Data Model Views

Published in April 2000

About Views and the Conceptual Schema

The notation of object models exactly corresponds to that of the entity/relationship (data) models that preceded them. Where entity/relationship models portrayed “entities”, object models show
boxes representing “object classes”. In either case, these ultimately are definitions of things of significance to the business. Where data models show “relationships” between them, object
models show “associations”. In either case these are representations of the number of occurrences of an entity/class that may be related to an occurrence of another entity/class.

Where they are different is in how the two kinds of models are used. While data modelers may begin with sketches and representations of the concrete things seen by the business community,
most apply certain disciplines to make sure that each attribute in a model appears only once, and to make sure that it can be retrieved from any of several directions. Moreover, they often combine
entities to produce more abstract ones that encompass a larger conceptual space. Object modelers are less concerned with these kinds of discipline. They are more interested in making sure that the
object classes in their models represent exactly the object classes seen by each viewer. Where an e/r model would represent the employment of a person by a company, an object model might simply
show an employee.

There is merit in both of these representations. The trick is to bring them together coherently. Unfortunately, as of this writing no CASE tool really does this well, so documenting the connection
between two models has to be done in MS Word or Excel.

This article is intended to present the differences between the external views of data represented by object models and the internal, “conceptual” views represented by entity/relationship

A data model (specifically, an “entity/relationship” model) has two purposes: first, a systems analyst uses it to confirm with prospective system users that he or she understands the nature of
the business involved; second, a system designer uses it as the blueprint for the underlying structure of a new or revised system.

Data models have often been used solely to meet the second objective. They help data base designers visualize data base structure, and thereby to clarify their thinking.

They often fail to meet their first objective, however, when they fail to account for different points of view of the company’s data. They are either drawn in very concrete terms, to reflect
the particular perspective of one part of the user community, but without recognition of more enterprise-wide issues, or they are drawn in more abstract, general terms, to attempt to address more
general issues, often at the expense of general understanding. In either case, only part of the picture is being represented.

Originally, data base theory envisioned four distinct perspectives for the data in a data base, as shown in Figure 1. First, the conceptual schema represents the structure of data as they
exist throughout an organization. The facts in the conceptual schema are true everywhere. Second, the external schema represents data as seen by each user. Each user may have a different
external schema, with the set of them overlapping and, in practice, with them being not entirely consistent. The logical schema represents data in the terms used by a particular database
managment system (tables and columns, network segments, etc.). Finally, the internal schema represents the structure of data as they are stored physically on the computer.

This has proven a useful view of things, and it stimulated the development of relational data base management systems. With a relational data base management system, you could now describe the
conceptual schema in terms of the logical schema as a structure of tables and columns. The software would take care of the internal schema, which could then be tuned independently of the logical
one. Moreover, thanks to the SQL language, you could specify the “views” individual users have of the data. The software would keep track of the relationships between these and the
conceptual/logical schema.


Figure 1: The Three-schema Approach

In practice, however, relational tools haven’t been used this way. Early relational data base management systems were slow, even with the tools available for logical tuning, so table
structures were de-normalized and departed from the conceptual model. The design of tables and columns became part of the logical schema design, just as the design of networks and hierarchies were
part of the logical schema design under earlier technologies. Even though the data base management systems have gotten faster, the practice remains.

Even where they are appropriate, SQL views often have often not been used, “for performance reasons.” Where SQL views are used, however, with the database no longer representing the conceptual
schema, they are now linked directly to the logical schema.

The only place where the conceptual model still exists is in the data model. The data model is a drawing — intended to represent the business in its most fundamental sense — showing
entities as “things of significance about which an organization wishes to hold information”.

Data modeling itself, however, as practiced, is not immune to biases toward both the logical and external schemata: Some analysts will draw models of an external view, creating entities for each
concrete thing seen by users, without regard for underlying similarities and principles. Others use the data model as a database design tool, simply reflecting the actual or intended database
design structure.

A true conceptual model, on the other hand, will show only things which are fundamental to the business, of which most of the things people see are examples.

All of this is merely another expression of the original problem. Different people in the organization have different views of data. If a true conceptual model can be drawn, it will not necessarily
be recognizable to all in the enterprise. If the model is more concrete, in deference to a particular department, it will reflect a particular external view, and people in other departments may
either not recognize it or they may disagree with it. Even the physical database design represents but another view of the data, that is entitled to representation.

As shown in Figure 2, the three-schema architecture has gone astray.

So data modeling, our tool for developing conceptual models, has become removed from the process of specifying and developing systems. When the data model represents the conceptual schema, the
external schemata are no longer connected to it, and these external schemata are not available at all in data model form. And while the conceptual schema often is the starting point for physical
data base design, it is very difficult to keep the link current as requirements change.


Figure 2: The Conceptual Schema Adrift

What we need is the ability to return to Figure 1. If the facilities to support it were available, an analyst in early discussions with users would take advantage of data model graphics and
sentences to build the models in the users’ concrete terms. These models could be “object models.” The analyst would then translate the result into generalized, corporate conceptual models.

