IxD Theory 2: Telecomunicazioni

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IxD Theory 2: Telecomunicazioni

IUAV University of Venice

Visual and Multimedia Communication graduate programme

Data and databases 1

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Database: definition

Database = an organized collection of related information (data) stored on a computer system. Typical ‘everyday’ databases:

Your address book

A catalogue of your DVD collection Bus timetable

A doctor’s patient list University course list.

A database can be any organized collection of related data.

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Database: components and DMBSs

A database has two components:

1. The data (e.g. the names, phone numbers and email addresses of your friends)

2. Software which stores this data and allows you to input and retrieve it.

The software is a Database manager

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Database: DBMSs

DBMSs (Database managers) are of two basic types:

1. Specialist (e.g. address books, calendars, library catalogues, employee records, company payment systems)

2. General-purpose (e.g. for Mac, FileMaker Pro and FileMaker Bento; for Windows, Microsoft Access).

DBMSs organize the data as a collection of one or more ‘tables’.

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Tables

A table has two components:

1. ‘Records’ about an individual person or thing – normally imagined as a horizontal row

2. ‘Attributes’ of the individual person or thing – normally imagined as a vertical column.

Magdaléna Lazzarini 265837 2 clasVEM

Davide Bellato 263149 2 clasVEM

Morris Vianello 262024 1 clasT

Marina Albiero 265958 1 clasVEM

Enrico Buccella 263389 2 clasVEM

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Tables

From the DBMS’s point of view, the order of the records (rows) and attributes (columns) is not important:

Elvira Fiorin 261837 1 clasT

1 Vianello clasT 262024 Morris

2 Bellato clasVEM 263149 Davide

2 Lazzarini clasVEM 265837 Magdaléna

1 Fiorin clasVEM 261837 Elvira

1 Albiero 263389 265958 Marina

2 Buccella clasVEM 263389 Enrico

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Tables

Each attribute (column) must contain only one type of content:

e.g. in this table, column 1 must contain the individual’s forenames (‘Magdaléna’, ‘Davide’).

In some databases, each attribute (column) must contain only one type of format:

e.g. column 3 (matriculation number) must contain only 6-digit numbers (‘265837’).

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Tables

A table must not contain two identical records (rows):

e.g. in this table, the two Magdaléna Lazzarinis in year 2 of clasVEM cannot be distinguished:

If necessary, a ‘primary key’ attribute must be added (e.g. matriculation number, codice fiscale).

Magdaléna Lazzarini 2 clasVEM Magdaléna Lazzarini 2 clasVEM

Magdaléna Lazzarini 265837 2 clasVEM Magdaléna Lazzarini 263152 2 clasVEM

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Database input

A record is often input attribute by attribute. E.g. This questionnaire:

inputs this database record: Forename? Magdaléna

Surname? Lazzarini

Matric. no? 265837

Year? 2

clas? clasVEM

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Database output

A record is often output as a ‘record card’. E.g. This database record:

outputs this record card (= ‘profile’): Forename: Magdaléna

Surname: Lazzarini Matric. no: 265837

Year: 2

clas: clasVEM

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Database output

A database can also be output as tables, perhaps ordered according to some criterion.

E.g. The request:

Show all records, ordered alphabetically by surname outputs:

Marina Albiero 265958 1 clasVEM Davide Bellato 263149 2 clasVEM Enrico Buccella 263389 2 clasVEM Elvira Fiorin 261837 1 clasT Magdaléna Lazzarini 265837 2 clasVEM Morris Vianello 262024 1 clasT

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Database output

A database can be output as tables, filtered according to one or more attributes.

E.g. The request:

Show all records, ordered alphabetically by surname, where clas = ‘clasT’

outputs:

Elvira Fiorin 261837 1 clasT Morris Vianello 262024 1 clasT

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The information society

Increasingly we live in an ‘information society’ in which all kinds of information is gathered and stored in order to make society run more smoothly.

This information (data) is stored and organized in databases.

E.g: Much of the information on the Internet is stored in databases. When we request a page, information is taken from a database, formatted using HTML and CSS, and displayed in our browser. Some DBMSs can contain and organize sounds and images, not only words and numbers.

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Company databases

Some companies have astronomically large databases.

E.g:

Google stores all its Googlebot searches of the Web, and a record of every search made by users Amazon stores millions of book details, and a

record of all searches and purchases by users.

(Info. source: www.businessintelligencelowdown.com/2007/02/ _top_10_largest_.html)

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Government databases: British children

Government institutions also have vast

databases about citizens.

E.g. British children are checked in 5 different national databases:

1. National Pupil Database tags all children with 40 separate pieces of information

2. Connexions logs all teenagers

3. Every Child Matters logs everyone under 18

4. Contactpoint registers all children with details of all practitioners associated with them

5. Common Assessment Framework registers all

children needing any extra state service (50% of all children).

