Intro to Sharp

1
Intro to Sharps 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
• Sharps 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 (Sharps
  method)
• Generate data model from fact types (Sharps
  algorithm?)
• Stock X3.285 Registry with
• PPI definitions data model
• Conceptual Models (economics statistics)
• OSMRs ontologies
• Create an interface to X3.285 Registry based on
• Proposed O-O interface ANSI standards
• Sharps process analysis of fact type matrix
• Ron Ross business rules
• Design components that use X3.285 Registry
  interface

7
Sharps Information Modeling Methods

• Function requirements for database
• Basis Natural Language Modeling
• Benefits quality data metadata

8
Sharps Method Whats 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
Sharps MethodWhats 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?
• Cant generalize.
• Why store it?

12
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

13
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.

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
Sharps Methods
18
Jims Vision
Refined Natural Language Source Statements
Machine Language Component
19
Implementation

• 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