IxD Theory 2: Telecomunicazioni

Transcription

1 IxD Theory 2: IUAV University of Venice Visual and Multimedia Communication graduate programme Data and databases 1 Gillian Crampton Smith + Philip Tabor 2008

2 What is a database?

3 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.

4 Database: components and DMBSs A database has two components: 1. The data (e.g. the names, phone numbers and addresses of your friends) 2. Software which stores this data and allows you to input and retrieve it. The software is a Database manager = DBMS (Database Management System).

5 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.

6 Database tables: a simple example

7 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 clasvem Davide Bellato clasvem Morris Vianello clast Marina Albiero clasvem Enrico Buccella clasvem Elvira Fiorin clast

8 Tables From the DBMS s point of view, the order of the records (rows) and attributes (columns) is not important: Magdaléna Lazzarini clasvem Davide Bellato clasvem Morris Vianello clast Marina Albiero clasvem Enrico Buccella clasvem Elvira Fiorin clast = 1 Vianello clast Morris 2 Bellato clasvem Davide 2 Lazzarini clasvem Magdaléna 1 Fiorin clasvem Elvira 1 Albiero Marina 2 Buccella clasvem Enrico

9 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 ). Magdaléna Lazzarini clasvem Davide Bellato clasvem Morris Vianello clast Marina Albiero clasvem Enrico Buccella clasvem Elvira Fiorin clast 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 ( ).

10 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: Magdaléna Lazzarini 2 clasvem Magdaléna Lazzarini 2 clasvem If necessary, a primary key attribute must be added (e.g. matriculation number, codice fiscale). Magdaléna Lazzarini clasvem Magdaléna Lazzarini clasvem

11 Database input A record is often input attribute by attribute. E.g. This questionnaire: Forename? Magdaléna Surname? Lazzarini Matric. no? Year? 2 clas? clasvem inputs this database record: Magdaléna Lazzarini clasvem

12 Database output A record is often output as a record card. E.g. This database record: Magdaléna Lazzarini clasvem outputs this record card (= profile ): Forename: Magdaléna Surname: Lazzarini Matric. no: Year: 2 clas: clasvem

13 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 clasvem Davide Bellato clasvem Enrico Buccella clasvem Elvira Fiorin clast Magdaléna Lazzarini clasvem Morris Vianello clast

14 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 clast Morris Vianello clast

15 Why are databases important?

16 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.

17 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: _top_10_largest_.html)

18 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 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).

19 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 children at risk of poor outcomes.

20 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:

21 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.

22 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.

23 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.

24 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 Chapter 5.)

25 Types of database

26 Types of database Flat Relational Dimensional (multidimensional) Hierarchical Network Object

27 Flat databases ClasVEM Corso di laurea specialistica in comunicazioni visive e multimediali Assetto dei corsi assettto fornito da periodo lab/corso categorie crediti/ 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 clasvem av II c b1 4/8 L-ART/03 4 Storia delle arti applicate (clasav) Francesco Amendolagine II

28 Flat database: a simple example Flat databases have no relations between tables. 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 Arte Vetttese 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.

29 Relational database: a simple example The same tables, but here they are related: 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 Arte Vetttese

30 Relational database: a simple example Find the mark for courses this periodo didattico. 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 Arte Vetttese

31 Relational database: a simple example Find the mark for courses this periodo didattico 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 Arte Vetttese

32 Relational database: a simple example Find the mark for courses this periodo didattico 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 Arte Vetttese

33 Relational database: a simple example Find the mark for courses this periodo didattico 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 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...

34 Relational database: a simple example Which professors teach which students? 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 Arte Vetttese

35 Relational database: a simple example Which professors teach which students? 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 Arte Vetttese = PROF EXAM 1 NAME Costa Cinema Minelli Michelis Teatro Storace Plato Etica Bruno Plato Etica Bianco Sonnoli Tipi Rosso Sonnoli Tipi Prodi...

36 Relational database: a simple example Which professors have successful students? 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 Arte Vetttese + NAME PROF Bruno Plato Minelli Costa Bianco Plato Rosso Sonnoli Storace Michelis Prodi Sonnoli...

37 Relational database: a simple example Which professors have successful students? 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 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...

38 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...

39 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

40 Three-dimensional databases We can assign different axes to different tables and calculate average marks along a slice of the volume. Bruno Minelli Bianco PeriodoI Periodo III PeriodoII Prof Sonnoli Prof Costa Prof Plato Average mark/prof Which professors are giving high marks?

41 Three-dimensional databases Or look for a different kind of information Bruno Minelli Bianco Periodo I Periodo II Periodo III Prof Costa Prof Sonnoli Prof Plato Average mark/students/periodo Are students making progress with Professor Plato?