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Government databases: British children

“The National Pupil Database tags all children with 40 separate pieces of information (ethnicity, free school meals, behaviour, attendance etc). Data are held in perpetuity, with no consent sought.

“Connexions logs all teenagers. Personal Advisers may profile their teenagers with assessments of their friends, and capability of parents. It's for careers advice. Consent is required, for now, to upload the data.

“Every Child Matters also logs everyone until the age of 18. ‘At risk’ children are tagged. So are

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Government databases: British children

“Contactpoint registers all children with details of all practitioners associated with them. Its

implementation has been held up by the HMRC data disc loss.

“The Common Assessment Framework registers all children needing any extra state service (50 per cent of all children).

“In-depth profiling for all. It is amazing.”

[Quote from Simon Carr, The Independent, 03/03/2008: http://

www.independent.co.uk/opinion/commentators/simon-carr/simon-carr-if-you-want-snobbery-look-to-the-commons-790499.html]

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Databases and designers

The most common way of presenting information from databases – a table full of words and numbers – is not always the most digestible way to present the data.

Graphic and interaction designers are increasingly asked to find better ways of showing data from databases, on paper or on screen – i.e: to be information designers.

So designers must know how databases work. As designers, they may also have the skills to advise on database design.

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Database dangers

Data-mining uses algorithms to find patterns in data, such as credit card spending or loyalty-card information, to understand and serve consumers better.

But there are social and political dangers: Civil liberties activists fear that it is too easy to

access information about citizens held in databases, and that cross-referring (‘mashups’) between

databases can be used in ways which are against the interests of the citizen.

Activists also fear that it gives governments too much power. When governments are benign, this may not matter. But we cannot depend on this. Privacy laws are important to counteract this.

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Database dangers

There is also the problem of errors.

E.g: In the USA it is very difficult to live without a credit card. If your credit rating is wrong, because of some error in a database, you have great

difficulty living a normal life.

So privacy laws usually permit individuals to check the data that organizations hold on them.

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Databases as a ‘cultural form’

Some people think that the database is the 21st century’s dominant cultural form.

E.g. Lev Manovich believes that narrative forms (19th-century novels and 20th-century cinema) are being replaced by their enemy: database forms. To 21st-century people, he writes, the world seems an ‘endless unstructured collection of images, texts and other data records’. So our ‘poetics,

aesthetics and ethics’ are becoming those of the database, not those of the structured story.

Manovich sees Peter Greenaway (film director and exhibition curator) as a pioneer of database

aesthetics.

(Lev Manovich. The Language of New Media. Cambridge, MA: MIT Press. 2001. Chapter 5.)

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Types of database

Flat Relational Dimensional (multidimensional) Hierarchical Network Object

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Flat databases

ClasVEM

Corso di laurea specialistica in comunicazioni visive e multimediali Assetto dei corsi

assettto fornitoda periodo lab/corsocategoriecrediti/totale nec.

clasVEM av I c a3 4/4 ICAR/18 4 Tendenze dell’architettura contemporanea (clasAV) Roberto Masiero I clasVEM av I c a3 4/4 L-ART/02 4 Storia dell’arte moderna (clasAV) Laura Corti I

clasVEM dip I c a3 4/4 M-STO/05 4 Storia dell’innovazione scientifica e tecnologica (clasDIP) Raimonda Riccini I clasVEM VEM I c b1 4/8 ICAR/13 4 Design dell’interazione Philip Tabor I

clasVEM VEM I c b1 4/8 ICAR/13 4 Storia della grafica Carlo Vinti I

clasVEM VEM I c b1 4/8 ICAR/13 4 Teoria della comunicazione Giovanni Anceschi I clasVEM av I c b1 4/8 ICAR/16 4 Museografia e allestimento (clasAV) Giulio Alessandri I

clasVEM av I c b1 4/8 ICAR/16 4 Teorie e tecniche dell’allestimento (clasAV) Marco Della Torre I clasVEM t I c b1 4/8 ICAR/16 4 Teoria e storia della scenografia (clasT) Giorgio Ricchelli I

clasVEM VEM I c b2 4/4 ING-INF/05 4 Sistemi di elaborazione dell’immagine (obbl.) Massimiliano Ciammaichella I clasVEM av I c b4 4/4 M-PSI/05 4 Psicologia cognitiva (clasAV) Vittorio Girotto I

clasVEM dip I c c2 4/8 M-FIL/02 4 Logica e filosofia della scienza (clasDIP) Paolo Garbolino I clasVEM dip I c c2 4/8 M-FIL/05 4 Semiotica degli artefatti (clasDIP) Patrizia Magli I

clasVEM dip I c c2 4/8 M-PSI/01 4 Teorie della creatività e dell’innovazione (clasDIP) Paolo Legrenzi I clasVEM VEM I L b1 8/48 ICAR/13 8 Lab di design dei tipi Leonardo Sonnoli I

