Data -> Database -> Real World
• The languages of new media
• Data -> database -> real world ?
• Database already a piece of the real world
• In the real world, our interpretation or engagement with data?
• Free range data – dynamic linkages to crowd sourced material
• What is the difference between data and a database?
• Maonovich database vs narrative, Jameson – orienting in disorienting world, Morton – importance to perceive global patterns
• Database as perspective
• Reciprocal influence (database is in the world, produces knowledge that changes the work, which changes the nature of the database)
• The stretch of communication/information as interpreted by human beings
• Means of production
• Access to data?
• Isnot a database an individual or working parties interpretation of data?
• Data is dumb
• The Real World
• We access bits of it through our conceptual models
• Is pushed in front of you (why or how is it pushed?)
• Perception – business time in London is casual time in USA
• The Database
• Is it still a number anymore? A picture of pixels (Manovich)
• Does include a motion from quantitative to qualitative – adding other types of information (narratives, news, flickr…) ex archival material, radio, TV?
• We need databases to approximate and make sense
• The Data
• Is still a collection of samples e.g. Flickr mostly takes photos of popular places. Or an alternative view: FLickr is not capturing places, predominantly about experience rather than locations..
• Chronology: databases exist in time
• Air quality, spoil, census samples are all take at different times
• Invisible aspect of the quality of the relationships between the different data types within a database
• So, from data -> database-> Real at 3 specific times
• But real world is also an individual understanding of experience in relation to database (conceptual models)
• Data – database – realworld
• Cyclic process with the database having material affects
• Question of difference of data covered here as elsewhere
• Discussion of database as alternative to Narrative (Mannovich)
• Database as a vantage from which to act
• Questions – sampling bias in inclusion of data in DB
• Is data still only digital, eg. Photos (again Manovich), rich data/qual data (photos interviews etc) can broaden the uses and conceptual models related to a DB
• Chronology of DB – time is always an aspect of data
• Integrity -> meta information -> comprehensive?
• Social context
• Flickr photos
• Reusability of vast amounts of data – processing efficiency, usability, finding the data
• Data and data separations are a language
• Gluttony of data – a lot of data very varied (people, flickr, facebook, profiles) linkage with other types of information
• How do we assess accuracy? Context, objective data vs subjective data
• What is data? distance to it
• ‘my photos have meaning to me, they are not data!’
• data is what operations are performed on, dalgorithic understanding predictive or representing ‘world’
• data collection ~ data gathering/mining
• perception of accuracy – accuracy of the final database (database development)
• is it data? based in distinction
• does data always exist or do we create it?
• Qant data as something that can be counted/measured
• Qual data – cannot be directly compared
• Data – dumb/passive
• Information – interpretation/translation
• Epistemological questions on data as world view, the meaning of operational knowledge/ the role of positivism
• The value of humour in the interpretation of data – is it useful?
• Does an organic notion dictate that nothing can ever be accurately dated? Data(ed)
• What the opposite of data? (data = illusion of accuracy)
• What is the relationship between data and information?
• Is there data in negotiation?
• Collecting data vs collecting image?
• Forms of data – narratives, pictures, statistics?
• Can data transform the world?
• Information sovereignty – who owns data?
• Another issue/consideration: data accuracy vs data precision
• Check out the ‘D474” googel group
• Ticking/manipulating – user to accept map as being true representation
• Propaganda/bias in mapping
• Over simplified – powerpoint effect
• 1932 i-pad (LUG Map/Beck), AG/the New
• are they used? Are they understood?
• New technology = exclusion – digital divide
• Data representation is where ‘art’ comes to play the most dominant role
• Materiality is important assuming that the only place things will be in online
• Material you choose to use has it own identity
• What is a ‘good one’
• Describe data
• Has social investment
• Productive – decisions made on ‘simplifications’ of the messag
• Leave out the unwanted (who decides?) – linked to choice of appropriate metaphor
• Choice of metaphor acknowledges attributes of metaphor, cultural meanings are attached to choice
• Bias in maps
• European view of the world, assumed objectivity
• Bias of tools
• Maps look better than they are, built in set of assumptions
• People have bot been taught to question maps, users are not critical, maps can only show ‘selection’ of reality, automatic bias
• Which metaphor is most appropriate to purpose and available data
• Producing maps for self -> users forced to learn the ‘rules’ of cartography
• How do you represent data with affect
• High end graphics? Filtered out
• To lend credibility degrading images, how do you measure credibility?
• It is through effective communication of information or accurate recording of information
• CAD diagram may be accurate but user mat not be able to relate to it (preferring a sketch which they understood)
• Frank Gehry
• Visualisation – senses
• Disconnection between user, software designer and cartographer
• Sese of the pther – framing is about putting a comfortable distance from the viewer. Framing in arts and cartography is somewhere similar: either you are in or out?
• Can bring what is out – in?
• Oral exchange were probably the first database (sourced of data) than drawing the text, than photographs, then computure, the we don’t really need previous one!
• Is there a removal from the person designing the software and the user?
• Here are the senses? Where is the haud?
• With the computure the hand and the body, become useless (except for the brain)
Conceptual Models behind Database Use
• Scale/effect -/pattern
• At what level of abstract should the conceptual model be?
• Data and the force of the law/administration
• Data is always abstract
• Data is the act of observing something (record)
• Anything can be data – model come in to approach of a particular thing
• Information extracted from the thing ~ extraction process
• Conceptual model – relational
• Object orientated – levels of abstraction important
• Key behind any conceptual model is importance of metadata
• The higher you are from the detail – the further you are from reality
• Inter-subjective nature of the data and the DB
• Filtering of views & access – power, penatratability
• Considerations in designging the DB – people now access data – who are the people?
• What is the intention behind the database?
• More that people have access to data, than what it was designed for.
• Digital environments might be subject to hacking or unexpected interaction
• People can change systems 0 but its not possible for people to access the background
• When you are at the pattern level you can’t see the detail (the difference between the near and the far view) the less you see the detail, the more you see
Data doesn’t care
Database and it conceptual model is where certain values become evident – the database cares
Relativity – database patterns, details
Thinking about the impact of the database
Data -> DB -> Real World
Material effects of the DB – info has material affects in the real world – leads to actions
DB as an alternate narrative
Sampling bias – inclusion/exclusion
Qual data opens up new possibilities
Time/temporality – how can we compare such things?
Gluttony/ the amount of data available
Degrees of quality and character
Non-scientific data but still data
Accuracy/ relativity – maluable, biased and contextual
Not always evident social contexts –
Good = usable – rich info, rich ability to be linked to other kinds of data
Methodology for grouping is important – data is context specific
Data is dumb – data relies on correlation to make meaning and have sense.
Observations – social observations
Maetaphor – credibility – bias (collector/viewer), perspective
People’s beliefs in maps – selected realities
Problem of over simplification and impacts on integrity
Possibility of not conveying life effectively – materiality, material language, subsequent potential influence on the connection of an idea
How is credibility arrived at?
Disconnection between the people involved in the production of the outcome
Framing – controlled views, comfortable views
Technical limitations and relevance to the end users perception