Lsane: Collaborative validation and enrichment of heterogeneous observation streams
Open accessAbstract
The increasing amount of publicly available data streams of environmental observation stations opens up new opportunities: domain experts are provided with an extensive amount of observations covering large areas with high density of environmental sensors, which could hardly ever be provided by a single organization. However, these opportunities come at the cost of new challenges regarding trustworthiness and comparability of such observations. In this paper, we address the challenges of semantic validation and enrichment of heterogeneous observation streams by exploiting collaboratively created and curated annotations. For this purpose, we introduce and discuss the Linked Stream Annotation Engine (LSane) to validate observation messages from heterogeneous sensors. We enrich these observation messages with provenance information derived from annotations. We present an implementation of LSane with messages from public and private environmental observation stations, which are mapped to explicit semantics, and validate and enrich the mapped messages based on annotations from the LSane collaboration platform.
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Semantically enriched IoT data quality validation proposed by [3] is limited to an RDF graph based on SHACL and does not specifically relate to missing or incomplete IoT streams.
“Semantically enriched IoT data quality validation was proposed by [3].However, the validation employed is limited to an RDF graph based on SHACL (https://www.w3.org/TR/shacl/ (accessed on 10 April 2025)) and does not specifically relate to missing or incomplete IoT streams.”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [3]
The notion of trust can be considered a measure of the IoT data quality, apart from its broader use in the context of data privacy and security.
“Similarly, it is now becoming evident that the notion of trust can be considered a measure of the IoT data quality [3,4], apart from its broader use in the context of data privacy and security.”
From Semantic-Driven Approach for Validation of IoT Streaming Data in Trustable Smart City Decision-Making and Monitoring Systems (2025) · cites this paper as [3]
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5D-IoT, a semantic web based framework for assessing IoT data quality
Mante, S., Hernandez, N., Hussain, A. M., Chaudhari, S., Gangadharan, D., & Monteil, T. (2022) · Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing · doi:10.1145/3477314.3507234 · Cited by 5 · see citing works
Bamgboye, O., Liu, X., Cruickshank, P., & Liu, Q. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9040108 · Cited by 4 · see citing works
“Some of the dimensions to the indicators include timeliness, completeness, redundancy, accuracy, etc. Semantically enriched IoT data quality validation was proposed by [3].”
- Authors
- Frank, M. T., Bader, S., Simko, V., & Zander, S.
- Affiliations
- FZI Research Center for Information Technology (DE)
- Fraunhofer Institute for Intelligent Analysis and Information Systems (DE)
- Darmstadt University of Applied Sciences (DE)
- ORCID
- Bader, Sebastian
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 137
- Pages
- 235-241
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2896245592
- Cited by
- 4
- References
- 10
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.20
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- heterogeneous sensor data, semantic validation, data enrichment, provenance, collaborative annotation, environmental sensor networks, data interoperability
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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