When the designer then maps the conceptual data model to a data base design, he or she could map the external data models directly to SQL views and screens. Alternatively, stored procedures could
be created to implement the object model views of the world. In other words, a default view design could be generated along with a default data base design.

CASE tools, as they exist today, do not support the idea of data model views. At best, some (though by no means all) CASE tools allow the user to draw subsets of the total set of entities and
relationships. This facility can be used selectively to draw entities that pertain to a particular part of the business. Where one can achieve a specific view by simply selecting entities to show,
this is adequate.

No CASE tool (in your author’s experience), however, is flexible enough to allow one to represent a view of a data model where the relationships or entity names in the view are
different from those in the underlying model. We need such a facility — along with the ability to document the relationships between this view and the underlying complete model.

The following sections present some examples of situations where this view mechanism would be useful.

Examples of views

At least five situations give rise to the need for views of data models:

  • Hierarchies
  • Departmental views
  • Combining entities and relationships
  • Cross-departmental views
  • Meta model views


First, unlike data flow diagrams, data models are not inherently hierarchical, which makes it difficult to produce “summary” or “high level” data models. Upper management is often unwilling to
sit through presentations of excessive detail, but there is not a convenient, standard way of showing just the most important elements.

As mentioned above, one can select entities for presentation, based on their importance to the audience, but simply failing to include a low level entity on a diagram may not be enough: If entities
are left out of the presentation, relationship definitions themselves change. Figure 3 shows a relationship between two high-level entities. WORK ORDER and PERSON appear to a manager to have a
simple many-to-many relationship, but at lower levels of detail the relationship is much more complex .


Figure 3: A Hierarchical View

(It is not always easy to judge what constitutes a “most important” entity, by the way. Some entities are clearly “high-level” or “detailed”, but the significance of others depends on the
manager looking at the model. In this article, however, we are discussing the ability of the tools to represent views — the abilities of the analyst to represent views
intelligently is a separate issue.)

Note that this is not simply a matter of eliminating the TIME SHEET ENTRY and LABOR ASSIGNMENT from the summary diagram in Figure 3. The relationship pair in the summary diagram (“Each WORK ORDER
may be worked on by one or more PEOPLE”, and “Each PERSON may be involved with one or more WORK ORDERS”) is different from those in the detailed diagram. While you
could simply add the summary relationship pair, and show or not show the line when appropriate, we currently have no mechanism to show that these different relationships are logically equivalent.

Departmental Views

The second need for data model views comes from the fact that by building diagrams to describe a company as a whole, we often produce models that are not in terms each department can readily
understand. Different departments may look at similar things in completely different terms.

Regardless of the management level concerned, all people work in an environment of concrete things: Users know about “parts”, “instruments”, “sub-assemblies”, etc. They don’t know about
the more general “items” or even (except for the accountants) “assets” that would appear on a more enterprise-wide model. At the very least, any entity on a model could be documented with
examples, but better yet would be to make the entity names themselves reflect the concrete world of the user. Certainly one must portray relationships as the department sees them.

Where different departments have different names for the same things, each should be able to see a model with its own names on it. In Figure 4, PRODUCTION FACILITY and MEASURING DEVICE are two
broad categories of hardware (ITEM TYPE) in a plant. Individual departments, however, deal with subsets of these things. The maintenance department deals with PIECES OF EQUIPMENT, the LABORATORY
deals with INSTRUMENTS, and the process control department deals with TAGS.


Figure 4: Item Types

What is needed here is the ability to specify that a particular department’s entities and relationships exist — showing these in a data model — and to specify also the links
between these and a conceptual model whose entities and relationships may look quite different. The view entities and relationships are manipulated in exactly the same way as real entities, but the
data dictionary understands how they are different.

Another example is shown in Figure 5: A maintenance engineer may view a “ROTATING PART” as an entity of significance. In the conceptual model, this is simply another kind of EQUIPMENT. A data
model view language would permit definition of the following:

(each of which must be of EQUIPMENT TYPE) WHERE
= ‘Rotating part’” ;


Figure 5: A Departmental View

Relationships change as well in different views. For example, the departmental model may show that a PRODUCT is of one and only one PRODUCT TYPE, but from the point of view of the company,
a PRODUCT may be of one or more PRODUCT TYPES.


Figure 6: Another Departmental View

The mechanism for keeping these two views synchronized is what we seek here. (See Figure 6.)

In both these cases, simply hiding entities is not sufficient. Relationships are different between the views.

Combining Entities and Relationships

The third area where views of data models would be useful is the practice of incorporating a thing’s relationships into the definition of the thing itself. This is a practice that conceptual data
modelling tries to stamp out, but that audiences insist on doing.

