Abstract
A time series is a sequence of chronologically ordered numerical values that reflect some process or phenomenon. Currently, one of the most topical applications related to time series processing are Industry 4.0 and Internet of Things. In these applications, the typical task is to provide intelligent control and predictive maintenance of complex machines and mechanisms that are equipped with various sensors. Such sensors have a high frequency, and in a relatively short time interval produce time series from tens of millions to billions of elements. The data obtained from the sensors is accumulated and mined to make strategic decisions. Time series processing requires specific system software that is different from the existing relational DBMS and NoSQL systems. Time series database systems should provide, on the one hand, efficient operations for adding new atomic values arriving in streaming mode, and on the other hand, efficient mining operations where time series is considered as a whole. The paper discusses the features of time series processing in comparison with data of a relational and non-relational nature, and gives formal definitions of the basic tasks of time series mining. The paper also presents an overview of three most popular modern time series database systems, namely InfuxDB, OpenTSDB, TimescaleDB.
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Schedule a callMore From: Bulletin of the South Ural State University. Series "Computational Mathematics and Software Engineering"
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