Linked data driven EPCIS Event-based Traceability across Supply chain business processes

GS1, 7th November 2014, London

Linked Data Driven, EPCIS Event-BasedTraceability in Supply Chain Business

Processes

Monika Solankihttps://w3id.org/people/msolanki

@nimonika

Aston Business SchoolAston University, Birmingham, UK

GS1, 7th November 2014, London

Broad Outline

Motivation

BackgroundSemantic Web & Linked dataEPC, EPCIS, Pedigrees

Ontologies

Linked Pedigrees

Summary

m.solanki@aston.ac.uk, @nimonika Linked Data Driven, EPCIS Event-Based Traceability in Supply Chains

Motivation GS1, 7th November 2014, London

Part 1

Motivation

The FIspace project: Motivating use cases

Flowers and Plants Supply Chain Monitoring: themonitoring and communication of transport and logisticsactivities focusing on tracking and tracing of shipments,assets and cargo, including quality conditions andsimulated shelf life.Meat Information Provenance (GS1 Germany) :ensuring that consumers, regulators and meat supplychain participants all have accurate information concerningwhere a meat product originated (production farm) andhow it was affected by its distribution (quality assurance).

Observations: Data sharing in supply chains

Existing mechanisms for sharing data and informationalong supply chains are highly restricted and extremelycomplex.There is a lack of information models that facilitate theexchange of end-to-end supply chain product and processknowledge.There is a very conservative “need-to-know” attitude suchthat essentially information flows only “one-up, one down”.

Observations: Data sharing in supply chains

Traceability datasets curated by partners are inherentlyrelated, however the varied underlying schemas lead tomapping overheads and interoperability issues.The semantics of traceability data and data curationprocesses are informally defined inspecifications/standards and associated implementations.Lack of a systematic and standardised way to exchangetraceability information.

Large volumes of traceability data are recorded at each partner’s end.m.solanki@aston.ac.uk, @nimonika Linked Data Driven, EPCIS Event-Based Traceability in Supply Chains

Requirements

Data Sharing in Supply chainsInformation and knowledge need to be interlinked, shared andmade available consistently along the supply chains not leastfor regulatory reasons but also due to increasing consumerdemands of being able to track and trace commodities.

Flow of information across stakeholders (Abstraction)

SW/LD for Traceability in Supply chainsProposed framework

Exploits Semantic Web standards, Linked data principlesand well known ontologies/vocabularies.Based on GS1’s EPCIS 1.1 and CBV 1.1 standards.A set of ontologies: EEM, CBVVocab, OntoPedigree.Event-Based, Provenance-aware traceability artifact:Linked Pedigrees.Algorithms for the automated generation of linkedpedigrees from EPCIS events.

ETL processes: EPCIS RDBs R2RML−−−−→ Linked data.Exception detection, constraint validation and inferencing....and there is more work-in-progress...

Supply chain domain/sector agnostic, as long as there is conformance toEPCIS 1.1. & CBV 1.1

Semantic Web & Linked Data GS1, 7th November 2014, London

Part 2

Semantic Web & Linked Data

A minimalistic overview

Semantic Web in 1 slideWeb scale data→ Machines first!Marks a shift in thinking from publishing data on the Web ashuman readable, interlinked HTML documents to publishingself describing, interlinked data on the Web in“machine-interpretable” formats.

self describing: associating metadata with data viavocabularies/ontologies/data dictionaries.interlinked data: “meaningful” links between “pieces” of selfdescribed data.machine-interpretable: an underlying model for data thatenables the exploitation of computational power toautomate and improve certain tasks at Web (Big data)scale e.g. Search, data integration, visualisations andmore...

Linked data in 1/2 slideWeb scale data→ Machines first!

Central ideaPublish data using principles that support Web applications indiscovering and integrating data by complying to a set of bestpractices in the areas of linking, vocabulary usage, andmetadata provision.

