AV Related Interactive Media Web 2.0

Time based video annotation online

Mojiti and BubblePly offer time based video annotation for content posted on the main online video sharing sites. With these you can add your own subtitles or speech bubbles or other commentary to videos while they play for sharing and commenting by other viewers (who have to view the annotated video’ through either BubblePly or Mojiti).

The source file on YouTube etc stays untouched and what these two services are doing is hosting the annotations and then overlaying them as the source video plays embedded in their site.

This is a nifty idea and something that Mike Jones first suggested would be cool – in relation to some art and academic projects – about a year ago around our lunch table. Well, now it is here.

I’d assume that for most this will be a gimmick (see the sample movies on BubblePly) or useful for niche audiences (see the subtitling samples on Mojiti) but there are some really interesting possibilities for artists and others to play around with this technology too.

I remember a presentation by some academic researchers at University of Queensland who were experimenting with SMIL to build automated narrative generators and video search tools. I am not 100% sure of the project but it could have been related to the work of the Harmony Project.

Collection databases Web 2.0 Web metrics

Lorcan Dempsey on ‘intentional data’

Lorcan Dempsey opens the new year with a great post with lots of outward linkages on the under-utilisation of intentional data by libraries.

In general, consumer sites on the web make major use of such data, and it is especially valuable when they can connect it to individial identities. They use it to build up user profiles, to do rating and comparisons across sites, to recommend, and so on. Of course this is increasingly important in an environment of abundant choice and scarce attention: they are investing more effort in ‘consumption management’. We are all familiar with the benefits, and the irritations, of organizations who want to build a deeper understanding of what we do and make us offers based on that.

Libraries have a lot of data about users and usage. And there are now some initiatives which are looking at sharing it. However, in general, libraries do not have a data-driven understanding of individual users’ behaviors, or of systemwide performance of particular information resources. This is likely to change in coming years given the value of such data. So, we are seeing the growth in interest in sharing database usage data. And technical agreements and business incentives for third party providers will support this development. And, of course, libraries want to preserve the privacy of learning and research choices.

Whilst libraries are in a fundamentally better position to know more about the intentions of their users, museums tend to restrict their interest to the very visitation/donation-oriented CRM model of intention tracking.

As Dempsey points out, such data actually has much broader implications for organisations, and he summarises Chunku Mui’s proposed taxonomy of ‘Emergent Knowledge’ – knowledge that is gained about users by analysing behaviour gathered from log data and user pattern analysis.

At the Powerhouse Museum we have only very recently, with our OPAC2.0 project, started to move beyond simple log file analysis for intentional data from our website users, and now into beginning to examine the emergent trends in collection popularity. I hope that by the time Museums & The Web 2007 comes around in April, we will have the first of our open APIs to connect and use data patterns from our Synonymiser Beta.

This will allow any museum with a similar collection (or subset) to mine our anonymous behaviour data to generate recommendation data for their own collections.