Abstract
The Hazard Analysis and Critical Control Point (HACCP) approach is widely used to develop food safety procedures in restaurants and other food related businesses. IoT devices could be deployed in this context to automate monitoring of critical control points (such as the maximum storage temperature of raw meat). However, such sensor infrastructures will result in the generation of significant amounts
of data as well as associated meta-data describing the context for these readings. In this paper, we demonstrate how streams of semantically annotated sensor data can be automatically transformed into concise records describing significant
events required to check compliance of business operations against HACCP-based food safety rules.
of data as well as associated meta-data describing the context for these readings. In this paper, we demonstrate how streams of semantically annotated sensor data can be automatically transformed into concise records describing significant
events required to check compliance of business operations against HACCP-based food safety rules.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Semantics 2016 |
| Publisher | CEUR-WS |
| Pages | 1-4 |
| Number of pages | 4 |
| Volume | 1695 |
| Publication status | Published - 30 Sept 2016 |
| Event | Semantics 2016: 12th Joint International Conference on Semantic Systems - Liepzig, Germany Duration: 12 Sept 2016 → 15 Sept 2016 Conference number: 124177 |
Conference
| Conference | Semantics 2016 |
|---|---|
| Country/Territory | Germany |
| City | Liepzig |
| Period | 12/09/16 → 15/09/16 |
| Other | 12th Joint International Conference on Semantic Systems, SEMANTiCS 2016 and the 1st International Workshop on Semantic Change and Evolving Semantics, SuCCESS 2016; Leipzig; Germany; 12 September 2016 through 15 September 2016; Code 124177 |
Keywords
- internet of things
- provenance
- semantic web
- food safety