Principles for 5 star * Linked data

Use (HTTP) URIs as names for thingsProvide useful information as structured dataProvide data in non-proprietary formatsLink your data to other datasets using URIs

Linked Open Data *Publish data under an open license

*http://datahub.io/group/lodcloud*http://5stardata.info/

The evergrowing LOD cloud*: April 2014

*http://data.dws.informatik.uni-mannheim.de/lodcloud/2014/ISWC-RDB/m.solanki@aston.ac.uk, @nimonika Linked Data Driven, EPCIS Event-Based Traceability in Supply Chains

LOD cloud stats: April 2014

Max Schmachtenberg, Heiko Paulheim and Christian Bizer. Adoption ofLinked Data Best Practices in Different Topical Domains. ISWC 2014

Semantic Web: The W3C Technology stack

HTTP URIs - universal identifiers for resources on the Web.RDF data model - a “triples” based model.RDFS and OWL - domain knowledge representationstandards that enable inferencing over asserted facts.SPARQL - a query language for datasets encoded usingthe RDF data model.

HTTP URIsIn order to publish data on the Web, resources, i.e., itemsin the dataset and their relationships must be uniquelyidentified.HTTP URIs provide a simple way to create globally uniquenames in a decentralised manner.Besides identifying resources uniquely, they also serve asa means to access further information about the resources.

Identifying Cologne:http://live.dbpedia.org/resource/Köln

Identifying Germany:http://live.dbpedia.org/resource/Germany

RDF: Resource Description Framework

RDF is a data model.Basic building block: a triple, a statementA triple is composed of:<subject predicate (property) object>Each RDF Triple is a complete and unique fact.Abstract data model with several concrete syntaxes.Most common informal syntax: Directed GraphMost common formal syntaxes: Turtle, RDF/XML

RDF: Examples

Informal statement (Implicit semantics):Cologne is in Germany

Informal statement (Explicit semantics):Cologne has country Germany

RDF “triple” statement:Cologne hasCountry GermanyCologne country Germany

RDF: Examples

RDF triple in Turtle:@prefix: <http://fispace.aston.ac.uk/cities#>.:Cologne :country :Germany.

Adding more statements

:Cologne :country :Deutschland,:Germany;

:leaderName :Jürgen_Roters;:leaderTitle ‘‘Lord Mayor’’@en;:areaTotal 405150000.0000^^xsd:double.

RDF: Directed Graph representation

Graphical representation of the RDF data modelm.solanki@aston.ac.uk, @nimonika Linked Data Driven, EPCIS Event-Based Traceability in Supply Chains

HTTP URIs

RDF: HTTP URIs Examples in Turtle

@prefix dbpedia: <http://live.dbpedia.org/resource/>.@prefix dbprop: <http://live.dbpedia.org/ontology/>.

dbpedia:Köln dbprop:country dbpedia:Deutschland,dbpedia:Germany;

dbprop:leaderName dbpedia:Jürgen_Roters;dbprop:leaderTitle ‘‘Lord Mayor’’@en;dbprop:areaTotal 405150000.0000^^xsd:double.

Schemas for RDF triples: RDFS & OWL 2

Resource Framework Description Language (RDFS)Web Ontology Language (OWL 2)

Ontologies: Specification of domain knowledgeDefinition of standardised vocabularies used in RDFtriples, e.g, country indbpedia:Cologne dbprop:country dbpedia:Germany

RDFS: Class hierarchies, property hierarchies, basicproperty restrictions, Individuals(real world entities).OWL 2: RDFS + (very) expressive constraints + rules + ...RDFS syntax: RDF/XML, TurtleOWL 2 syntax: RDF/XML, Turtle, Manchester syntax

Class hierarchy

City and Country are Geographical entities.City is related to Country through the property country

Defining City: Manchester syntax

Prefix: wiki: <http://en.wikipedia.org/wiki/>Class: <http://purl.org/ontology/places#City>

SubClassOf:<http://purl.org/ontology/places#GeographicalEntity>

Annotations:rdfs:comment "A large settlement;rdfs:label "City" ,rdfs:label "City"@de ,rdfs:label "City"@en ,rdfs:label "City"@fr-fr ,rdfs:label "Ciudad"@es,rdfs:seeAlso wiki:City,

EquivalentTo:<http://dbpedia.org/ontology/City>

Properties

RDFS Property Restrictions

rdfs:domain and rdfs:range specify permitted subjectsand objects for a property respectively.

dbprop:country rdf:type owl:ObjectProperty ;rdfs:comment "identifies the country for a city";rdfs:domain ns2:City;rdfs:range ns2:Country.

dbprop:leaderName rdf:type owl:DatatypeProperty ;rdfs:comment "identifies the mayor for a city";rdfs:domain ns2:City;rdfs:range xsd:String.

