{"title":"STSDB: spatio-temporal sensor database for smart city query processing","authors":"Utsav Vyas, P. Panchal, Mayank Patel, Minal Bhise","doi":"10.1145/3288599.3296015","DOIUrl":null,"url":null,"abstract":"Modern world smart devices are equipped with several sensors which continuously generate the data. Managing and analyzing these data efficiently is a key need of the current sensor world. Present applications require real-time analysis of past sensor data for decision making. The goal of this work is to efficiently process the spatio-temporal queries for sensor data. Spatio-Temporal Sensor Index STSI helps in managing the sensor details and leads to faster query processing. The types of queries that have been considered are; 1) Spatio-Time Travel, 2) Temporal Aggregation and 3) Time Travel, 4) Spatio-temporal Aggregation. Spatio-Temporal Sensor Database STSDB is built by including STSI index in HBase. The STSDB performance is compared with HBase on two parameters Data Insertion Time DIT, and Query Execution Time QET. The DIT of STSDB is almost identical as compared to HBase. While the QET averaged over all four types of queries show 49% improvement for STSDB over HBase. Both the performance parameters continue to show similar trends for scaled data in HBase and STSDB. STSDB is demonstrated in this work using smart city data.","PeriodicalId":346177,"journal":{"name":"Proceedings of the 20th International Conference on Distributed Computing and Networking","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th International Conference on Distributed Computing and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3288599.3296015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Modern world smart devices are equipped with several sensors which continuously generate the data. Managing and analyzing these data efficiently is a key need of the current sensor world. Present applications require real-time analysis of past sensor data for decision making. The goal of this work is to efficiently process the spatio-temporal queries for sensor data. Spatio-Temporal Sensor Index STSI helps in managing the sensor details and leads to faster query processing. The types of queries that have been considered are; 1) Spatio-Time Travel, 2) Temporal Aggregation and 3) Time Travel, 4) Spatio-temporal Aggregation. Spatio-Temporal Sensor Database STSDB is built by including STSI index in HBase. The STSDB performance is compared with HBase on two parameters Data Insertion Time DIT, and Query Execution Time QET. The DIT of STSDB is almost identical as compared to HBase. While the QET averaged over all four types of queries show 49% improvement for STSDB over HBase. Both the performance parameters continue to show similar trends for scaled data in HBase and STSDB. STSDB is demonstrated in this work using smart city data.