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Themes and Directions

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Themes

and

Directions

Charlie Bachman, Dist. FBCS

Hosted by the

BCS Data Management Interest Group

and UK DAMA





London, England, October 21, 2008

2

Themes

1. Layers of Abstraction



2. Evolution of Network Data Models



3. Generalized Systems







3

Motivations for Abstraction

• To break large systems into a set of layers

where a coordinated set of functions are to

be offered.

• To establish a discipline as to who may

request a given service, and who will carry

out the functions associated with that

service.

• Being able to replace one implementation

of a layer, with another, by matching its

external specifications.

4

Rules for

Layers of Abstraction

• Each layer offers services only to the

layer immediately above it.

• Each layer only calls on the services

of the layer immediately below it.

• The two adjacent layers must have

explicit agreement as to their planned

actions and responses.

• Communication within a layer is by a

well defined protocol. 5

Theme: Layers of Abstraction

Directions:

• ISO/TC97/Open Systems Interconnection

“Seven Layer” communication model



• Database Management (DBMS) model



• The “Three Schema” approach to data

independence



6

Reviewing the

OSI Layers of Abstraction,

circa 1980

1. application layer

2. presentation layer

3. session layer

4. transport layer

5. network layer

6. data link layer

7. physical layer 7

Theme: Layers of Abstraction

Directions:

• ISO/TC97/Open Systems Interconnection

“Seven Layer” communication model



• Database Management (DBMS) model



• The “Three Schema” approach to data

independence



9

Reviewing the

DBMS Layers of Abstraction,

circa 1964



1. application layer

2. presentation layer

3. physical record layer

4. virtual memory layer

5. storage device layer

11

Theme: Layers of Abstraction

Directions:

• ISO/TC97/Open Systems Interconnection

“Seven Layer” communication model



• Database Management (DBMS) model



• The “Three Schema” approach to data

independence



13

Data Independence and the

“Three Schema” Approach

Direction:

• In the mid 1970s, an ANSI/SPARC

study group began studying the problem

of sharing file data and database data

between application programs with

different histories of development and

different formats.



14

Early Practice:

• The early practice, to facilitate the transfer

of data between a pair of programs, was to

look at their formats and to write a

dedicated translation program which could

specifically mediate between the

differences in their data and relational

structures.





15

Two Schema Approach

C



B





1

A









6

1

5

2 4

3 4

16

Simplifying the Work

• They came up with the three schema approach:

i.e., the external schema, the conceptual

schema, and the internal schema.

• This required a map between each external

schema and the conceptual schema.

• This also required a map between each internal

schema and the conceptual schema.

• With these maps, taken pair-wise, they could

compute as many of direct mappings as might

be required.

17

Three Schema Approach







Computed

Maps







18

The Three Schema Approach

Yields Two Layers of Abstraction,

when the “external” and “internal” schema were

recognized as really being the same, i.e., a

“physical record” schema, as seen from

different points of view.



1. Conceptual Schema Layer





2. Physical Record Schema Layer

19

The Conceptual Schema

Needs to Understand:

1. all record, table, other record-like

constructs, as entity types,

2. all field, column, other item-like

constructs, as attributes,

3. all chains, sorted records, primary and

foreign keys, other relationship-like

constructs, as sets of binary relationships,

4. and all other data modeling constructs

appropriately, at the conceptual level.

20

Reviewing the

OSI Layers of Abstraction,

circa 1980

1. application layer 1. application layer

2. presentation layer 2. presentation layer

3. session layer 3. session layer

4. transport layer 4. transport layer

5. network layer 5. network layer

6. data link layer 6. data link layer

7. physical layer 7. physical layer

21

Reviewing the

DBMS Layers of Abstraction,

circa 1964



1. application layer

2. presentation layer

3. physical record

4. virtual memory layer

5. memory device layer



22

The Layers of Abstraction and

the Conceptual Schema provide

the means by which the

Presentation Layer could support:

1) data communication,

2) database management, and

3) middleware.



23

Themes



• Layers of Abstraction



• Evolution of Network Data Models

• Generalized Systems









26

IDS: Owner/Member Sets

(early 1960s)

• original network data model (1962)

• explicit O/M set declarations

• optional member associations

• ordered member sets

• multiple membership record types

• cascading deletes



(all, as seen in CA-IDMS today) 30

Role-Oriented Capabilities:

• Provides the ability to bind together two or more records,

when records of differing types represent the same

business, or real world entity,



* if “Dr. Smith” and “Baby Joe”

represent the same real-world person,

then,

* when the record representing Dr.

Smith is retrieved, the existence of

record of representing Baby Joe is

known and directly accessible,

and visa versa.





31

The Partnership Set

Concept

• Built on non-directed binary relationships.

• Terminates both ends of a relationship at a

partnership set.

• Each partnership set may have cardinality

constrains with regard to the numbers and

types of relationships that may be

concurrently terminated there.

• One-to-one, one-to-many, and many-to-many

cardinalities are supported.

32

Nouns, As Entities



• The boy hit the ball.



• The ball was hit by the boy.



The words, “boy” and “ball”,

are both nouns, being used as the

subject or the object of a sentence.



33

Verbs and Verb-Phrases

• The boy hits the ball.



