Semantically Enhanced Time Series Databases in IoT-Edge-Cloud Infrastructure
Shuai Zhang, Wenxi Zeng, I-Ling Yen, Farokh B. Bastani

TL;DR
This paper introduces a semantic model and a tool suite called SE-TSDB for enhancing time series databases in IoT-Edge-Cloud systems, enabling semantic-based data retrieval and automatic data stream derivation.
Contribution
The paper presents a novel semantic model for IoT monitoring data streams and a tool suite that integrates with existing TSDBs to improve data retrieval and management.
Findings
Semantic model enables automatic derivation of data streams.
SE-TSDB improves data retrieval without prior knowledge of data location.
Enhanced semantics facilitate timely data analysis in large-scale IoT systems.
Abstract
Many IoT systems are data intensive and are for the purpose of monitoring for fault detection and diagnosis of critical systems. A large volume of data steadily come out of a large number of sensors in the monitoring system. Thus, we need to consider how to store and manage these data. Existing time series databases (TSDBs) can be used for monitoring data storage, but they do not have good models for describing the data streams stored in the database. In this paper, we develop a semantic model for the specification of the monitoring data streams (time series data) in terms of which sensor generated the data stream, which metric of which entity the sensor is monitoring, what is the relation of the entity to other entities in the system, which measurement unit is used for the data stream, etc. We have also developed a tool suite, SE-TSDB, that can run on top of existing TSDBs to help…
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Taxonomy
TopicsCloud Computing and Resource Management · Scientific Computing and Data Management · Software System Performance and Reliability