Several other restrictions on properties can be specified inOWL.

SPARQL: Querying RDF datasets

SPARQL is a syntactically-SQL-like language forquerying RDF datasets via pattern matching.SPARQL queries contain a set of triple patterns called abasic graph pattern (BGP).Triple patterns are like RDF triples except that each of thesubject, predicate and object may be a variable.A BGP matches a subgraph of the RDF data when RDFterms from that subgraph may be substituted for thevariables.

RDF: Directed Graph representation

Graphical representation of the RDF data modelm.solanki@aston.ac.uk, @nimonika Linked Data Driven, EPCIS Event-Based Traceability in Supply Chains

SPARQL example

PREFIX dbprop: <http://live.dbpedia.org/ontology/>SELECT ?city ?country ?leaderWHERE{?city rdf:type ns2:City;

dbprop:country ?country;dbprop:leaderName ?leader.

?country rdf:type ns2:Country.}

Vocabularies in the LOD cloudWell-Known Vocabularies used by more than 5% of all datasetsin the LOD cloud.

Proprietary Vocabularies in the LOD cloud

....and then there is schema.org....

From Guha’s SemtechBiz 2014 KeynoteSince 2010: Google, Yahoo!, Microsoft & then Yandex.One vocabulary understood by all the search engines.Make it very easy for the (5 million) webmasters.Syntax: Microdata, RDFa, JSON-LD

*http://www.slideshare.net/rvguha/sem-tech2014c

Linked data principles?5 star linked data?Authoritative URIs for entities?Dereferenceable URIs for entities with content negotiation?

Only a few of the classes and properties are actually used

*http://www.slideshare.net/bizer/schmachtenberg-bizerpaulheim-lodbestpracticesiswc2014

EPC, EPCIS, CBV & Pedigrees GS1, 7th November 2014, London

Part 3

EPC, EPCIS, CBV & Pedigrees

A minimalistic overview

EPC, EPCIS, CBV

EPC: provides products with unique, serialised identities.EPCIS 1.1: provides a set of specifications for the syntacticcapture and informal semantic interpretation of EPC basedproduct information.CBV 1.1 supplements EPCIS by defining the structure ofvocabularies and specific values for the vocabularyelements.Events as abstractions for traceability: One generic (EPCISEvent) and four speciliased (Object, Aggregation,Transaction, Transformation) physical event types.

Data model componentsWhat(product(s)), Where(location), When(time), andWhy(business step and status) of events (product movement)occurring in any supply chain.

EPCs (SGTINs)TimeRead PointsBusiness LocationBusiness stepsDispositionTransaction typesActionQuantities and measurementsSources and DestinationsILMD (Instance Lot Master Data)

Pedigrees

Most widely prevalent in the pharmaceutical industry.Pedigree (e-pedigree) is an audit trail that records the pathand ownership of a drug as it moves through the supplychain.Each stakeholder involved in the manufacture ordistribution of the drug adds information to the pedigree.

SW & LD for Visibility in Supply chains

Problem statement* Can we formalise EPCIS using the underlying standardsfor Semantic Web and principles of linked data torepresent traceability-specific domain knowledge insupply chains?

* Can we exploit EPCIS events for the automatedgeneration of provenance-based traceability/visibilityartifacts that can be shared across supply chain partners?

Ontologies GS1, 7th November 2014, London

Part 4

Ontologies

EEM*: The EPCIS Event Model

A domain specific, ontological information model.Focuses on a tight conformance with the EPCIS 1.1standard and Simplicity.Explicitly defines relationships with CBV 1.1 entitiesthrough CBVVocab*.EEM has been mapped* to PROV-O*.

*http://purl.org/eem#*www.w3.org/ns/prov-o*http://purl.org/cbv#

*http://fispace.aston.ac.uk/ontologies/eem_prov.html

EEM Modules

Modelling the generic EPCISEvent

An EPCIS event has three temporal properties associatedwith it.An EPCIS event occurs at a unique location and is part ofa singular business process.

Modelling the generic EPCISEvent

Class: EPCISEventSubClassOf:

eventTimeZoneOffset exactly 1 xsd:dateTime,eventRecordedAt exactly 1 xsd:dateTime,eventOccurredAt exactly 1 xsd:dateTime

ObjectProperty: hasReadPointLocationCharacteristics:

FunctionalDomain:

EPCISEventRange:

ReadPointLocation

Modelling ObjectEvent

An ObjectEvent is an EPCISEvent.An ObjectEvent is required to have associated EPCs,and an action.

