计算机科学
云计算
SPARK(编程语言)
时间序列
分析
系列(地层学)
低延迟(资本市场)
数据管理
数据挖掘
数据库
操作系统
计算机网络
生物
机器学习
古生物学
程序设计语言
作者
Chen Wang,Xiangdong Huang,Jialin Qiao,Tingting Jiang,Lei Rui,Jinrui Zhang,Rong Kang,Julian Feinauer,Kevin A. McGrail,Peng Wang,Diaohan Luo,Jun Yuan,Jianmin Wang,Jun Sun
标识
DOI:10.14778/3415478.3415504
摘要
The amount of time-series data that is generated has exploded due to the growing popularity of Internet of Things (IoT) devices and applications. These applications require efficient management of the time-series data on both the edge and cloud side that support high throughput ingestion, low latency query and advanced time series analysis. In this demonstration, we present Apache IoTDB managing time-series data to enable new classes of IoT applications. IoTDB has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and time-series database with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, down-sampling and sub-sequence similarity search. An edge-to-cloud time-series data management application is chosen to demonstrate how IoTDB handles time-series data in real-time and supports advanced analytics by integrating with Hadoop and Spark. An end-to-end IoT data management solution is shown by integrating IoTDB with PLC4x, Calcite, and Grafana.
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