data modelling and er diagrams a way to make sense out of large collections of information

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Data Modelling and ER Diagrams A way to make sense out of large collections of information

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Page 1: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Data Modelling and ER DiagramsA way to make sense out of large collections

of information

Page 2: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Data Modelling and ER Diagrams

Learning Intentions• Understand what an entity, an

attribute and a relationship are• Understand how these are related to

instances and values• Understand the different types of

relationships between different entities• Understand the purpose of an ER

diagram• Appreciate that ER Diagrams are one

way of creating an information model

Success Criteria• Given some example attributes be able

to group them with particular entities• Given a set of values group them into

specific instances of an entity.• Given a set of entities describe the

relationship between them.• Define what the terms entity, attribute

and relationship mean.• Create a simple ER Diagram given a set

of structured data.

Page 3: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Activity 1:- What kind of information can we find on social networking sites?

Page 4: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Posts

Pictures

Picture Albums

News

Events

Basic Profile Details

People I’m Friends with

Groups I belong to

Page 5: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Facebook

There is a lot of data on Facebook. ■ 1.3 billion users■ 80 billion items shared every month

(pictures, posts, notes, news, events, etc), ■ Average user creates 100 items per month

“How can FB give me back my data out of the several trillion items out there?!

“How can the data be organised so that it finds it quickly?”

Page 6: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Activity 2:- Grouping data with things1. Make a group of four and make sure you have some space to work

together2. Place the four cards in grey in a horizontal line with space

underneath for other cards

3. Decide which pieces of data are most likely to be seen with a particular thing on a grey card and place them underneath

4. You should make sure your group can explain why they’ve placed that piece of data with a particular thing.

Person Event Post Picture

Person Event Post Picture

Page 7: Data Modelling and ER Diagrams A way to make sense out of large collections of information

How did your group arrange their cards?

Page 8: Data Modelling and ER Diagrams A way to make sense out of large collections of information

One Possible Grouping

Person Event Post Picture

Name

Date of Birth

Hometown

Email

School

Text

Time

Location

URL

Name

Start Date

Host

End Date

Location

Description

Location

File Name

Page 9: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Some Definitions

•The grey cards are called entities which are classes of object we want to model and store information about. •The white cards are called attributes which are characteristics that an entity has.•The collection of all entities and their attributes is the beginning of a data model that describes the situation we are interested in modelling.

Page 10: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Activity 3:- Creating example instances1. Make a group of four and make sure you have some space to work

together2. Place the four cards in grey in a horizontal line with space

underneath for other cards

3. Group specific values on the cards together to make specific examples of a particular entity. There may be more than one example for each entity.

Person Event Post Picture

Person Event Post Picture

Page 11: Data Modelling and ER Diagrams A way to make sense out of large collections of information

•Alison Smith•Born 30/08/1995•Born in Glasgow•Goes to Hamilton Grammar School•[email protected]

•“Had a fantastic day at the zoo with Steven”•Posted 14:32 on 12th September 2013•http://www.thezoo.com

•Alison’s birthday party•Born 30/08/1995•Starts 30th Aug, 7pm•Ends 30th Aug, 11pm•Hosted by Alison•At the Community Hall

•“The view from Edinburgh Castle”•Taken at Edinburgh Castle•Edincastle.jpg

•Steven Brownlee•Born 23/09/1982•Born in London•Goes to London High School•[email protected]

•“It’s transfer deadline day”•Posted 12:01 on 11th January 2014•http://www.bbc.co.uk/transfernews

•Holiday to Tenerife•Starts 1st June 2015•Ends 15th June 2015•Hosted by Steven•At Playa de Las Americas

•“Me and my snowman”•Taken at Steven’s house•snow003.png

Person Event Post

Picture

Page 12: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Discuss

•What was easy or hard about this activity?•What is the difference between an entities and attributes and instances and values? •What is missing from this model? What did we talk about when we discussed social networks at the start that is not represented in the information we have?

Page 13: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Some More Definitions

•An instance is a specific concrete example of an entity. For example Fred is an instance of the person entity.•A value is a specific concrete example of a particular attribute. For example 24/05/1982 is the value for Fred's birthday attribute.

Page 14: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Activity 4:- Relationships

•Alison Smith•Born 30/08/1995•Born in Glasgow•Goes to Hamilton Grammar School•[email protected]

•Alison’s birthday party•Born 30/08/1995•Starts 30th Aug, 7pm•Ends 30th Aug, 11pm•Hosted by Alison•At the Community Hall

•“Had a fantastic day at the zoo with Steven”•Posted 14:32 on 12th September 2013•http://www.thezoo.com

•“Me and my snowman”•Taken at Steven’s house•snow003.png

How are these instances related to each other?

Page 15: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Activity 4:- Relationships

How are these instances related to each other?

•“The view from Edinburgh Castle”•Taken at Edinburgh Castle•Edincastle.jpg

•Steven Brownlee•Born 23/09/1982•Born in London•Goes to London High School•[email protected]

•“It’s transfer deadline day”•Posted 12:01 on 11th January 2014•http://www.bbc.co.uk/transfernews

•Starts 1st June 2015•Holiday to Tenerife•Ends 15th June 2015•Hosted by Steven•At Playa de Las Americas

Page 16: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Person

PicturePost

Event

Draw the relationships between these events

Page 17: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Person

PicturePost

Eventhosts, attends, likes

uploads, likes

creates,likes,

contains

Entity Relationship Diagram

Page 18: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Summary

• Entities are classes of object we want to model and store information about. •Attributes are characteristics that an entity has.• Relationships are how two entities are related to

each other.•Data model is the collection of all entities, their

attributes and the relationships between them.• Instances are specific examples of a particular entity.•Values are the specific data for a particular attribute.• ER Diagram is a diagram showing entities and the

relationships between them.

Page 19: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Discuss

What other entities and relationships exist in social networking sites?

Is a particular relationship between instances one to one, one to many or many to many?

Page 20: Data Modelling and ER Diagrams A way to make sense out of large collections of information

Person

PicturePost

Eventhosts, attends, likes

uploads, likes

creates,likes,

contains

Group

is in a ..

extensions

Page

likes a ..