STSDB:用于智慧城市查询处理的时空传感器数据库

Utsav Vyas, P. Panchal, Mayank Patel, Minal Bhise
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引用次数: 1

摘要

现代世界的智能设备配备了几个传感器,这些传感器可以不断地产生数据。有效地管理和分析这些数据是当前传感器世界的关键需求。目前的应用需要实时分析过去的传感器数据来进行决策。本工作的目的是有效地处理传感器数据的时空查询。时空传感器索引STSI有助于管理传感器细节,并导致更快的查询处理。已经考虑的查询类型有:1)时空旅行,2)时间聚集,3)时间旅行,4)时空聚集。通过在HBase中加入STSI索引,构建了时空传感器数据库STSDB。在数据插入时间DIT和查询执行时间QET两个参数上比较了STSDB与HBase的性能。与HBase相比,STSDB的DIT几乎相同。而所有四种查询类型的QET平均值显示,STSDB比HBase提高了49%。对于HBase和STSDB中的扩展数据,这两个性能参数继续显示类似的趋势。本研究使用智慧城市数据演示了STSDB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
STSDB: spatio-temporal sensor database for smart city query processing
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.
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