Enabling EPCIS Event Based Traceability in Healthcare Supply Chains via Automated Generation of Linked Pedigrees
Share this Session:
  Monika Solanki   Monika Solanki
Senior Research Fellow
Aston University
http://windermere.aston.ac.uk/~monika/publication.html
 


 

Wednesday, August 20, 2014
04:15 PM - 05:00 PM

Level:  Technical - Intermediate


In this presentation we show how event processing over semantically annotated streams of EPCIS events can be exploited, for implementing tracing and tracking of products through the automated generation of linked pedigrees. In our abstraction, events are encoded in EEM(EPCIS Event Model) as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose algorithms that operate over streams of RDF annotated EPCIS events to generate linked pedigrees We show how certain critical integrity constraints in supply chains such as counterfeit product detection and hierarchical containment inference can be implicitly validated using our pedigree generation algorithm. We exemplify our approach on an abstraction of the pharmaceuticals supply chain.

  • EEM(EPCIS Event Model) enables the sharing and semantic interpretation of EPCIS event data.
  • Linked pedigrees are datasets described and accessed using linked data principles.
  • Linked pedigrees facilitate the interlinking of a variety of related and relevant data, i.e., product master data with event data.
  • The curating of linked pedigrees is based on a domain independent data model for the sharing of knowledge among Semantic Web/Linked data aware supply chain systems deployed for tracking, tracing and data capture.
  • Linked pedigree generation algorithm exploits stream processing over continuous streams of semantically interlinked EPCIS event datasets.


Dr Monika Solanki is a Senior Research Fellow in the Operations and Information Management academic group at Aston Business School. She has a Ph.D. in Computer Science. Her current research focuses on developing and implementing stream processing algorithms and models for tracking and tracing in supply chains, using event based pedigrees over GS1’s EPCIS standards, Semantic Web standards and Linked Data technologies. Dr Solanki has extensive experience in designing and developing ontological domain models, curating linked datasets, implementing supporting Semantic Web applications and Web services for a number of interdisciplinary projects spanning the boundaries of Computer Science, Bioenergy, Archaeology and Plant Biology. She has published several papers in international conferences, workshops and journals, proposing methodologies, formal models and concrete implementation infrastructures for building Semantic Web and linked data applications.


   
Close Window