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…
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