The most common example of this is the entity VENDOR, which modelling purists know is only a PARTY (a PERSON or an ORGANIZATION) which is a seller in a CONTRACT. (See Figure 7.) That is,
the word “vendor” contains within its definition not only the thing itself (a person or organization), but its relationship with other things.

Many people are more comfortable, however, with the use of VENDOR and CUSTOMER entities (probably because this is the way we have always built purchasing and sales systems), even though the
underlying entities (PERSON and ORGANIZATION) are the same, whether they are buying or selling. In the interest of harmony, it should be possible to portray the more familiar entities.

Again, a view syntax would allow us to say:

PARTY is “seller in” one or more CONTRACTS;


Figure 7: An entity Plus a Relationship

Cross-departmental Views

A fourth area where data modelling views would be useful concerns entities that relate to many or all aspects of the business, in a way that redefines local entities. An accounting transaction, for
example treats quite different entities (such as DEPARTMENT, or ASSET) as “cost centers”. (See Figure 8.)

In some cases a COST CENTER may be an EXPENSE ACCOUNT for a particular DEPARTMENT and ASSET, or it may just be the set of all expense accounts for a DEPARTMENT. The model could be drawn so
that each COST CENTER must be for either one EXPENSE ACCOUNT, or for one DEPARTMENT, but that belies the fact that people who are concerned with COST CENTERS don’t want to
know about EXPENSE ACCOUNTS, and vice versa.


Figure 8: Cross-company Views

In another example, labor and parts usage charged to a work order require complex models in their own right to describe accurately all the relevant relationships specific to each. But to a project
manager, they are simply “resources”.

Meta Model Views

The real world is not, in fact, relational. Situations arise where different occurrences of an entity have different attributes, depending on the category of the occurrence. This could be handled
by the use of sub-types, except in those cases which are very dynamic, with categories being added and deleted frequently. An example is PRODUCT, where the attributes of a fruit are quite different
from the attributes of a computer.

The solution to this problem is shown in Figure 9. This model defines attributes for each ITEM TYPE, via one or more ATTRIBUTE ASSIGNMENTS. Each ATTRIBUTE ASSIGNMENT is of one ATTRIBUTE to an ITEM
TYPE. A VALUE of the ATTRIBUTE can then be defined for an ITEM of that ITEM TYPE.


Figure 9: A meta model of a variable-length table

This model is too abstract for many audiences, however. Many people only want to know that an ITEM is of one and only one ITEM TYPE, and that the format of a particular ITEM will depend on
its ITEM TYPE. The intricacies of the model that accomplishes this in a relational environment are of no interest. A CASE tool could present this, even though the dictionary contains the more
complex underlying model.

Nothing in the existing tools prevents us from drawing meta-models such as this one. What is missing, however, is the ability to link this model to one that shows ITEM the way everyone sees it —
as a single entity with variable length rows.


In each of these cases, a systematic way of presenting these views of data would significantly increase data modelling’s power as a tool for managing data base design.

CASE tools should officially recognize the concept of “object view” and have a language for describing one, as presented here. Among other things, this would involve:

1. Separating drawings from their underlying model. This would allow a user to select which entities and relationships would appear in a particular drawing. This is a basic requirement which some
CASE tools allow for now.

2. Making it possible to select the synonym to be displayed as the name of an entity in a particular drawing.

3. Making it possible to define a “virtual entity” (object class) whose definition is derived from other entities and relationships.

4. Making it possible to define a “virtual relationship” (association) in terms of (or as a synonym for) one or more other relationships and, taking into account any intermediate entities.

5. Documenting the links between virtual and real entities and relationships, and manipulating them in reports and selection criteria. (“Show me the details behind this relationship…”) It would
be a nice touch (although not necessary) also to indicate such a virtual object on the drawing. For example a “(v)” could be placed next to the object name.

6. Making it possible to show a sub-type entity without having to show its super-type. Indeed, different applications should be allowed to own different sub-types.

These are only the most obvious requirements. Clearly the full implications of this idea have yet to be explored. Your author would welcome comments from anyone who has struggled with this problem
and has further ideas on how to address it.

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David Hay

David Hay

In the Information Industry since it was called “data processing”, Dave Hay has been producing data models to support strategic and requirements planning for thirty years. As President of Essential Strategies International for nearly twenty-five of those years, Dave has worked in a variety of industries and government agencies. These include banking, clinical pharmaceutical research, intelligence, highways, and all aspects of oil production and processing. Projects entailed defining corporate information architecture, identifing requirements, and planning strategies for the implementation of new systems. Dave’s recently-published book, “Enterprise Model Patterns: Describing the World”, is an “upper ontology” consisting of a comprehensive model of any enterprise—from several levels of abstraction. It is the successor to his ground-breaking 1995 book, “Data Model Patterns: Conventions of Thought”–the original book describing standard data model configurations for standard business situations. In addition, he has written other books on metadata, requirements analysis, and UML. He has spoken at numerous international and local data architecture, semantics, user group, and other conferences.

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