1. © Amit Mitra & Amar Gupta ANALYZING THE REAL WORLD WHAT IS A MODEL? –ONLY REPRESENTS, AND IS NOT REALITY »Repeatable, consistent & accurate within a limited scope –PARTIAL VIEW OF REALITY =SCOPE OF MODEL REAL WORLD HAS –No Data, No process - only behavior –SCOPE OF MODEL= BEHAVIOR OF INTEREST »STIMULUS & RESPONSE: Repeatable, consistent & accurate »ABSTRACTION OF RULES WHAT IS BEHAVIOR? TECHNIQUES FOR REPRESENTING BEHAVIOR Make cookie Dough Arrange dough glob on cookie sheet Bake doughRemove Cookie BAKE COOKIE

2. © Amit Mitra & Amar Gupta BEHAVIOR RESPONSE TO A GIVEN STIMULUS –HIT METAL SHEET: it bends –HIT GLASS SHEET: it breaks INVOLVES OBJECTS, EVENTS, CHANGE CHANGE INVOLVES TIME TECHNIQUES FOR REPRESENTING BEHAVIOR –BLACK BOX »“INPUT-OUTPUT” VIEW –NODE BRANCH »“ERD TYPE” TECHNIQUES

3. © Amit Mitra & Amar Gupta PROCESS BAKE COOKIE DOUGH OVEN COOKIE COOKIE SHEET (new) COOKIE SHEET (used) COOK R E A L W O R L D O B J E C T S R E A L W O R L D O B J E C T S REAL WORLD RELATIONSHIP BEFOREAFTER TIME

4. © Amit Mitra & Amar Gupta BLACK BOX VIEW RULES & FORMULAE Operations on values of v1..v4 to derive values of v5 thru v7 at various points in TIME V1 V2 V5 V6 INPUTS OUTPUTS V7 (BEFORE)(AFTER) time valuevalue v`1 v2 v3 v4 valuevalue time v6 v7 v5 (INPUTS CHANGE) (OUTPUTS RESPOND) Examples: Oven Temperature Ingredient Quantities Examples: No. of cookies Crispness of cookies Weight of each cookie BLACK BOX (TRANSFORM) V3 V4 Example: Transform for baking a cookie

5. © Amit Mitra & Amar Gupta THE PROBLEM THE REAL WORLD CHANGES THE REAL WORLD CAN BE COMPLEX –CAN WE FILL IN DETAIL IN SUCCESSIVE STEPS? –PROCESS DECOMPOSITION V1 V2 V3 V4 V5 V6 INPUT VARIABLES OUTPUT VARIABLES V7 RULE 1 RULE 2 BEFORE (CAUSE) AFTER (EFFECT) PROCESS DECOMPOSITION = INFLEXIBLE SYSTEMS – WE DIVIDE EVEN BEFORE WE KNOW WHAT WE DIVIDE – ALMOST LIMITLESS WAYS OF DIVIDING THE BOX ■SOME WORK IN A LIMITED CONTEXT, OTHERS DO NOT ■NO PRECISE RULES FOR WHAT WILL WORK AND WHAT WON’T – THE PROBLEM OF CHAOS DATA FLOW INTERPRETATION RULE 3RULE 4 BLACK BOX

6. © Amit Mitra & Amar Gupta NODE BRANCH REPRESENTATION RULE V1 V2 V3 V4 V5 V6 V7 The future of v1 depends on its past values RULE How can we represent cross effects between v2 & v3 ? MORE “HOLISTIC” VIEW –State vs Input-output CYCLE: a loop EQUILIBRIUM: May or may not exist LOGICAL UNIT OF WORK –assumes equilibrium –physical design concept ERD IS DERIVED FROM THIS: Static rules

7. © Amit Mitra & Amar Gupta THE PROBLEM TOO MUCH DETAIL NEEDED UP FRONT HOW CAN VARIABLES BE GROUPED? GOODBAD GOODBAD There are few absolute truths

8. © Amit Mitra & Amar Gupta FACT BASED BEHAVIOR MODELING –GROUPING: THEORY OF CATEGORIES APPLIED TO REAL WORLD OBJECTS –FACT BASED ENTITY & PROCESS DESIGN PHASED INFO CAPTURE –BASED ON COMMON “IRREDUCIBLE” FACTS –CROSS SCOPE COMMONALITY THE ANSWER

