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Intro to Sharp’s Methods

Intro to Sharp’s Methods. Jim Carpenter Bureau of Labor Statistics OTSP Seminar May 24, 1999. Who is Dr. John Sharp?. Sharp Informatics, Inc. Sandia National Laboratories (18 yr.) Pioneer in NLM applications NLM = Natural Language Modeling

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Intro to Sharp’s Methods

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  1. Intro to Sharp’s Methods Jim Carpenter Bureau of Labor Statistics OTSP Seminar May 24, 1999

  2. Who is Dr. John Sharp? • Sharp Informatics, Inc. • Sandia National Laboratories (18 yr.) • Pioneer in NLM applications • NLM = Natural Language Modeling • Author: “mathematically precise procedure for performing information analysis” http://www.dama-ncr.org/SpeakerBios.htm

  3. Why is he here at BLS? Convergence of: • CMM Project • 3 Key Technologies in Systems Development • OTSP Project (Carl Lowe) • Requirements specification • Broad scope - extends my scope • OSMR Research • Ontology • Usability • Sharp’s Methodology

  4. CMM Project: Key Technologies • Components - packaging & distribution of CPU processes • Modeling Languages - every method & tool has a language • Metadata - managing sharable data • BLS participation in ISO & ANSI • standards committee • 2 international forums at BLS • editor of Terminology Management Technical Report • Metadata Registry Implementers Coalition • OSMR research: taxonomy, usability,...

  5. CMM Project Demos • Conceptual Models: Economics & Statistics • based on linguistic analysis of definitions in BLS Handbook of Methods & personal experience on IPP • Uses • Resolve multiple definitions (map meanings) • Classification for search engines • UI - table of contents • Communication of concepts • DB based on X3.285 model (ANSI standard) • literal translation of model to DB • PPI data dictionary (tentative)

  6. Demo: PPI Data Dictionary(tentative) How to: • Refine definitions into fact types(Sharp’s method) • Generate data model from fact types (Sharp’s algorithm?) • Stock X3.285 Registry with • PPI definitions & data model • Conceptual Models (economics & statistics) • OSMR’s ontologies • Create an interface to X3.285 Registry based on • Proposed O-O interface ANSI standards • Sharp’s process analysis of fact type matrix • Ron Ross’ business rules • Design components that use X3.285 Registry interface

  7. Sharp’s Information Modeling Methods • Function: requirements for database • Basis: Natural Language Modeling • Benefits: quality data & metadata

  8. Sharp’s Method:What’s in scope? • Persistent data: facts in a database • Called facts because we wish them to be, or are “close enough ...” • Rows in a relational table • (Column 1 in Zachman Framework) • “Little processes”: constrained clusters of CRUD • CRUD operations: Create, Read, Update, Delete • Cluster: should be performed together as a group • Constraints: Ross’ Atomic Table of Business Rules • The interface to the facts • (Column 2 in Zachman Framework)

  9. Sharp’s Method:What’s not in scope? • How you use • the persistent data • the little processes (just keep the interface) • Specifically… “big process” stuff, like • Workflow — Security • Components — Communications • Unless you are … • … building a database for managing • the metadata and • the “big processes” • … expanding little processes using Ross’ rules

  10. Key Concept: Fact Type • Fact • an assertion that something (object) plays a role • generalization of attribute & relationship from ER • Fact type • an assertion that objects in a type (class) play a role

  11. Trivia: an isolated factFact 1: Jack gave the red ball to Jill • What to do with a single fact? • Can’t generalize. • Why store it?

  12. Generalizing with more facts Object Role give... boy A boy gave the red ball to Jill • Fact 1: Jack gave the red ball to Jill. • Fact 2: John gave the red ball to Jill. • Fact type: A boy gave the red toy to Jill. • Object: a boy (with a name) • Role: giver of the red ball to Jill

  13. More objects & roles in a fact type Object 1 Role 1 Role 2 Object 2 give... receive... boy girl A boy gave the red ball to/received the red ball from a girl • Fact 1: Jack gave the red ball to Jill.Fact 2: John gave the red ball to Jill. • Fact 3: Jack gave the red ball to Jane. • Fact type: A boy gave the red ball to a girl.

  14. Generalize the objects • Fact 1: Jack gave the red ball to Jill.Fact 2: John gave the red ball to Jill.Fact 3: Jack gave the red ball to Jane. • Fact 4: Jane gave the red ball to Jack. • Fact type: A child gave the red ball to a child.

  15. More generalizations • Fact 5: Jane gave the white ball to Jack. • Fact type: A child gave a ball of a certain color to a child • Fact 6: Jane gave the green truck to Jack. • Fact type: A child gave a toy of a certain color to a child.

  16. Data Base Table

  17. Sharp’s Methods Source Statements Sharp’s Procedure Valid Fact Types Transform Data Model Valid Fact Types Cluster Process Model

  18. Jim’s Vision Network of Models Models, too! Refined Natural Language Source Statements Machine Language Component

  19. Implementation Model A Source Statements Model Mapping Hub Model B System Component Model Z • Direction of standards bodies (OMG & MDC): • Hub is MOF (Model Object Facility) • All Models expressed as extensions of UML tree • Transport (application level) is XML • Other proprietary implementations

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