A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

Monika Solanki*, Christopher Brewster

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication

Abstract

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management
Subtitle of host publication19th international conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014. Proceedings
EditorsKrzysztof Janowicz, Stefan Schlobach, Patrick Lambrix, Eero Hyvönen
Place of PublicationChem (CH)
PublisherSpringer
Pages503-518
Number of pages16
ISBN (Electronic)978-3-319-13704-9
ISBN (Print)978-3-319-13703-2
DOIs
Publication statusPublished - 31 Dec 2014
Event19th international conference on Knowledge Engineering and Knowledge Management - Linköping, Sweden
Duration: 24 Nov 201428 Nov 2014

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume8876
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th international conference on Knowledge Engineering and Knowledge Management
Abbreviated titleEKAW 2014
Country/TerritorySweden
CityLinköping
Period24/11/1428/11/14

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