Finally I can start the Field experiment.

Keyhole co-founder and Second Life veteran Avi Bar-Zeev writes a long post that argues Google Earth has a long way to go before it approaches what we commonly imagine to be the metaverse. A few random quotes I liked:

What we really need is a new language of object representation that encapsulates and preserves form and function, aesthetics, style, meaning, and behavior, all tightly coupled and never discarded in the “art pipeline” until the object is finally rendered on your screen. And the big problem here is that things like semantics are so far from concrete math that any program, even if it supports the concept, can have its own varying interpretations. So this language needs to be fully expressed, down to a fairly programmatic level, so that these assumptions are clear and enforced. It should contain the instructions on how to render the 3D object, but also how to create it, use it, kick it, break it, change it, and even say what it is.

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So to the extent Google helps you search the non-semantic web, they can certainly help you search the non-semantic set of 3D objects too. And they’ll succeed, to the extent there’s some value to add beyond simple keyword searching (think PageRank). But is it world-changing? Not until we change the fundamental properties of the virtual world.

I kind live the same trying to combine the semantics of little messages and the geometry of the place where the author attach them: semantics is very far from geometry. Maybe is closer to geography because of the meanings we attribute to. How to re-conciliate the two?

Tags: art, information visualization, map algorithms, maps, modeling tool

ZoneTag is a research prototype that aims to leverage the context available from cellular phones to create new experiences around media, and in particular, photographs. The name ZoneTag was inspired by Susan Sontag’s 1977 quote asserting that “Everything [in the world] exist to end up in a photograph”. Indeed, ZoneTag’s first tasks involve enabling users to capture, share, search (and find) as well as discover images from this excessively growing and accessible repository.

An early prototype release from ZoneTag is available from zonetag.research.yahoo.com. In this first release, for the Nokia Series 60 cameraphones platform, the application allows you to quickly and easily upload images from your camera phone to Flickr. The photos uploaded can be automatically annotated with the location (usually based on cell tower) in which they were taken.

This project is very similar to Radar.

Tags: CellID, graffiti, information visualization, Location Based Services, maps

Back from Barcelona I managed to reorganize the notes I took during the workshop. I have to admit that most of the presentations were a bit, herr …, technical for my taste. However here are the notes.

I presented the work I did with Pierre Dillenbourg and Lorenzo Viscanti and I received some good feedback that I will use to deep my knowledge of semantic information retrieval algorithms.

Tags: data mining, information retrieval, information visualization, search engine, spreading activation

Who is paying my bandwidth? :

Researching Barcelona for a vacation?  Its always a good idea to consider staying in a time share.  Relax and enjoy yourself where ever you may choose get timeshares for rent.  Make sure to read up on time share regulations to educate yourself.   If you are looking for a change of scenery then you need timeshares for sale.  A good resource that explains how a time share works is something you need to look over.

MAPresso, developed by Adrian Herzog, is a free Java applet for making choropleth maps and cartograms. These kind of distorted maps equalize the surface of a certain country on the map with a particular feature (the population, for instance).

In the picture below: Italy, South Tyrol: Election results (elezioni provincali 2003) by the Civic Network.

Tags: anamorphosis maps, information visualization, map algorithms, maps

The maps presented on this website are cartograms, otherwise known as density-equalising maps. The maps of the world you are used to seeing attempt to represent countries according to their land area. A cartogram re-sizes each country (or other geographical unit) according to some other variable – for example population, GDP, number of people with AIDS, etc. In the population example, densely-populated country such as the UK will appear much larger than it does on a standard map, and sparsely populated countries will appear smaller.

In the picture below, the map of Crude Petroleum Import.

Singapore imports (net) more crude petroleum than anywhere else, when this is measured per person. The value of imports per person is US$ 1808 per year. Burundi has the lowest value of net imports of crude petroleum per person: 0.02 US cents worth is imported per hundred people that live there.

One explanation for this difference between Singapore and Burundi is as follows. Singapore is a rich island well positioned on trade routes, so can afford to receive large amounts of oil per person. Burundi is a poor landlocked central African territory, so there are many barriers to imports.

Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.

Tags: anamorphosis maps, art, map algorithms, maps

This is the title of the presentation I am going to give, together with Lorenzo Viscanti, at the Workshop the Future of Web Search, held in Barcelona on May 19-20, 2006.

I am basically going to present the work we did on the Visual Information Retrieval Experiment, as well as the general outline of my thesis. Here are the slides of the presentation.

UPDATE 2-2-2007: Fabien pointed me to the video of the talk. From the page is also possible to download the updated slides of the presentation.

A. Sarma. A survey of collaborative tools in software development. ISR Technical Report UCI-ISR-05-3, Donald Bren School of Information and Computer Science, University of California, Irvine, USA, 2005. [pdf]

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Collaboration is at the heart of software development. Virtually all software development requires collaboration among developers within and outside their project teams, to achieve a common ob jective. A number of classification frameworks exist that can be used to classify collaborative tools. In addition to placing the various tools in context, developers can use these kind of frameworks to select the right mix of tools for their situation. Each classification framework has a different focus: some provide a detailed taxonomy to compare tools in a particular area, some classify tools based on the functionality of the tools, some classify tools based on the high-level approach to collaboration that the tools take, and so on. However, currently no framework exists that classifies tools based on the user effort required to collaborate eectively. This however is also a critical component in choosing the “right” set of tools for a team.

In this survey, the authors take a look at collaborative tools from the perspective of user effort. For the purposes of this paper, the users define user eort as the time expended in setting up the tools, monitoring the tools, and interpreting the information from the tools. While they cannot quantify the efforts required of each tool in detail, it is clear that there is a natural ordering among dierent groups of tools. We propose a framework that identifies these groups and highlights this ordering. Based on this framework, their survey organizes the individual tools into tiers.

Tags: collaboration tools, software visualization

Google Trends is a nice tool to reconstruct how some search terms have been used over time. In the picture below I tried the name of two learning technologies paradigm which do not mean anything per se. Usually around these buzz words there are very different ideas and a great deal of interest. The graph shows how people are less and less using the word e-learning  and how the word mobile learning is barely used since the last quarter of 2004. Interesting.

With Google Trends, you can compare the world’s interest in your favorite topics. Enter up to five topics and see how often they’ve been searched for on Google over time. Google Trends also displays how frequently your topics have appeared in Google News stories, and which geographic regions have searched for them most often.

Tags: data mining, google, information metric, search engine, society, statistics