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Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

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Page 1: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

Information structures and implications 2015

Prof. Bettina BerendtThanh Le Van

22 September 2015

Page 2: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

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Most important URL for this course

http://people.cs.kuleuven.be/~bettina.berendt/teaching/2015-16-1stsemester/isi/

Page 3: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

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TodayWho are we?

Course overview and practical things

What are data? What is a database? What does this mean for you?

Some relevant concepts

Introducing our first case study

Page 4: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

Course overviewand practical things

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Lectures

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Exercise sessions

Start in week 3

Are an integral part of the course

Hands-on experience with data(bases)

Detailed plan will follow

Direct preparation for the assignments

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Assignments and evaluation

Proceed from “exercise-style” to “mini-project”

Written and oral (presentation of your results)

Are the main basis of evaluation

Plus participation in class

No exam

No second examination chance in September!

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Materials

Plus extras:

online

or

distributed otherwise

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Ask questions!

In the lecture

In the exercise session

In the Toledo forum

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Page 11: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

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TodayWho are we?

Course overview and practical things

What are data? What is a database? What does this mean for you?

Some relevant concepts

Introducing our first case study

Page 12: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

... and now for the interactive part

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(Just a reminder of what we did)

1. Introduce your neighbour in terms of data

2. “How else can you describe a person?“ – datafying ourselves

3. Do you use databases? Which ones? How?

4. Other people have similar experiences ... “Homebrew Databases“ – Amy Voida, Ellie Harmon, and Ban Al-Ani. 2011. Homebrew databases:

complexities of everyday information management in nonprofit organizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, New York, NY, USA, 915-924.

– Paper: http://ellieharmon.com/docs/VoidaHarmonAlAni-Homebrew-CHI2011.pdf – Talk: http://people.csail.mit.edu/karger/Talks/HomebrewDatabases.pdf

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[…] it is on account of the grown-ups and their ways. When you tell them that you have made a new friend, they never ask you any questions about essential matters. They never say to you, "What does his voice sound like? What games does he love best? Does he collect butterflies?" Instead, they demand: "How old is he? How many brothers has he? How much does he weigh? How much money does his father make?" Only from these figures do they think they have learned anything about him.

If you were to say to the grown-ups: "I saw a beautiful house made of rosy brick, with geraniums in the windows and doves on the roof," they would not be able to get any idea of that house at all. You would have to say to them: "I saw a house that cost $20,000." Then they would exclaim: "Oh, what a pretty house that is!"

Page 15: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

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TodayWho are we?

Course overview and practical things

What are data? What is a database? What does this mean for you?

Some relevant concepts

Introducing our first case study

Page 16: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

Some relevant concepts

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Databases vs. files

Files– Each program defines structure– Several programs: redundancies, or need for

transformation

Databases– Data + metadata* that describe the structure– Independent of programs

* At least in traditional databases

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An example:What should the KU Leuven student database contain?

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Tables, also known as relations,

referring to one another

Relational databases

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An example of metadata of

such a database

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Goals?

– Structure– Make accessible– manipulate– Hold consistent– Store “securely”

data

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Database

A set of centrally managed, permanently stored data, which always have to be available to different applications

• All data that are relevant for all previewed applications

• Every application sees/access only relevant data

• Different applications share the same data

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Some definitions

Database system– Database

(the data themselves)– Database management system (DBMS)

DBMS– Create database structure (“schema”)– Add, delete, modify data– Access / query data

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A foretaste … ER

Page 26: Information structures and implications 2015 Prof. Bettina Berendt Thanh Le Van 22 September 2015

EMPLOYEE

Sex

Name

Ssn

Fname Minit Lname

Address

Bdate

Salary

DEPARTMENT

Number

LocationsName

PROJECT

Name

Number

LocationDEPENDENT

Name

Sex BirthDateRelationship

WORKS_FOR

CONTROLS

MANAGES

StartDate

SUPERVISESHAS_DEP.

1N

11

1

N

supervisor supervisee1

N

1 NWORKS_ON

Hours

NM

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A foretaste … SQL

SELECT DISTINCT PnumberFROM PROJECTWHERE Pnumber IN

(SELECT Pnumber FROM PROJECT, DEPARTMENT, EMPLOYEE

WHERE Dnum=Dnumber AND …) OR Pnumber IN

(SELECT Pnumber FROM …

WHERE …) ;

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TodayWho are we?

Course overview and practical things

What are data? What is a database? What does this mean for you?

Some relevant concepts

Introducing our first case study

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Exercise for next session

1. Read the European Parliament texts that are hyperlinked on the course page

2. Think of a (small) research question you could have from the perspective of your discipline – or another one

3. Model the data that you would need to answer it with “boxes, bubbles and diamonds”

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