9. © Amit Mitra & Amar Gupta FACTS A FACT IS... –ASSERTION: SIMPLE, COMPLEX, CAVEATS AN IRREDUCIBLE FACT... – CANNOT BE DIVIDED WITHOUT LOSING INFORMATION OR A PART OF ITS ORIGINAL MEANING »eg: product sold to customer at a place thru a distribution channel WHY DIFFERENT MODELS FOR THE SAME BUSINESS REQUIREMENTS? –DIFFERENT GENERALIZATIONS AND SPECIALIZATIONS OF THE REAL WORLD –NEED FOR STANDARD OBJECT TAXONOMY –NEED TO START WITH IRREDUCIBLE FACTS

10. © Amit Mitra & Amar Gupta BUSINESS RULES Business Rules are… –Policies, practices, facts, assertions and rules about required business behavior –Individually simple, complex in combination The Business Rule Approach focuses systems development on business constraints & opportunity –Unified view of knowledge about products & customers –Separated from technology constraints –Business rule changes can be automatically reflected in applications Framing business rules in a real world object ontology helps avoid repetition & unmanageable “rule tangling” for the most frequently used rules of the enterprise –Combined with Object Inheritance it can provide a powerful method of building systems that will facilitate, not control change

11. © Amit Mitra & Amar Gupta An example of how business rules are assembled from meanings…

12. © Amit Mitra & Amar Gupta MODEL COMPONENTS OBJECT –INSTANCES –INSTANCE MAY PLAY MULTIPLE ROLES AT THE SAME TIME –SET THEORY –FOUR SET OPERATIONS »SUBSET, UNION, INTERSECTION, CARTESIAN PRODUCT »BOREL OBJECTS PROPERTIES –ATTRIBUTES: DATA, STATE –EFFECT OF EVENT »FINITE NO. OF POSSIBLE OPERATIONS ON OBJECT DOMAIN (an abstraction) –COMMON TO MANY ATTRIBUTES AND OBJECTS –NORMALIZES REAL WORLD MEASURABILITY INFORMATION –NOMINAL, ORDINAL, DIFFERENCE & RATIO SCALED –DIFFERENCE & RATIO SCALED DOMAINS MUST HAVE ATLEAST ONE, AND MAY HAVE MANY UNITS OF MEASURE (uom) –EACH UOM MAY HAVE MANY PHYSICAL REPRESENTATIONS: (FORMATs) OFTEN CONFUSED WITH EACH OTHER

13. © Amit Mitra & Amar Gupta ASSUMPTIONS PROPERTIES (ATTRIBUTE VALUES & RELATIONSHIPS) CHANGE IN RESPONSE TO DISCRETE EVENTS CONSTRAINTS ON ENTITIES CHANGE IN RESPONSE TO DISCRETE EVENTS DETERMINISTIC SYSTEM Time slice (a single state of an instance of an object) OBJECT CLASS Present Past V1 V2 V3 V4 V1 V2 V3 V4 Instance Time V1 V2 V3 V4

14. 14 © Amit Mitra & Amar Gupta SETS & SET OPERATIONS A B A BB set intersection A  B is the set of objects that are members of both A and B.  Multiple inheritance A-B B-A A-B is the set of objects that are members of set A, but not B. B-A is the set of objects that are members of set B, but not A. C C AA set difference subset of A C  A implies all members of C are also members of A, but not vice-versa.  Inheritance (Data, behavior & constraints) A B A BB A  B is the set of object that are members of either set A, or set B, or both. set union 

15. 15 © Amit Mitra & Amar Gupta X SET C=AB a1 a2 a3 SET A b1 b2 SET B X (a1, b1) (a1,b2) (a2, b1) (a2, b2) (a3, b1) (a3, b2) = CARTESIAN PRODUCT OF SETS A AND B SET OPERATIONS (CONTINUED)

16. © Amit Mitra & Amar Gupta A Knowledge Artifact is abstract

17. © Amit Mitra & Amar Gupta Attributes of Objects “Value” includes “Any” (i.e., “All”) “Don’t Know” “Null”

18. © Amit Mitra & Amar Gupta Relationships are objects Relationships are also features of objects

19. © Amit Mitra & Amar Gupta What is a Metamodel? Information about information –A model of information that structures the concept of “model” Consists of “Meta-Objects” –Eg. “Object Class”, “Object Instance”, “Relationship”, “Process”

