Panel presentation at ECDL 2009

  • 571 views
Uploaded on

 

More in: Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
571
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • barriers to direct collaboration - time, geography, limited technology
    people as entry points
  • ‘liking’ items in FriendFeed being used successfully by chemists and other scientists to rapidly explore topics of interest
    more subtle ‘gestures’ - e.g. the ‘re-tweet’ in Twitter which points as much to the originator as it does to the resource or assertion they might have made
  • the AOL search data scandal in 2006 was widely reported as a breach of privacy, which it clearly was. But it also hinted at the extent to which users are increasingly present in systems. human richness of presence within these systems - not just data!!
    User 927 was identified by some as some disturbing ‘attention’ patterns were revealed - but what was very interesting about this user record is that is appears to represent an account shared by three very different people. Works with Amazon because there is a credit card involved - in academia need incentive for not sharing
  • is this a bug or a feature? people rather than representations of patterns of behaviour
  • is it better to really satisfy niche networks with tailored services, rather than a generalised offering which doesn’t really satisfy anyone equally?
    Barrier to creating bespoke apps has been reduced

Transcript

  • 1. (representations of patterns of aggregated usage) OR (people) Paul Walk Technical Manager p.walk@ukoln.ac.uk UKOLN is supported by: www.ukoln.ac.uk A centre of expertise in digital information management 1
  • 2. people in systems • (representations of patterns of aggregated usage) OR (people) • the development of data-centric approaches and services which provide scholars with resources targeted to their individual, personalised needs • pervasive networking technologies which have reduced some barriers to collaboration 2
  • 3. resource discovery via other people • ‘gestures’ indicating attention - enlightened self-interest • direct human ‘presence’ rather than anonymous or algorithmic actor • is this what we mean by ‘digital society’? • can a well developed & highly available social network reduce ‘filter-failure’? 3
  • 4. however.... • the scholar is not always in a ‘social’ mood - they might be: • collaborative • competitive • neutral • much of recent technology-enhanced collaborative enterprise depends on enlightened self-interest.... • ...this might not always apply • will people demand more control over their own attention data? • what do you know about me? • can I reuse this data myself elsewhere? • can I remove it from your system? 4
  • 5. lessons from AOL • anonymised user 4417749.... • ...or as her friends know her, Thelma Arnold • user 927 5
  • 6. anonymity? • David said ‘… and the more we track, the better we can adapt our service without your intervention.’ • but: • perhaps I welcome the chance to intervene • perhaps I want other people, known to me, to be able to intervene on my behalf 6
  • 7. niche/specialist networks • recommendation systems being tried with some apparent benefits being realised at undergraduate level • but, in academia, beyond undergraduate it’s long tail all the way! • small networks based on people actually knowing each other • do academics work this way? • economic/business drivers underpinning service design may be changing 7
  • 8. questions for service providers • what can those who provide digital library services offer the “long-tail” of academia? • in which context(s) might the personal/social network offer a good approach to resource discovery? • when might the service built on aggregated, anonymised attention data be appropriate? • can these approached be integrated by service providers, or is this task best left to the user or another agent? 8