EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

OntoPedigree:modelling pedigrees for traceability in supply chains

The sharing of product and process information plays a central role in coordinating supply chains operations and is a key driver for their success. "Linked pedigrees" - linked datasets, that encapsulate event based traceability information of artifacts as they move along the supply chain, provide a scalable mechanism to record and facilitate the sharing of track and trace knowledge among supply chain partners. In this paper we present "OntoPedigree" a content ontology design pattern for the representation of linked pedigrees, that can be specialised and extended to define domain specific traceability ontologies. Events captured within the pedigrees are specified using EPCIS - a GS1 standard for the specification of traceability information within and across enterprises, while certification information is described using PROV - a vocabulary for modelling provenance of resources. We exemplify the utility of OntoPedigree in linked pedigrees generated for supply chains within the perishable goods and pharmaceuticals sectors.

A semantic modelling approach to knowledge based statistical software

The topic of this thesis is the development of knowledge based statistical software. The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Some of the issues involved in the development of knowledge based software are presented and a review is given of some of the systems that have been developed so far. The majority of these have moved away from conventional architectures by adopting what can be termed an expert systems approach. The thesis then proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented, the system has been designed as an enhanced variant of a conventional style statistical package. This involved developing a semantic data model to represent some of the statistically relevant knowledge about data and identifying sets of requirements that should be met for the application of the statistical techniques to be valid. Those areas of statistics covered in the prototype are measures of association and tests of location.