20. © Amit Mitra & Amar Gupta Metamodel of State

21. 21 © Amit Mitra & Amar Gupta Qualitative Attribute FORMAT convert to 0 or 1 [convert from] Qualitative Attribute Ordinal Attribute Nominal Attribute is a Subtype of Ordinal/Nominal Partition is expressed by 1 or many [express none, or many] (INHERITED FROM DOMAIN) QUALITATIVE DOMAIN Qualitative Value The two sets are equal is a Subtype of Must take only 1 [may be value of none, or many]

22. 22 © Amit Mitra & Amar Gupta Quantitative Attribute FORMAT UNIT OF MEASURE is expressed by 1 or many [express none or many] convert to 0 or 1 [convert from] convert to 0 or 1 [convert from] Quantitative Attribute Difference Scaled Attribute Ratio Scaled Attribute is a Subtype of Difference/Ratio Scaled Partition is expressed in 1 or many [express none or many] (INHERITED FROM DOMAIN) Quantitative Value The two sets are equal QUANTITATIVE DOMAIN is a Subtype of Must take only 1 [may be value of none, or many]

23. © Amit Mitra & Amar Gupta INCREASING INFORMATION CONTENT QUALITATIVE DOMAIN QUANTITATIVE DOMAIN FORMAT UNIT OF MEASURE is expressed by 1 or many [express] is expressed by 1 or many [express] is expressed by 1 or many [express] convert to 0 or 1 [convert from] convert to 0 or 1 [convert from]

24. © Amit Mitra & Amar Gupta Subtype of NOMINAL VALUE ORDINAL VALUE DON’T CARE ALL Partition of [partitioned by] MEANINGFULNESS RATIO SCALED VALUE Subtype of DIFFERENCE SCALED VALUE Metamodel of Value Subtype of Subtype of Instance of NIL NULL (MEANINGLESS) (absence of magnitude )

25. © Amit Mitra & Amar Gupta KINDS OF DOMAINS MUST EVERY OBJECT HAVE ATTRIBUTES ? (can be logically [automatically] inferred) (impossibility can be logically [automatically] inferred )

26. 26 © Amit Mitra & Amar Gupta Subtype of NOMINAL DOMAIN ORDINAL DOMAIN DOMAINS WITH NIL VALUES DOMAINS WITH LOWER BOUNDS UNKNOWN DOMAIN ORDINAL DOMAIN WITH NIL VALUES RATIO SCALED DOMAIN Subtype of DIFFERENCE SCALED DOMAIN Subtype of (impossibility can be logically [automatically] inferred ) (can be logically [automatically] inferred)

27. © Amit Mitra & Amar Gupta Names of object properties emerge naturally from the structure of information –Names reflect Meaning –Meanings are patterns of information NAMING RULE: Object Class (optionally possessive form), Domain, IN Unit of Measure –Car (‘s) Color Multiple interactions between object and domain needs qualifier –E.g. Car body Color, Car Seat Color –Person (‘s) Color-Preference Person’s (Visual) Car-Color-Preference –String (‘s) Length String (‘s) Length IN Feet Person Length?; Room height Length? Room Width Length?!! NAMING RULE: All nominal domains are subtypes of the Type domain (aka Class, Category) – Classify cars into sedans, hatchbacks, SUVs etc Object = Car, Domain = Type, Attribute Name = Car Type NAMING RULE: All ordinal domains are subtypes of the Rank domain – Titles in an organization The same title may imply different levels in different organizations –VP in the insurance industry is 2 levels above a Director; Director in a bank is several levels above VP Object = Title, Domain = Rank, Attribute Name = Title Rank ATTRIBUTE NAMES (difference & Ratio Scaled domains only) Analysis itemDesign item

28. © Amit Mitra & Amar Gupta NAMING RULE: Object Class, Domain, IN Unit of Measure EXPRESSED IN Format –Object Class must be singular – Domain name must be singular –Unit of Measure must be Plural –String Length Object = String, Domain = Length, UOM= Feet, Format = Numeric Digits –Attribute Name = String Length IN Feet EXPRESSED IN Numeric Digits Format = English Speech –Attribute Name = String Length IN Feet EXPRESSED IN English Speech REALIZING ATTRIBUTES IN A COMPUTER SYSTEM – What is an attribute? Current technology does not recognize the meaning of an attribute or the pattern that creates attributes Each tangible expression is considered a separate and independent attribute in most database systems and many CASE tools But times are a –changing! –XML partly separates the meaning from its expressions –The Metamodel of Knowledge can be the blue print for tools better aligned with the real world Tangible expression ATTRIBUTE NAMES (continued) DOMAINOBJECT Attribute is a Subtype of a single is a Property of a single [is a class of none, or several attributes] [state described by 1 or more] FORMAT expressed innoneor more UOM expression of only 1

