Presentation on theme: "Long Term Sustainment of Digital Information for Engineering Design: Model Driven Approach Sudarsan Rachuri National Institute of Standards and Technology/"— Presentation transcript:
Long Term Sustainment of Digital Information for Engineering Design: Model Driven Approach Sudarsan Rachuri National Institute of Standards and Technology/ George Washington University USA
Acknowledgements Joshua Lubell Mahesh Mani Sub DPG Group
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
Digital Preservation – A Status check Terry Kuny 1 listed various issues of archiving in his 1997 paper Increasing loss of digital informationIncreasing 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. 1 A Digital Dark Ages? Challenges in the Preservation of Electronic Information 63RD IFLA Council and General Conference
Digital Preservation Goal: –To identify challenges, research, and implementation issues in digital preservation of information with an emphasis on design and manufacturing. –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. – T hree main methods of digital preservation: technology preservation; technology emulation; information migration.
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
A Mind Map for LTKR
OAIS ENVIRONMENT Information Package Concepts Relationships Obtaining Information from Data Environment Model of an OAIS OAIS Archive External Data Producer is the role played by those persons, or client systems, who provide the information to be preserved Management is the role played by those who set overall OAIS policy as one component in a broader policy domain Consumer is the role played by those persons, or client systems, who interact with OAIS services to find and acquire preserved information of interest
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.
OAIS Functional Entities Ingest: functions include receiving SIPs, performing quality assurance on SIPs, generating an Archival Information Package (AIP) which complies with the archives data formatting and documentation standards, extracting Descriptive Information from the AIPs for inclusion in the archive database, and coordinating updates to Archival Storage and Data Management. Archival Storage: provides the services and functions for the storage, maintenance and retrieval of AIPs. Its functions include receiving AIPs from Ingest and adding them to permanent storage, managing the storage hierarchy, refreshing the media on which archive holdings are stored, performing routine and special error checking, providing disaster recovery capabilities, and providing AIPs to Access to fulfill orders. Data Management: This entity provides the services and functions for populating, maintaining, and accessing both Descriptive Information which identifies and documents archive holdings and administrative data used to manage the archive. Administration: This entity provides the services and functions for the overall operation of the archive system. Preservation Planning: This entity provides the services and functions for monitoring the environment of the OAIS and providing recommendations to ensure that the information stored in the OAIS remains accessible to the Designated User Community over the long term, even if the original computing environment becomes obsolete. Access: This entity provides the services and functions that support Consumers in determining the existence, description, location and availability of information stored in the OAIS, and allowing Consumers to request and receive information products.
OAIS: Six Functional Entities SIP = Submission Information Package SIP DIP Administration PRODUCERPRODUCER CONSUMERCONSUMER queries result sets MANAGEMENT Ingest Access Data Management Archival Storage Descriptive Info. Preservation Planning orders AIP AIP = Archival Information Package DIP = Dissemination Information Package
OAIS: Agents based Approach SIP = Submission Information Package SIP DIP Administration Agent PRODUCERPRODUCER CONSUMERCONSUMER queries result sets MANAGEMENT Ingest Agent Access Agent Data Management Archival Storage Descriptive Info. Preservation Planning Agent orders AIP AIP = Archival Information Package DIP = Dissemination Information Package SIP Agent DIP Agent AIP Agent
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
Knowledge Representation: Beyond Geometry Artifact Form Function Behavior Relationships * Specifications * Rationale * Requirements * Assembly,... Information * Documentation * Configuration * … Geometry Material Design : Function dictates Form Manufacturing : Form dictates Function
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 )
CPM : Four categories of classes 1.Classes that provide supporting information for the objects (abstract classes) for storing common information –CoreProductModel, CommonCoreObject, CommonCoreRelationship –CoreEntity, CoreProperty 2.Classes of physical or conceptual objects –Artifact, Feature, Port, Specification, Requirement –Function, TransferFunction, Flow, Behavior –From, Geometry, Material 3.Classes that describe relationships among objects, they are derived from CommonCoreRelationship –Constraint, Usage, Trace, EntityAssociation 4.Classes that are commonly used by other classes. –Information, ProcessInformation, Rational
CPM : Three kinds of associations 1.All object classes have their own separate, independent decomposition hierarchies by attributes such as subArtifacts/subArtifactOf for the Artifact class. 2.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. 3.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.
Core Product Model
Open Assembly Model Open Assembly Model
Extensions to CPM Design-Analysis Integration Model - Objectives: –support tighter design-analysis integration than is possible today –support broad range of discipline-specific functional analyses –make analysis-driven design (function-to- form reasoning) more practical –eventually support opportunistic analysis –Key feature: two models and two one-way associations Product Family Evolution Model -Objectives: –represent the evolution of product families and of the rationale for the changes Key features: –keeps track of product & component series & versions; configuration relationship ties them together –keeps track of what, how and why of all changes between versions/series
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
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
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
Ontology and Languages for Representation XML, XML Schema RDF, RDF Schema OWL Tools for Ontology and Reasoning Protégé Protégé ontology editor and knowledge base frameworkontology editor and knowledge base framework Racer Pro Racer Pro a reasoner/inference server for the Semantic Web 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
Domain of discourse Formal Informal Language Processible expressiveness Content Represented by Programming Information modeling Visual modeling Logic based Query Natural language Representational and inferential needs Language types ProducerConsumer Specific content Mental model Mental model Language and expressivity A Model of Communication between Agents
Programming Product Lifecycle Information Content Creators/Users Geometry information Design Information Change Mgt Processes Behavior Standards: STEP Standards : Incomplete Function Requirements Constraints/Relati onships Standards : Evolving Stakeholders Designers Manufacturers Suppliers Marketing/Sales Engineers Maintenance Recyclers Lifecycle information 2D/3D models Surface Model Features Material Tests Maintenance Recycle Disposal Formal Informal Language Expressivity Content Represented by Information Modeling Visual Modeling Logic Based Query Natural Language Representational Needs Language Features Topology Mathematics DesignersManufacturers SuppliersMarketing/SalesEngineers Operations/ Maintenance Recyclers Content, Language, Expressivity
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
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
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
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)
Representation: –What constitutes a canonical representation for archiving? Can we exploit CPM to define such a representation? –Representation space - What is it and what does it look like? –How to compress data and develop data reduction schemes? Archiving processes: –What is the initial requirement (draft) for Preservation Description Information (PDI) for product data? –How to convert SIP to AIP and how to create DIP taking a holistic view of information package? This is essential to avoid fragmentation of creation, storage, and retrieval. Requirements: Product Data Archiving
Broader issues: –How to authenticate the archived information? –Is there a larger community for digital archiving for product data? –How to manage interoperability among different archival systems? –What is the role of standards in information packages? How to develop standard schemas for submission information package, archival information package, dissemination information package, and Producer-Archive Interface Methodology Standard? 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)
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.
LTKR NIST WORKSHOP 2007 Long Term Sustainment of Digital Information for Science and Engineering: Putting the Pieces Together Tuesday- Wednesday, April NIST, Gaithersburg, MD 20899, USA 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
Summary Language Theory Domain Theory Representation Theory