Data DJs

Last week on Wednesday, I gave a seminar at LARGE (Learning Agents Research Group at Erasmus). Thanks to Wolfgang Ketter for both inviting me and more importantly the excellent discussion. The nice thing about seminars is that they often give you an opportunity to do something different. In this case, I did an expanded version of the October WAI talk I gave introducing myself to my new colleagues in the VU’s AI department. When I moved to the VU, I started trying to think about a better organization or perspective on my research. Something that encompassed what I’ve already done but also something that pointed towards the future. The thing that kept coming to my mind is that what we really need is tools that make remixing data as easy as it is to remix music or video. Essentially, why don’t we have Data DJs? (or should it just be DJ – Data Jockey??)

Indeed, we can see that a lot of what scientists (i.e. data analysis pros) do is remix data. In a happy coincidence, this idea was reinforced to me last night when I watched the documentary RiP: A Remix Manifesto. It’s both an entertaining and important documentary about the impact of strong intellectual property laws on creative freedom. Most importantly for this post, it describes the analogy between a music DJ and the practice of science in vivid terms. See the clip below starting at about 4 minutes in.

The other thing about the documentary to me was it showed how accessible the tools for working with music and video were to people. I think we can make tools that are just as good or better for data sitting in files, spreadsheets and databases.  I think this DJ to data-analysis analogy is a powerful framework to think about how we can make such tools. I summarize the analogy as follows:

• records = data in one format (linked data?)
• turntable and mixers = end-user programming (workflows)
• recording equipment = capturing what goes on during data analysis (provenance)

The slides at the end of this post are from the talk I gave at LARGE, explaining how my research fits into this framework.

I want to be a Data DJ, do you?

Content-based Trust for Electronic Contracts

I’m at the 10th Annual International Workshop “Engineering Societies in the Agents’ World” (ESAW 2009) and gave a talk this morning about how an electronic agent can use past experience (e.g. process documentation) with contracts to predict whether it should trust a new contract proposal. You can check out the slides below. It’s interesting to be at an agents workshop… they use a vocabulary I haven’t heard in a couple of years, but it’s fun.

I was also on a panel where we had a nice discussion on the overlap between reputation and content based trust and the role of context in trust. Roles seem to be a super important topic here.

Big idea from Frank Dignum – trust reduces to machine learning

Something to think about.