29. 29 © Amit Mitra & Amar Gupta IDENTIFYING DOMAINS Yes. Eg:Policy premiums Is the attribute a basis, or potential basis, for creating mutually exclusive entity subtypes? No The attribute is at least Nominal Scaled, and may be Ordinal, Difference or Ratio Scaled. Can the values of the attribute be arranged in a natural order from least to most? #2 The attribute is at least Ordinally Scaled, and may be Difference or Ratio Scaled. Can attribute values be meaningfully subtracted? #3 The attribute is at least Difference Scaled, and may be Ratio Scaled. A Nominally Scaled Attribute. An Ordinally Scaled Attribute. No Eg: Color preference. No Eg:Policy effective date No E.g: Color of car Are attribute ratios meaningful?#4 #1 A Difference Scaled Attribute. #2 Yes. A Ratio Scaled Attribute. #3 #4 Yes. #1 STOP May be “fuzzy” concept. Rethink.

30. © Amit Mitra & Amar Gupta “Although I am one, I shall become Many.” - passage from Chandogya Upanishad, an ancient text from India, on manifestation of material reality, translated by Swami Prabhupada

31. © Amit Mitra & Amar Gupta Reading Assignments 1. Supplementary materials in Modules 1 & 3 at http://next.eller.arizona.edu/books/ 2. Prologue and Chapter 1 of

32. © Amit Mitra & Amar Gupta Formatting Rules Sequencing Rules Display OBJECT CLASS INFORMATION SOURCING CONNECTION (OPTIONAL) INCLUSION/EXCLUSION SET(S) Components of View must contain only 1 [contained in 0 or more] Formatting Rule Sequencing Rule (OPTIONAL) INCLUSION/EXCLUSION SET GROUP(S) must contain only 1 [contained in 0 or more] May contain 0 or 1 [contained in 0 or more] METAMODEL OF VIEW VIEW ACTOR VIEW Expressed by 0 or more [of 1 or more] OBJECT Display Formats for 0 or more [formatted by 1 or more] must contain only 1 [contained in 0 or more] Intersection of 0 or more [Intersection of 0 or more ] Union of 0 or more [Union of 0 or more ] Intersection of 0 or more [Intersection of 0 or more ] Union of 0 or more [Union of 0 or more ] Selection criteria

33. © Amit Mitra & Amar Gupta Names of object properties emerge naturally from the structure of information –Names reflect Meaning –Meanings are patterns of information NAMING RULE: Object Class (optionally possessive form), Domain, IN Unit of Measure –Car (‘s) Color –Person (‘s) Color-Preference Person’s (Visual) Car-Color-Preference –String (‘s) Length String (‘s) Length IN Feet NAMING RULE: Nominal domains may be called Type PERSON VISUAL CAR COLOR PREFERENC E ATTRIBUTE NAMES (difference & Ratio Scaled domains only) DOMAINOBJECT Attribute Attribute is a Subtype of a single Attribute is a Property of a single [Domain is a class of none, or several attributes] [state described by 1 or more] PREFERENCE COLOR PREFERENCE PERSON CAR Visualize Car see

34. © Amit Mitra & Amar Gupta DOMAINOBJECT Attribute Attribute is a Subtype of a single Attribute is a Property of a single [Domain is a class of none, or several attributes] [state described by 1 or more] ATTRIBUTE NAMES (continued)

35. © Amit Mitra & Amar Gupta DOMAINOBJECT Attribute Attribute is a Subtype of a single Attribute is a Property of a single [Domain is a class of none, or several attributes] [state described by 1 or more] HOW CAN WE REALIZE ATTRIBUTES IN A COMPUTER?

36. © Amit Mitra & Amar Gupta DOMAINOBJECT Attribute is a Subtype of a single is a Property of a single [is a class of none, or several attributes] [state described by 1 or more] FORMAT expressed in none or more UOM expression of only 1

37. 37 © Amit Mitra & Amar Gupta Subtype of NOMINAL DOMAIN ORDINAL DOMAIN DOMAINS WITH NIL VALUES DOMAINS WITH LOWER BOUNDS UNKNOWN DOMAIN ORDINAL DOMAIN WITH NIL VALUES RATIO SCALED DOMAIN Subtype of DIFFERENCE SCALED DOMAIN Subtype of