Long Term Sustainment of Digital Information for Engineering Design: Model Driven Approach

1. Long Term Sustainment of Digital Information for Engineering Design: Model Driven Approach Sudarsan Rachuri National Institute of Standards and Technology/ George Washington University USA sudarsan@nist.gov

2. Acknowledgements • Joshua Lubell • Mahesh Mani • Sub • DPG Group

3. Overview • Digital Preservation • OAIS • 2006 LTKR Workshop Report • Product Model • Beyond Geometry – NIST CPM/OAM • Annotations for Archiving • Standards and Semantic Interoperability • Sustainment of Digital Information for Science and Engineering – Requirements • Conclusions

4. Digital Preservation – A Status check Terry Kuny 1 listed various issues of archiving in his 1997 paper • Increasing loss of digital information • Information explosion • Proliferation of digital formats with hardware and software dependencies. • Decrease of Financial resources available for libraries and archives • Increasingly restrictive IPRs • Increasing privatization of archiving • Standards will not emerge to solve fundamental issues with respect to digital information. 1A Digital Dark Ages? Challenges in the Preservation of Electronic Information 63RD IFLA Council and General Conference

5. LTKR NIST WORKSHOP 2006 Main themes • a view of LTKR as an archiving process • an emphasis on business case development. Main Issues • lack of support & understanding of LTKR in the engineering community • An economic model to rationalize archiving • lack of formal methods and standards for long term retention of engineering knowledge • uncertainty in the utility of the archived data, inefficient archival procedures • Clear Policy guidelines and cost-benefit models

6. A Mind Map for LTKR

7. Reference Model for OAIS • It addresses the following preservation functions • Ingest • Archival storage • Data management • Access • Dissemination • Migration to new media and forms • Data models • Role of software in information preservation • Exchange among archives • The OAIS Reference Model is designed as a conceptual framework in which to discuss and compare archives.

8. Preservation Planning P R O D U C E R C O N S U M E R Data Management Descriptive Info. queries result sets SIP Ingest Access orders Archival Storage DIP AIP Administration MANAGEMENT SIP = Submission Information Package AIP = Archival Information Package DIP = Dissemination Information Package OAIS: Six Functional Entities

9. Descriptive Info. AIP OAIS: Agents based Approach Preservation Planning Agent P R O D U C E R C O N S U M E R Data Management queries Access Agent result sets SIP Agent Ingest Agent SIP orders Archival Storage DIP DIP Agent AIP Agent Administration Agent MANAGEMENT SIP = Submission Information Package AIP = Archival Information Package DIP = Dissemination Information Package

10. Overview • Digital Preservation • OAIS • Product Model • Beyond Geometry – NIST CPM/OAM • Annotations for Archiving • Standards and Semantic Interoperability • Sustainment of Digital Information for Science and Engineering – Requirements • Conclusions

11. Information * Documentation * Configuration * … Knowledge Representation: Beyond Geometry Artifact Function Form Geometry Behavior Material Relationships * Specifications * Rationale * Requirements * Assembly,... Design : Function dictates Form Manufacturing : Form dictates Function

12. Core Product Model • Objective: base-level product model that is: • generic • extensible • independent of any one product development process • capable of capturing full engineering context • Key feature: explicit representation of Function – Form - Behavior (in contrast to STEP AP 209 that essentially represents only form )

13. CPM : Four categories of classes • Classes that provide supporting information for the objects (abstract classes) for storing common information • CoreProductModel, CommonCoreObject, CommonCoreRelationship • CoreEntity, CoreProperty • Classes of physical or conceptual objects • Artifact, Feature, Port, Specification, Requirement • Function, TransferFunction, Flow, Behavior • From, Geometry, Material • Classes that describe relationships among objects, they are derived from CommonCoreRelationship • Constraint, Usage, Trace, EntityAssociation • Classes that are commonly used by other classes. • Information, ProcessInformation, Rational

14. CPM : Three kinds of associations • All object classes have their own separate, independent decomposition hierarchies by attributes such as subArtifacts/subArtifactOf for the Artifact class. • there are associations between: • a Specification and the Artifact that results from it • a Flow and its source and destination Artifacts and its input and output Functions • an Artifact and its Features. • Four aggregations are fundamental to the CPM: • Function, Form and Behavior aggregate into Artifact • Function and Form aggregate into Feature • Geometry and Material aggregate into Form • Requirement aggregates into Specification.

15. Core Product Model

16. Open Assembly Model Open Assembly Model

17. Use of Annotations The research issues in annotations • Management of annotations • Structure and type of annotation • Formal languages for annotation • Ontology based annotations • Human in the loop and Semi-automatic annotation generation • How to add non-geometry information to CAD/PLM information? • Annotations could be a good mechanism • Feature based annotation • Annotations as information handles for archiving

18. Overview • Introduction • Digital Preservation • OAIS • Product Model • Geometry • Beyond Geometry – NIST CPM/OAM • Annotations for Archiving • Standards and Semantic Interoperability • Sustainment of Digital Information for Science and Engineering – Requirements • Conclusions

