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Abstract Data Models

Instructor: David Gloag
How do we simplify the vast amount of data at disposal? The answer to that is abstract data models. In this lesson, we'll take a look at data models and abstraction, and their characteristics and uses.

The Need for Simplification

Our daily lives are influenced significantly by collected data. Driver insurance rates are determined by data collected on accident and traffic ticket instances, Government policies are directed by census data, and political decisions are determined by collected voting data. With approximately 350 million people in Canada and the US, there is a lot of data to sift through and process. But even more than that, we need to derived meaning from it all. Clearly, we need a way to simplify and organize this data. That is where abstract data models come in.

What is a Data Model?

Before we talk about abstract data models, let's define a data model itself. A data model is a particular representation of some data. It specifies a subject for the representation, attributes, and groups everything together based on the relationship of the attributes to the subject.

Let's take a look at an example. Suppose that people is our subject. Furthermore, country of residence, continent, and region, are the attributes we are interested in. In this case, the data model would be people and their geographic location.

What is Abstraction?

Abstraction is a process of simplification. It removes details, and replaces them with concepts. For example, using geographic location instead of the attributes; country of residence, continent, and region. The idea is to make describing them, and talking about them, easier. We use this a lot in our daily lives. Anytime we talk about 'Pam's house' or 'Joe's place' during a conversation, we are using abstraction. We know that 'Pam's house' is located at '123 Anywhere Place, Some State, USA', but we don't say that. We use 'Pam's house' to simplify the idea.

What is an Abstract Data Model?

In most respects, a data model, and an abstract data model are similar. They both talk about the representation of some data, and both are meant to simplify. But they differ in the amount of detail provided and scope. If we think back to the example of people and their attributes (country of residence, continent, and region), we see a certain level of detail. An abstract data model reduces that even further and talks about people and their geographic location. The specifics of the location are not important, and are discussed only in general terms. Scope increases in abstract data models because they often have more than one relationship, like adding age group to the model.

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