Semantically Enhanced Time Series Databases in IoT-Edge-Cloud Infrastructure

dc.contributor.authorZhang, Shuai
dc.contributor.authorZeng, Wenxi
dc.contributor.authorYen, I-Ling
dc.contributor.authorBastani, Farokh B.
dc.contributor.utdAuthorBastani, Farokh B.
dc.descriptionDue to copyright restrictions full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractMany 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 establish semantic specifications for data streams and enable semanticbased data retrievals. With our semantic model for monitoring data and our SETSDB tool suite, users can retrieve non-existing data streams that can be automatically derived from the semantics. Users can also retrieve data streams without knowing where they are. Semantic based retrieval is especially important in a largescale integrated IoT-Edge-Cloud system, because of its sheer quantity of data, its huge number of computing and IoT devices that may store the data, and the dynamics in data migration and evolution. With better data semantics, data streams can be more effectively tracked and flexibly retrieved to help with timely data analysis and control decision making anywhere and anytime. ©2019 IEEE.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationZhang, S., W. Zeng, I. -L Yen, and F. B. Bastani. 2019. "Semantically enhanced time series databases in IoT-edge-cloud infrastructure." International Symposium on High Assurance Systems Engineering, 19th: 25-32, doi: 10.1109/HASE.2019.00014
dc.publisherIEEE Computer Society
dc.rights©2019 IEEE
dc.source.journalInternational Symposium on High Assurance Systems Engineering, 19th
dc.subjectCloud computing
dc.subjectSemantics--Data processing
dc.subjectInternet of things
dc.subjectData mining
dc.subjectDecision making
dc.subjectElectronic data processing—Distributed processing
dc.subjectSearch engines
dc.subjectSystems engineering
dc.subjectTime-series analysis
dc.titleSemantically Enhanced Time Series Databases in IoT-Edge-Cloud Infrastructure


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