19. Product Ontology – A Work in Progress • Currently evaluating CPM/OAM as possible ontology for product and for annotations • Representation of CPM/OAM in • OWL representation and Inferencing and Reasoning • UML 2.0 and SysML • Extracting information models from STEP AP 203/214, AP 233, AP 239

20. Ontology and Languages for Representation XML, XML Schema RDF, RDF Schema OWL • Tools for Ontology and Reasoning • Protégé • ontology editor and knowledge base framework • Racer Pro • a reasoner/inference server for the Semantic Web • Semantic Web Rule Language Plug-in • to write rules • Jess Engine • to make rules work • Jess Bridge • to connect OWL, SWRL and Jess Engine

21. Programming Language and expressivity Information modeling Visual modeling Informal Processible expressiveness Language Logic based Formal Query Represented by Representational and inferential needs Content Natural language Language types Domain of discourse Specific content Mental model Mental model Producer Consumer A Model of Communication between Agents

22. Content, Language, Expressivity Programming Content Creators/Users Formal Information Modeling Informal Designers Representational Needs Language Features Visual Modeling Manufacturers Language Expressivity Engineers Logic Based Suppliers Query Represented by Marketing/Sales Content Mathematics Maintenance Stakeholders Recyclers Natural Language Product Lifecycle Information Geometry information Design Information Lifecycle information Requirements Behavior 2D/3D models Features Processes Change Mgt Surface Model Material Tests Maintenance Function Constraints/Relationships Recycle Disposal Topology Standards : Incomplete Standards : Evolving Standards: STEP Designers Manufacturers Engineers Suppliers Marketing/Sales Operations/ Maintenance Recyclers

23. Overview • Introduction • Digital Preservation • OAIS • Product Model • Geometry • Beyond Geometry – NIST CPM/OAM • Annotations for Archiving • Canonical Representation • Standards and Semantic Interoperability • Sustainment of Digital Information for Science and Engineering – Requirements • Conclusions

24. General requirements • User Acceptance and Requirements • Suitable retrieval-techniques • Minimize workflow expenses for archiving • Legal demand • Ability to verify the conformity of a part with its documentation • The system has to enable the user to assure the provisions of a law regarding data security and protection of data privacy over the life cycle of the archives. • Possibility to audit the processes of archiving and retrieval • Security

25. Requirements: Product Data Archiving • Legal • accident investigation, failure analysis • customer delivery requirements • Merger and acquisition • Patent infringements • Operational and support • Historical data to provide lifecycle support (maintenance, spares, recycling and disposal) • Product development management • Effectivity; tracing design rationale in cases of failure • Design re-use (used in multiple products or models) • Engineering change proposals/analysis • Reverse engineering • Comparison with new work, test beds, validation suites

26. Requirements: Product Data Archiving • Content: • What is to be archived beyond geometry information? How is this information to be represented? • Is STEP a starting point for content information? • How to scale from part level to system level information? • What are the Access points (for retrieval) for product data? Is there a role for generic features and contextual indexing? • How to progress from Content representation to reasoning and inferencing? • How to incorporate tolerance information? What is in AP 203 Edition 2 for tolerance semantics? • Observations: • OAIS RM forms the necessary basis for world-class archive. • OAIS RM is not sufficient. Need domain specific standards for: • understanding what information must be preserved • understanding what constitutes proper and complete descriptive information (metadata standards)

27. Conclusions • Archiving of engineering informatics is very critical in this information age in order to fulfill legal, business, and product quality and  liability obligations. • Engineering informatics is the discipline of creating, codifying (structure and behavior that is syntax and semantics), exchanging (interactions and sharing), processing (decision making), storing and retrieving (archive and access) the digital objects that characterize the cross-disciplinary domains of engineering discourse. • The main difficulty lies in maintaining digital information intact, while providing access to this information in a usage context that is subjected to change. • Kuny coined the term “preservation nexus” to mean the relationship between hardware, software and humanware and can be maintained, then digital object can be preserved forever. • It is the “contents” that must be preserved not conserved. Unlike conservation practices where an item can often be treated, stored and essentially forgotten for some period of time, digital objects will require frequent refreshing and recopying to new storage media. Keeping the “original” digital artifact is not important.

28. LTKR NIST WORKSHOP 2007 • Long Term Sustainment of Digital Information for Science and Engineering: Putting the Pieces TogetherTuesday- Wednesday, April 24-25NIST, Gaithersburg, MD 20899, USA http://www.mel.nist.gov/div826/msid/sima/interopweek/meetings.htm Abstract: Researchers at universities, in industry, and in government are developing tools and standards for archiving and preserving the ever-increasing volume of digital information humankind produces. Meanwhile, records managers are grappling with maintaining their organizations' data assets and responding to requests for electronic information from regulators, legal investigations, and other sources. Scientists and engineers, in addition to the aforementioned issues, want the digital models and systems they build today to be extensible and reusable for subsequent generations of technologists. Our discussion, a sequel to last year's highly successful Long Term Knowledge Retention workshop, will focus on policies of digital preservation and applying promising technologies to solve preservation problems in product design, engineering, and manufacturing in particular, with possible extensions to include chemistry, biology, and other disciplines where critical information must be "future-proofed." Please visit http://digitalpreservation.wikispaces.com/

29. Summary Language Theory Representation Theory Domain Theory