1. 1 Introduction to Sharp’s Methods Striving for Engineering Precision in Information Systems Jim Carpenter Bureau of Labor Statistics, and President, DAMA-NCR Seminar on Validating Models May 24, 1999: BLS only May 25, 1999: DAMA-NCR Draft Version 1.4 dated 5/17/99 8 a.m.

2. 2 Agenda Part I: History Context (coming) Part II: Technology Framework (good start) Part III: Business Context (if time)

3. 3 Part II: Technology Framework Describe a technology context for Sharp’s methods and all other methods Provide contrast between Sharp’s methods and other methods Introduce the essential concepts in Sharp’s methods

4. 4 Summary Context –Universal Systems Development Process: a network of transformations between models Contrast –Sharp’s methods: analysis of instances –Usual methods: conceptual debate Concepts –Valid fact type –Object –Predicate

5. 5 A Central Theme: How to Describe a System Answer: use a network of descriptions –Starting from a bunch of English sentences. –Can use any natural language –Ending in the ultimate description, i.e., the system itself –Information Technology: the executable binary code. –Architecture: the building –Manufacturing: the product

6. 6 Some Basic Notions Description = Model (for our purposes) Model = a description in some language –The Modeling Language Language = a set of concepts with representations –also has rules but we’ll gloss over this for now Concept = a unit of thought, a notion Representations –sound –word = a group of letters –graphic –mathematical symbol

7. 7 The Notion of a Model A model is a projection (translation) of our knowledge of the real world onto a fixed set of concepts. Example: –Knowledge: “This person I (Jim) am pointing at right now who I know as John owned a thing we call a car yesterday.” –Concepts: Object: relationship: –Projection: a “sentence” in the modeling language: John Car ownership Jim point Note: Dimension of “when” is lost and other subtle info. We could recover some info by extending the “sentence”. But when to stop?

8. 8 Data & Process Modeling Languages –Entity Relationship  UML –Data Flow  Work Flow Programming Languages Software packages ( Microsoft PowerPoint, etc.) Linear Models (Math & Statistics) –Vector, V (observation) –Space,  (hypothesis) –Projection, P (statistic) –Difference, E (Error) Other Technical languages - branches of science Natural Languages - English, French, Japanese, … Natural Language Modeling Language Some Modeling Languages V  P E

9. 9 UML - a standard language ( Unified Modeling Language) A standard set of 90 some elements (concepts) established by OMG Each element has a fixed graphical representation Nine (overlapping) bags of elements are defined Each bag is called a diagram type = dialect –Use Case Diagram  Collaboration Diagram –Class Diagram  Activity Diagram –Object Diagram  Component Diagram –State Diagram  Deployment Diagram –Sequence Diagram

10. 10 Natural Language Modeling Language Words, sentences, equivalence John loves Mary. = Mary is loved by John. Object - thing we want to know facts about (John, Mary) Predicate - the glue that holds the object together, words that give an object meaning, what can be known about it: attributes & relationships (… loves …) = (... is loved by...) Note: “…” is a placeholder Complex sentence –Jack gave the ball to Jill –Object: (Jack, ball, Jill) like a parameter set (variable) –Predicate: ( … gave … to … ) like a function (fixed)

11. 11 Class: defined by the predicate Fact: Jack gave the ball to Jill Context 1: Jack is one of 5 boys in a room with Jill Object: Jack (Class of boys) Predicate: … gave the ball to Jill Context 2: Jack & Jill are among 5 children in class Object: Jack, Jill (Class of children) Predicate: … gave the ball to … Context 3: Jack & Jill and toys in a classroom Object: Jack, ball, Jill (Class: children, toys) Predicate: …gave the…to

12. 12 Fact Types: describing 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. Fact 3: Jack gave the red ball to Jane. Fact type: A boy gave the red ball to a girl. Fact 4: Jane gave the red ball to Jack. Fact type: A child gave the red ball to a child. 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.

13. 13 Fundamental Axiom of Information Technology Idea occurs repeatedly in IT –Basis for communication –Basis for concept of “round trip engineering” –Basis for OMG’s & MDC’s tool interoperability architectures –Bob Schmidt’s book Data Modeling for Information Professionals in a discussion of whether modeling is possible. It is possible to map some elements from one language into elements of another. In other words: languages may have similar structures and rules.

14. 14 Mappings from NLM NLM maps well the principle concepts of existing data & process modeling languages, including business rules. The differences are in the methodologies To compare methodologies, we’ll consider a universal framework for the development process...

15. 15 The Universal Systems Development Process: A network of transformations between models. Starting model is a set of statements representing the knowledge of the subject domain expert Ending model is the system, a set of executable binary code (in a machine language) Intermediate models –provide insightful views based on subset of statements –are kept as architectural documentation of the system. Network: The Zachman Framework is a metamodel of a network of models –http://www.dama-ncr.org/Library/Frameworks.ppt

16. 16 Isomorphism, Validity, & Equivalence ISOMORPHIC MAPPING (a very nice mathematical concept) –A mapping from one set to another (ordered pairs) –Properties of the mapping One-to-one (some rule which pairs the elements) Onto (no elements left over) VALID MODEL: if there is a subset of statements that is isomorphic to the model VALID SYSTEM: if the entire statement set is isomorphic to the system EQUIVALENT MODELS: if there is an isomorphic mapping between them

17. 17 Fundamental Problems of Systems Development 1How to capture a complete and accurate set of well formed statements in some natural language? 2How to transform a set of well formed statements into a model in a given modeling language? 3How to transform a model from one language to another? (Some good news!) 4What set of modeling languages is sufficient to capture all of the knowledge embodied in the statement set?

18. 18 Model-guided discussions with subject expert –E.g., Use Case Analysis (many variations, see articles at http://www.crim.ca/~aseffah/investigation/use_case.htm ) Existing documentation –Statements of mission & objectives, methods handbook,... –Models (Sharp’s Lecture) –Database schemas –Forms used to collect data –Models of application packages used!!! –Code and code libraries Guided queries based on existing documentation Problem 1: Capturing the Statements reverse engineering & validation

19. 19 Problem 1: Refining the Statements Usual methods: conceptual statement alternatives are compared and debated –example: is a “passenger” an entity or state of a person? Sharp’s method: –Each conceptual statement is analyzed by a truth comparison of specific instances –Refined statement is called a valid fact type well formed NLM sentence verifiable by tracing to yes/no answers to specific questions addressed to subject experts

20. 20 Fact Type An assertion that a sentence formed from a set of domains and a predicate could be true for all (allowable) instances of the objects in the domains. Person identified by social security number has the name. Domains:, Predicate: … is identified by a … Instances: 123-45-6789 Jane Doe 987-65-4321 Jane Smith

21. 21 Problem 2: Transforming the statement set Usual method: mental process (usually treated as part of problem 1) –E.g.: Use Case Analysis (scenarios) Identify the “objects” then “classes” Identify the “relationships” and “attributes” Sharp’s method: algorithm –Well defined map from set of valid fact types to a unique model in any given language –Use the translator hub (next slide)

22. 22 Problem 3: Translating between languages Pair-wise translation –Each tool does N translations –N x N translations total Translator hub (repository) –Each tool does 2 translations (to & from the hub) –2 x N translations total Standard hubs –OMG: MOF –MDC: OIM (parallel structures???)

23. 23 Problem 4: A Sufficient Network of Models (tentative ideas)