Shanshan Wu, Liang Bao, Zisheng Zhu, Fan Yi, Weizhao Chen
{"title":"Storage and retrieval of massive heterogeneous IoT data based on hybrid storage","authors":"Shanshan Wu, Liang Bao, Zisheng Zhu, Fan Yi, Weizhao Chen","doi":"10.1109/FSKD.2017.8393258","DOIUrl":null,"url":null,"abstract":"With the rapid development of the Internet of Things (IoT), the IoT is characterized by a wide variety of data sources, large scale and heterogeneous structure. But those characteristics bring great difficulties to the storage and rapid retrieval of IoT data. By considering the common attributes of IoT data, based on plug-in ideas, combined with Redis and HBase, the paper proposes a framework named HSFRH-IoT, which solves the problem of efficient storage and retrieval of massive heterogeneous IOT. Finally, the insertion and query performance of the proposed HSFRH-IoT framework is tested in detail, and the results shows that it has better performance than other RDBMS based solutions.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2017.8393258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
With the rapid development of the Internet of Things (IoT), the IoT is characterized by a wide variety of data sources, large scale and heterogeneous structure. But those characteristics bring great difficulties to the storage and rapid retrieval of IoT data. By considering the common attributes of IoT data, based on plug-in ideas, combined with Redis and HBase, the paper proposes a framework named HSFRH-IoT, which solves the problem of efficient storage and retrieval of massive heterogeneous IOT. Finally, the insertion and query performance of the proposed HSFRH-IoT framework is tested in detail, and the results shows that it has better performance than other RDBMS based solutions.