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Multivariate memory vectorization technique to facilitate intelligent caching in time-series databases

机译:多元内存向量化技术可促进时间序列数据库中的智能缓存

摘要

The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.
机译:公开的实施例涉及一种在时序数据库系统中缓存时序数据的系统。在操作过程中,系统接收时间序列数据,其中时间序列数据包括从传感器读数中获取的一组信号中的一系列观测值。接下来,系统对时间序列数据执行多变量存储矢量化(MMV)操作,该操作选择时间序列数据中观察值的子集,该子集表示单个和多变量信号的时间序列数据的基础结构,包括时间序列数据。然后,系统对选定的时间序列数据子集执行几何压缩老化(GAC)操作。在随后处理涉及时间序列数据的查询时,该系统:在时间序列数据库系统的内存数据库高速缓存中高速缓存时间序列数据的选定子集;并从内存数据库高速缓存访​​问选定的时间序列数据子集。

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