Modelling ObjectEvent

Class: ObjectEventSubClassOf:

(action some Action)and (associatedWithEPCList some SetofEPCs),

EPCISEvent

EEM Entities: Axiomatisation

EEM Entities: Mapping to PROV-O

Implementing EEM: LinkedEPCIS library

EEM is a complex data model.Non trivial to generate class assertions and complexqueries without knowing the structure of the model andnomenclature of the entities.LinkedEPCIS* - an open source Java API to,

Capture EPCIS events as linked data.Encourage the uptake of EEM among EPCIS conformingorganisations and industriesEase the creation of EEM instancesProvides classes, interfaces and RESTful Web services forcapturing, storing and querying EPCIS events.

* https://github.com/nimonika/LinkedEPCIS

Interlinking EPCIS Event data

Applying EEM to the Agri-food domain

The tomato supply chain involves thousands of farmers,hundreds of traders and few retail groups.

Agri-food scenario: Subset of EPCIS events

Supply chain operation EPCIS event type Business Step Disposition Action type

1. Commissioning crates for tomatoes Object event commissioning active ADD2. Storing crates Quantity event storing in_progress -3. Aggregating crates in pallets Aggregation event packing in_progress ADD4. Loading and shipping pallets Transaction event shipping in_transit ADD

Linked Pedigrees GS1, 7th November 2014, London

Part 5

Linked Pedigrees

Event-based Linked Pedigrees

Encapsulate EPCIS event-based knowledge required totrace and track products in supply chains.Facilitate the interlinking of a variety of related and relevantdata, i.e., product master data with event data and otherpedigrees.Enable sharing of knowledge among partners - pedigreesare exchanged as products physically flow downstream orupstream in the supply chain.

OntoPedigree: A CO design pattern

Competency questions:Who is the creator of the pedigree?What is the supply chain creation status of a givenpedigree?Which are the business transactions recorded against aparticular consignment?What are the events associated with pedigrees createdbetween dates X and Y?Which products have been shipped together?Which other pedigrees are included in the receivedpedigree?

OntoPedigree: A CO design pattern

Pedigree: Axiomatisation

Class: ped:PedigreeSubClassOf:(hasPedigreeStatus exactly 1 ped:PedigreeStatus)and (hasSerialNumber exactly 1 rdfs:Literal)and (pedigreeCreationTime exactly 1 xsd:DateTime)and (prov:wasAttributedTo exactly 1 ped:PedigreeCreator)and (ped:hasConsignmentInfo some eem:SetOfEPCISEvents)and (ped:hasTransactionInfo exactly 1 eem:SetOfEPCISEvents)and (ped:hasProductInfo min 1),(prov:wasGeneratedBy only ped:PedigreeCreationService),(ped:hasReceivedPedigree only eem:Pedigree),prov:Entity

*possible integration with GTIN+ on the Webhttp://www.gs1.org/digital

Generating Linked Pedigrees event URIs

Events incorporated in pedigree creation

commissioning: uniquely identifying productsaggregation: uniquely identifying aggregationsshipping: associating products with ordersreceiving: associating received products with orders

Pedigree Component Linking relationship Resource identifierProduct information hasProductInfo Product data URIs

Serialised product data URIsConsignment information hasConsignmentInfo Commissioning events -

Object event/Aggregation event URIsTransaction information hasTransactionInfo Shipping events -

Transaction event URIs

Direct linkages in the linked pedigree generated by each supplychain trading partner

Linked Pedigree: An example

### http://fispace.aston.ac.uk/joetrader/pedigrees/JoeTomatoTraderPedigree456

jsc:JoeTomatoTraderPedigree456 rdf:type ped:Pedigreeped:hasSerialNumber "joeTradePed456"^^xsd:String;ped:hasStatus ped:Intermediate;ped:hasConsignmentInfo jci:JoeTraderObjectEvent20,

jci:JoeTraderObjectEvent30;ped:hasTransactionInfo jti:JoeTraderTransactionEvent40;ped:hasProductInfo jpi:JoeTradesMay2013Info.ped:hasReceivedPedigree fsc:FranzTomatoFarmerPedigree123,

bsc:BobTomatoFarmerPedigree123.

