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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

Intro to Sharp’s Methods

Jim Carpenter

Bureau of Labor Statistics

OTSP Seminar

May 24, 1999

who is dr john sharp
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

why is he here at bls
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
cmm project key technologies
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,...
cmm project demos
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)
demo ppi data dictionary tentative
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
sharp s information modeling methods
Sharp’s Information Modeling Methods
  • Function: requirements for database
  • Basis: Natural Language Modeling
  • Benefits: quality data & metadata
sharp s method what s in scope
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)
sharp s method what s not in scope
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
key concept fact type
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
trivia an isolated fact fact 1 jack gave the red ball to jill
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?
generalizing with more facts
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
more objects roles in a fact type
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.
generalize the objects
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.
more generalizations
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.
sharp s methods
Sharp’s Methods

Source

Statements

Sharp’s

Procedure

Valid

Fact Types

Transform

Data Model

Valid

Fact Types

Cluster

Process Model

jim s vision
Jim’s Vision

Network

of

Models

Models, too!

Refined

Natural

Language

Source

Statements

Machine

Language

Component

implementation
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