Time Series Management Systems: A Survey

TL;DR

This survey comprehensively analyzes and classifies existing Time Series Management Systems (TSMSs), highlighting their architectures, functionalities, and research directions to guide future developments in handling large-scale time series data.

Contribution

It provides a detailed classification and overview of TSMSs, including their architectures, functionalities, and research trends, which was lacking in prior literature.

Findings

Classified TSMSs into architecture-based categories

Highlighted capabilities in stream processing and approximate query processing

Outlined future research directions for TSMS development

Abstract

The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.