• The ball was hit by the boy.



The words, "hits” and “was hit by,” can be used

as partnership set names, which apply to the

same relationship, based on the subject’s point

of view.



There are two entities, two partnership sets, and

a single binary relationship.

34

Dimensions and Domains



• Dimensions define information spaces used

to categorize compatible values or measures.

• Domains define the means for representing

comparable values, of the same dimension.



“Feet,” “inches,” “miles,” “millimeters,”

“meters,” and “kilometers” are names of

different domains, used to capture the

measure of a value, representing the

“distance” or “length” dimension.

39

Inheritance is the Means for

Factoring or Simplifying a Data

Structure

• This is the same thing that we do when we

factor or simplify an algebraic expression.

• This minimizes the number of data model

elements, reduces the chance of errors in

defining common constructs, and helps the

user to understand the total model.

• Inheritance and multiple inheritance offer

important data modeling opportunities.





42

The Partnership Set Oriented,

Network Data Model (today)

• started with the original IDS “owner/member set” data

model (Bachman Diagrams)



• added role-oriented capabilities



• added partnership set capabilities



• added one-to-one, and many-to-many and relationships



• added recursive relationships



• added dimensions and domains



• added inheritance

44

Themes



• Layers of Abstraction



• The Evolution of Network Data

Models



• Generalized Systems





47

What are Generalized Systems?

• They are systems designed to support the

needs of a large number of users.

• Typically, they are created by a vendor

with the intent to be sold or licensed.

• Compilers and operating systems were

early examples.

• Accounting, personnel, and resource

planning systems are newer candidates.

• Internet browsers are very new.

48

Arguments For:



• Generalized systems are quicker to install,

and should cost less than custom tailored

systems.

• Generalized systems have fewer startup

problems.

• Generalized systems have a broader set of

functions and options.

• Generalized system developers can afford

to invest the time required to build a well

engineered solution. 49

Arguments Against:

• Generalized systems will not fit my

business requirements – “we are

different.”

• Generalized systems will be too slow,

trying to solve everybody’s problems.

• Generalized systems will be wasteful of

computer and communication resources.

• Generalized systems will force us to use

associated package systems, foreign to

our operations. 50

Cord Blood Registry (Cbr)

• Cbr is in the business of collecting,

processing, freezing, and storing blood

cells from new born babies.

• These blood cells can provide stem cells

for that child, or a near relative, if a stem

cell transplant should ever be necessary.

• Capturing and preserving records of all

aspects of the business is essential to

the integrity of the business and the

storage of blood cells.

51

Cbr Enterprise Model - 2005









53

Cbr ERP Structure, circa 2005









54

Cbr ERP Structure, circa 2005









55

Item Schema object and the

specialized sub schema objects

that inherit properties from it.









56

Item Schema object and the

specialized sub schema objects

that inherit properties from it.









57

Enterprise Resource Planning:

Generalized Items:

• The focus is on the similarities and

differences in four kinds of “items.”



* material-oriented (one time use)



* resource-oriented (serial re-use)



* information-oriented (unlimited use)



* project-oriented (one-of-a-kind)

58

Inheritance: A Big Boost

• Inheritance, as a data modeling and

programming tool, is a powerful plus in

support of generalization.

• Inheritance permits factoring out common

data properties and functions for one-time

definition and the reduction of

programming errors.

• It greatly reduces the complexity of data

models by grouping similar objects

showing how they are the same and how

they are different. 59

Generalized “NdbaEntityObject”



• Cbr created the NdbaEntityObject” to

represent a super entity class, which could

support a number of specialized roles,

which all make heavy use of a set of

common data properties.





“Ndba” (New DataBase Architecture)



65

Some of the NdbaEntityObjects



• person • organization

* caregiver * carrier

** doctor * insurance company

** nurse * bank

** * supplier

* * other caregiver * customer

* hospital

* employee *

* patient * other organization

*

* other person • other NdbaEntityObject



66

NdbaEntityObjects:

Generalized Properties

• addresses, each with its purpose

• identifiers, each with purpose

• phone numbers, each with its purpose

• names, each with its purpose, and

• internet addresses, each with its

purpose



67

Mapping Packaged Systems









Communications









68

Mapping Packaged Systems





Processes









Communications









69

Mapping Packaged Systems





Processes









Communications







People Organizations



70

The Enterprise Model is the

Gold Standard

• The packaged application systems provide a

practical realization.

• They must be evaluated to see if they will

provide an acceptable solution.

• Custom-coded applications fill the vacuum.

• Data exchange between packaged systems and

between packaged systems and custom

systems must be bridged:

1. by manual processes, and

2. by middleware.



71

In Summary:

Better Abstractions Lead To:

• More robust systems

• Partitioning of work effort.

• Reduction of errors

• Interchangeable parts

• Reduction in complexity





72

Better Data Models Led To:

• Greater semantic power



• Better understanding of our business

systems



• Greater support for data independence,

and therefore greater support for

interconnecting heterogeneous

business systems.

73

Better Generalizations Lead To

Better Packaged Systems



• Finance and Accounting

• Manufacturing

• Personnel

• Marketing

• Communication

• Industry-Specific Systems

74

Better Abstractions



Better Data Models



Better Generalizations





75



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