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”

3 Why is he here at BLS? 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 Convergence of:

4 CMM Project: Key Technologies Components - packaging & distribution of CPU p rocesses 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) –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 How to:

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

1010 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

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

1212 Generalizing with more facts 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 boy give... A boy gave the red ball to Jill ObjectRole

1313 More objects & roles in a fact type 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. boy girl give...receive... A boy gave the red ball to/received the red ball from a girl Object 1Role 1Role 2Object 2

1414 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.

1515 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.

1616 Data Base Table

1717 Sharp’s Methods Source Statements Sharp’s Procedure Valid Fact Types Valid Fact Types TransformData ModelCluster Process Model

1818 Jim’s Vision Refined Natural Language Source Statements Network of Models Machine Language Component Models, too!

1919 Implementation Model Mapping Hub Source Statements System Component Model A Model B 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