Introduction to Sharp’s Methods

1. Introduction to Sharp’s Methods Jim Carpenter Bureau of Labor Statistics and President, DAMA-NCR BLS Seminar May 24, 1999 DAMA-NCR Seminar May 25, 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: • OTSP Research (Office of Technology & Survey Processing) • CMM Project • 3 Key Technologies in Systems Development • New requirements specification project • OSMR Research (Office of Survey Methodology Research) • Ontology • Usability • DAMA-NCR service expansion • Sharp’s Methodology

4. CMM Project:3 Key Technologies in Systems Development • Components • packaging & distribution of CPU processes • our product • Modeling Languages • every method & tool has a language • our thinking • Metadata • managing sharable data • our map out of the Tower of Babel

5. More on Metadata • BLS participation in ISO & ANSI metadata standards • standards committees (ANSI X3/L8, ISO/IEC JTC1/SC32/WG2) • International forums/workshops (BLS hosted 2) • Metadata Registry Implementers Coalition • Types of Principles described in ISO 11179 and embodied in X3.285 model of Metadata Registry • Naming & identification • Stewardship • Classification • Administration Designed as a Natural Language Model

6. CMM Project Demos • Conceptual Models: Economics & Statistics • based on crude linguistic analysis of definitions in BLS Handbook of Methods & personal experience • Uses to be demonstrated • Resolve multiple definitions (map meanings) • Classification for search engines • UI - table of contents • Communication of concepts • DB based on ANSI X3.285 model of MD Registry • literal translation of model to DB • Searching for unified demo - next slide

7. Demo: Start with PPI Data Dictionary(tentative) Show how to: • Refine definitions into fact types(Sharp’s method) • Generate data model from fact types (algorithm) • Stock X3.285 Registry with • PPI definitions, metadata & data model • Conceptual Models (economics & statistics) • OSMR’s ontologies • Create an interface to X3.285 Registry based on • Draft ANSI standards for interface to X3.285 Registry (read only) • Sharp’s process analysis of fact type matrix (of X3.285 Registry) • Ron Ross’ business rules • Design components that use X3.285 Registry interface

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

9. Sharp’s Method:What’s in scope? Requirements for database: • Persistent data: facts in a database • Called facts because we wish them to be, or nearly so. • 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)

10. Sharp’s Method:What’s not in scope? Requirements for how you use the database: • How you use • the persistent data (outside of the interface) • 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 • the “big processes” • … expanding little processes using Ross’ rules

11. 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

12. 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?

13. 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

14. 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.

15. 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.

16. 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.

17. Database Table Fits 1 Fact Type A child gave a toy of a certain color to a child.

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

19. Jim’s Vision Tool Information Model Valid Fact Types & Constraints Network of Models Natural Language Statements Machine Language Component

20. Implementation • Direction of standards bodies (OMG & MDC): • Hub is MOF (Meta Object Facility): repository with interface • All Models expressed as extensions of UML tree • Transport (application level) is XML & XMI Valid Fact Types & Constraints Model A Model Mapping Hub Model B Machine Language Component Model Z