Linked Pedigrees: Agri-food supply chains

Linked Pedigrees: Healthcare supply chains

Flow of linked pedigrees (Abstraction)

M. Solanki and C. Brewster. EPCIS event-based traceability inpharmaceutical supply chains via automated generation of linked pedigrees.

ISWC 2014. Springer-Verlag.

Architecture and Implementation

Transformation Events: Wine production

EPCIS events generated during the wine processing stages

M. Solanki and C. Brewster. Modelling and Linking transformations in EPCISgoverning supply chain business processes. EC-Web 2014. Springer-LNBIP.

Typical queries

1 Tracking ingredients: What were the inputs consumedduring processing in the batch of wine bottles shipped ondate X?

2 Tracking provenance: Which winery staff were present atthe winery when the wine bottles were aggregated incases with identifiers X and Y?

3 Tracking external data: Retrieve the average values forthe growth temperature for grapes used in the production ofa batch of wine to be shipped to Destination D on date X.

Transformation Events: ETL Framework

EPCIS Exceptions

Typical examples(e1) Pedigree serial number discrepancy(e2) product inference problem - the inability to infer aboutproducts contained in an outer container withoutdisaggregation using pedigree information(e3) quantity inference problem - the inability to derive thetotal quantity of items packed in an outer container withoutdisaggregation using pedigree information(e4) missing or incorrect containment hierarchy betweenitems and their containers - source of counterfeits.(e5) incomplete pedigree data(e6) pedigree data with broken chains, i.e., missingintermediate stakeholder pedigree information.

Hierarchy of EPCIS Exceptions

EPCISExceptionEvent: Axiomatisation

M. Solanki and C. Brewster. Detecting EPCIS Exceptions in linkedtraceability streams across supply chain business processes. SEMANTiCS

2014. ACM-ICPS.

Summary GS1, 7th November 2014, London

Part 6

Summary

EEM: EPCIS Event ModelData visibility (tracking and tracing) in supply chains hasreceived considerable attention in recent years.EEM based linked datasets can be exploited in order toimprove visibility, accuracy and automation along thesupply chain.EEM along with CBVVocab can be used to derive implicitknowledge that can expose inefficiencies such as shipmentdelay, inventory shrinkage and out-of-stock situation.

Linked Pedigrees

Semantic Web standards, ontologies and linked data canbe utilised to record and represent real time supply chainknowledge via “linked pedigrees”.EEM forms the basis for traceability in supply chains -Event-based Linked Pedigrees.Complex Event Processing over continuous streams ofsemantically interlinked EPCIS event datasets enableautomated generation of linked pedigrees, detection ofexceptions and validation of integrity constraints.The proposed approach is domain independent and canbe widely applied to most scenarios of traceability as longas there is conformance to EPCIS 1.1 in the supply chain.

Further informationM. Solanki and C. Brewster. A Knowledge Driven Approach towards theValidation of Externally Acquired Traceability Datasets in Supply ChainBusiness Processes. EKAW 2014. Springer-Verlag.M. Solanki and C. Brewster. EPCIS event-based traceability inpharmaceutical supply chains via automated generation of linkedpedigrees. ISWC 2014. Springer-Verlag.M. Solanki and C. Brewster. Modelling and Linking transformations inEPCIS governing supply chain business processes. EC-Web 2014.Springer-LNBIP.M. Solanki and C. Brewster. Detecting EPCIS Exceptions in linkedtraceability streams across supply chain business processes.SEMANTiCS 2014. ACM-ICPS.M. Solanki and C. Brewster. Consuming Linked data in Supply Chains:Enabling data visibility via Linked Pedigrees. COLD2013 at ISWC,volume Vol-1034. CEUR-WS.org proceedings, 2013.M. Solanki and C. Brewster. Representing Supply Chain Events on theWeb of Data. DeRiVE at ISWC. CEUR-WS.org proceedings, 2013.http://windermere.aston.ac.uk/~monika/ontologies.html

http://windermere.aston.ac.uk/~monika/publication.html

Linked data driven EPCIS Event-based Traceability across Supply chain business processes

Technology

Transcript of Linked data driven EPCIS Event-based Traceability across Supply chain business processes

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