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ANALYZING THE REAL WORLD

Arrange dough glob on cookie sheet. Make cookie Dough. Bake dough. Remove Cookie. 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. BAKE COOKIE.

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ANALYZING THE REAL WORLD

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  1. Arrange dough glob on cookie sheet Make cookie Dough Bake dough Remove Cookie 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 BAKE COOKIE • 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

  2. 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. R R E E A A L L W W O O R R L L D D O O B B J J E E C C T T S S PROCESS BEFORE AFTER DOUGH COOKIE SHEET (used) OVEN BAKE COOKIE COOKIE COOKIE SHEET (new) REAL WORLD RELATIONSHIP COOK TIME

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

  5. V7 RULE 4 RULE 3 THE PROBLEM INPUT VARIABLES OUTPUT VARIABLES • THE REAL WORLD CHANGES • THE REAL WORLD CAN BE COMPLEX • CAN WE FILL IN DETAIL IN SUCCESSIVE STEPS? • PROCESS DECOMPOSITION BLACK BOX BEFORE (CAUSE) AFTER (EFFECT) V1 V6 RULE 1 V2 RULE 2 V5 V3 V4 • 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

  6. The future of v1 depends on its past values RULE V1 RULE V6 RULE V2 RULE How can we represent cross effects between v2 & v3 ? RULE V7 RULE V3 RULE RULE V4 V5 NODE BRANCH REPRESENTATION • 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 RULE

  7. THE PROBLEM • TOO MUCH DETAIL NEEDED UP FRONT • HOW CAN VARIABLES BE GROUPED? There are few absolute truths GOOD BAD BAD GOOD

  8. THE ANSWER • 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

  9. 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. 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. An example of how business rules are assembled from meanings…

  12. 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. V1 V1 V1 V2 V2 V2 V3 V3 V3 V4 V4 V4 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) Past Instance Time Time Present OBJECT CLASS

  14. A • B A • B C C • A I A • B is the set of object that are members of either set A, or set B, or both. SETS & SET OPERATIONS A A B-A B B set difference set intersection A-B set union 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 is the set of objects that are members of both A and B. • Multiple inheritance 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.

  15. SET C=A B X (a1, b1) (a1,b2) (a2, b1) (a2, b2) (a3, b1) (a3, b2) SET OPERATIONS (CONTINUED) CARTESIAN PRODUCT OF SETS A AND B SET A SET B a1 a2 a3 b1 b2 X =

  16. A Knowledge Artifact is abstract

  17. Attributes of Objects • “Value” includes • “Any” (i.e., “All”) • “Don’t Know” • “Null”

  18. Relationships are objects Relationships are also features of objects

  19. 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. Metamodel of State

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

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

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

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

  25. MUST EVERY OBJECT HAVE ATTRIBUTES ? KINDS OF DOMAINS (impossibility can be logically [automatically] inferred ) (can be logically [automatically] inferred)

  26. ATTRIBUTE NAMES Analysis item Design item • 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 (difference & Ratio Scaled domains only)

  27. UOM expressed in none or more ATTRIBUTE NAMES (continued) is a Subtype of a single is a Property of a single Attribute FORMAT OBJECT DOMAIN [state described by 1 or more] [is a class of none, or several attributes] expression of only 1 Tangible expression • 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 • StringLength • Object = String, Domain = Length, UOM= Feet, Format = Numeric Digits • Attribute Name = StringLength IN Feet EXPRESSED IN Numeric Digits • Format = English Speech • Attribute Name = StringLength 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

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

  29. “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

  30. 1. Supplementary materials in Modules 1 & 3 at http://next.eller.arizona.edu/books/ 2. Prologue and Chapter 1 of Reading Assignments

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