data security in terms of data confidentiality and integrity. The result shows that the proposed integrated technique offers higher time efficiency in terms of producing higher Throughput than other security combinations. It further shows it produces a low percentage of Data Loss and higher Entropy Value as well as Avalanche Effect which justifies its ability to offer higher data confidentiality and integrity.","PeriodicalId":245662,"journal":{"name":"2021 International Conference on Computer & Information Sciences (ICCOINS)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Hadoop Allied Security Platform for Seismic Big Data Processing\",\"authors\":\"Shiladitya Bhattacharjee, Lukman A. B. Rahim\",\"doi\":\"10.1109/ICCOINS49721.2021.9497221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The importance of seismic big data exploration, especially in gas and oil industries, is indispensable. The processing of such complex data becomes more critical when its size is extremely large. These days the dispose of seismic big data over the network is notably common. Hence, the security of this huge complex data is equally important during its transportation over an insecure channel. Consequently, the application of any security algorithm on complex big seismic data makes it impractical for adopting it in any industry. Numerous researches have been conducted to resolve these issues. However, any unified solution has not been proclaimed by the exiting related studies. Therefore, this research work affirms a unique unified platform that uses the integration of Hadoop and Hive for parallel processing and advanced indexing for faster execution of large complex data. At the same time, it uses a low complex elliptic curve cryptography (ECC) to ensure data security in terms of data confidentiality and integrity. The result shows that the proposed integrated technique offers higher time efficiency in terms of producing higher Throughput than other security combinations. It further shows it produces a low percentage of Data Loss and higher Entropy Value as well as Avalanche Effect which justifies its ability to offer higher data confidentiality and integrity.\",\"PeriodicalId\":245662,\"journal\":{\"name\":\"2021 International Conference on Computer & Information Sciences (ICCOINS)\",\"volume\":\"173 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computer & Information Sciences (ICCOINS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCOINS49721.2021.9497221\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer & Information Sciences (ICCOINS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCOINS49721.2021.9497221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Hadoop Allied Security Platform for Seismic Big Data Processing
The importance of seismic big data exploration, especially in gas and oil industries, is indispensable. The processing of such complex data becomes more critical when its size is extremely large. These days the dispose of seismic big data over the network is notably common. Hence, the security of this huge complex data is equally important during its transportation over an insecure channel. Consequently, the application of any security algorithm on complex big seismic data makes it impractical for adopting it in any industry. Numerous researches have been conducted to resolve these issues. However, any unified solution has not been proclaimed by the exiting related studies. Therefore, this research work affirms a unique unified platform that uses the integration of Hadoop and Hive for parallel processing and advanced indexing for faster execution of large complex data. At the same time, it uses a low complex elliptic curve cryptography (ECC) to ensure data security in terms of data confidentiality and integrity. The result shows that the proposed integrated technique offers higher time efficiency in terms of producing higher Throughput than other security combinations. It further shows it produces a low percentage of Data Loss and higher Entropy Value as well as Avalanche Effect which justifies its ability to offer higher data confidentiality and integrity.