clasVEM VEM I L b1 8/48 ICAR/13 8 Lab di comunicazione visuale e cinetica Philip Tabor I clasVEM VEM I L b1 8/48 L-ART/06 8 Lab di teoria delle comunicazioni Wolfgang Scheppe I clasVEM av I L b1 8/48 L-ART/06 8 Lab di arti visive 1 (clasAV) Remo Salvadori I

clasVEM av I L b1 8/48 ICAR/16 8 Lab di allestimento 1 (clasAV) Cornelia Lauf I

clasVEM dip II c a1 4/4 ING-IND/11 4 Illuminotecnica e acustica (obbl.) Claudio Coloretti II clasVEM VEM II c a2 4/4 INF/01 4 Applicazioni di computer grafica (obbl.) Davide Riboli II clasVEM av II c a3 4/4 L-FIL-LET/11 4 Letteratura italiana (clasAV) Andrea Cavalletti II clasVEM t II c a3 4/4 L-ART/07 4 Storia della musica (clasT) Adriano Castaldini II clasVEM VEM II c b1 4/8 L-ART/06 4 Storia del cinema Irene Bignardi II

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Flat database: a simple example

Flat databases have no relations between tables.

This database does not show directly the marks

given on each course. Or which students are taught by which professor.

The user must make a new database and copy in, by hand, the relevant data.

NAME EXAM 1 NAME EXAM 1 CORSI PROF

Storace 30 Bruno Etica Grafica Camuffo

Rosso 19 Minelli Cinema Prodotto Cibic

Prodi 23 Bianco Etica Economia Myers

Minelli 27 Rosso Tipi Storia Iove

Bruno 22 Storace Teatro Cinema Costa

Bianco 29 Prodi Tipi Teatro Michelis

. . . . . . Etica Plato

Tipi Sonnoli

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‘Relational’ database: a simple example

The same tables, but here they are related:

. . . . . . Etica Plato

Tipi Sonnoli

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‘Relational’ database: a simple example

Find the mark for courses this periodo didattico.

. . . . . . Etica Plato

Tipi Sonnoli

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‘Relational’ database: a simple example

Find the mark for courses this periodo didattico

. . . . . . Etica Plato

Tipi Sonnoli

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‘Relational’ database: a simple example

. . . . . . Etica Plato

Tipi Sonnoli

Arte Vetttese

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‘Relational’ database: a simple example

. . . . . . Etica Plato

Tipi Sonnoli

Arte Vetttese +

= NAME EXAM 1 EXAM 1

Bruno Etica 22 Minelli Cinema 27 Bianco Etica 29 Rosso Tipi 19 Storace Teatro 30 Prodi Tipi 23 . . .

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‘Relational’ database: a simple example

Which professors teach which students?

. . . . . . Etica Plato

Tipi Sonnoli

Arte Vetttese

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‘Relational’ database: a simple example

Which professors teach which students?

. . . . . . Etica Plato

Tipi Sonnoli

Arte Vetttese +

= PROF EXAM 1 NAME Costa Cinema Minelli

Michelis Teatro Storace

Plato Etica Bruno

Plato Etica Bianco

Sonnoli Tipi Rosso

Sonnoli Tipi Prodi . . .

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‘Relational’ database: a simple example

Which professors have successful students?

. . . . . . Etica Plato Tipi Sonnoli Arte Vetttese + NAME PROF Bruno Plato Minelli Costa Bianco Plato Rosso Sonnoli Storace Michelis Prodi Sonnoli . . .

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‘Relational’ database: a simple example

Which professors have successful students?

. . . . . . Etica Plato Tipi Sonnoli Arte Vetttese + NAME PROF Bruno Plato Minelli Costa Bianco Plato Rosso Sonnoli Storace Michelis Prodi Sonnoli . . .

= NAME PROF EXAM 1

Minelli Costa 27 Storace Michelis 30 Bruno Plato 22 Bianco Plato 29 Rosso Sonnoli 19 Prodi Sonnoli 23 . . .

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‘Relational’ database: a simple example

NAME EXAM 1 EXAM 1

Bruno 22 Etica Minelli 27 Cinema Bianco 29 Etica Rosso 19 Tipi Storace 30 Teatro Prodi 23 Tipi . . .

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Two-dimensional databases

NAME EXAM 1 EXAM 1

Bruno 22 Plato Minelli 27 Costa Bianco 29 Plato Rosso 19 Sonnoli Storace 30 Michelis Prodi 23 Sonnoli . . . Bruno Minelli Bianco Rosso Plato Costa Sonnoli Michelis

22

27 27

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Three-dimensional databases

We can assign different axes to different tables and calculate average marks along a slice of the volume.