I am officially in holidays till the 18th of September but I cannot leave you without an interesting link: sew your own tux! Via Jamie’s weblog.
Monthly Archive for August, 2005
Today I discussed with my supervisor and Patrick on the possible shape of the algorithm for the clustering of the messages. The starting point is that we need to define similarity between the messages so to allow for two kinds of interaction:
a- spatial browsing, to show the available resources;
b- spatial information retrieval, to pinpoint to relevant resources matching a query.
In both cases what we need is a mapping from the matrix of semantic distances to the matrix of the geographical distances. What we can use for this scope is the “semantic cohesion”, an home-made marker that determines the level of semantic similarities between a group of messages. Mapping this marker against the radius of the spatial cluster (r) we might find some “drop zones”, or parameter if this ‘r’ for which there is a substantial drop of similarities which will signal the existence of the cluster. Using this we wont incur in the problem of the local maxima.
There will be two levels in the support of interaction. The first will be the construction of the geo-semantic patters that will work at visualization level but not on the data level. The second step will be to support the interaction through the query/retrieval and the user’s feedback (i.e., social rating of the messages).
Tags: clustering, map algorithms, maps
Why spaghetti breaks into at least three pieces. Includes videos and detailed(!) model of phenomena:
http://www.lmm.jussieu.fr/spaghetti/index.html
Bent dry spaghetti do not break in half but instead in three of more pieces. With the aim to explain this surprising phenomenon, we studied a related problem, namely the dynamics of an elastic rod that is bent quasi-statically and then suddenly set free. Counter-intuitively, we find that the mere release of the rod induces a stress increase. The multiple breaking of bent rods, like dry spaghetti pasta, can then be understood as a cascade of releases (loss of cohesion upon breakings) followed by stress increases leading to new cracks.
Tags: wierd
Okay, I am late but hey, I have been to Bern today!
Dencity is a very interesting and relevant project for my research. The system aims at examining the enrichment of real urban sites by a virtual dimension of information and networking, beeing accomplished by localization of the virtual. It is about moderating between “virtual reality”-networks and the city as physical existence. It uses two-dimensional bar codes.
I have been playing a bit with the great PyGoogle wrapper and I found another great idea by the great Mark Pilgrim: Google Neighborhood.
The idea is very simple, trying to find the list of sites that are “neighbor” of a certain site/resource web, following the simple rule that a neighbor is a resource that is referenced and references the originating resource.
From this very simple idea we can start rambling on other possible applications, like some of the usage that people find out in google local. Check this description for having a glance at what I am striving for in STAMPS. All comments are welcome.
Tags: clustering, google, hack, Location Based Services, maps, Python
This guy spent some time collecting screenshot of the Mac OS interface to show the trends and the dramatic changes through the years. Check this out!
System 0.0-1.1 introduced in 1984
Tags: interaction design, mac
Matthew Coates and Tim Meldrum projected an apartment that goes right to the core fundamentals of the Cradle to Cradle principles. Not only does the building run a photosynthetic and phototropic skin made with spinach protein, but it also produces more energy than a single family’s needs, allowing the excess to be distributed to neighbors. This radical shift, from centralized energy systems today, fosters community interdependence as neighbors benefit from the resources of others.
(via)
Tags: ecology, environment, hack, new technology, politics, society, urban planning
For those of you that happen to read Italian, here is a pdf containing the transcript of the conversation between Fazio, governor of Bankitalia and Giampiero Fiorani and wife on the Antonveneta issue.
What strikes me is that only two or three passages are marked as “hot” and compromising for these two “gentlemen”, while, according to me, the whole transcript is the proof of an attitude towards corruption. Sad ….
Teaching Turing is a fun, educational environment for learning about and programming Turing machines. The goal of Teaching Turing is to show people how Turing machines work by having them program a Turing machine themselves. The structure of Teaching Turing is divided into a series of levels with the earliest stages very clearly walking the user through the basic controls of a Turing machine, then working up to a series of graphic puzzles solved through programming. We want to present the Turing machine in a simple, easy to understand way. Users can move at their own pace through the levels, or proceed to free exploration and programming of the machine.
(via)
Tags: information visualization, interaction design, learning technology, microworlds
A. Markowetz, T. Brinkhoff, and B. Seeger. Geographic information retrieval. In 3rd International Workshop on Web Dynamics, New York, 2004. [url]
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This document describe a system for mapping internet resources to geographical locations. The authors start from the idea that “all business is local”, for which almost each web page has a local context. For lots of applications geographically close information is the most important.
The autheors list previous work to map internet resources by their physical proximity highlighting the difficulties of clustering in such a way the information. So they propose to use a combined mechanism for which the structure of links pointing to a particular resource is used to contextualise it: “the local pizza parlor might be referenced by only twenty or thirty links, but all from within Marlburg.”
Tags: clustering, information retrieval
MailTags is a plug-in for Apple’s Mail.app 2.0 (Tiger) that enables you to go way beyond folder filing for organizing mail by enabling metadata tagging that are spotlight compatible.
Tags: clustering, information retrieval, mac
SPIRIT (Spatially-Aware Information Retrieval on the Internet) is a research project engaged in the design and implementation of a search engine to find documents and datasets on the web relating to places or regions referred to in a query.
The project has been working on the creation of software tools and techniques that will be used to produce search engines and websites that display intelligence in the recognition of geographical terminology. In order to demonstrate and evaluate the project outcomes, a prototype spatially-aware search engine has been built and is serving as the platform for testing and evaluation of new techniques in geographical information retrieval.
Tags: information retrieval
Somewherenear is the UK’s leading Geographic Search Engine, a guide for leisure or business travellers looking for places to visit, food, accommodation and more. “Somewherenear” helps you to find places of interest in England, Wales or Scotland.
C. B. Jones, R. Purves, A. Ruas, M. Sanderson, M. Sester, M. van Kreveld, and R. Weibel. Spatial information retrieval and geographical ontologies: An overview of the spirit project. In Proceedings of the SIGIR’02, Tampere, Finland, August 11-15 2002. ACM. [url]
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A large proportion of the resources available on the world-wideweb refer to information that may be regarded as geographically located. Thus most activities and enterprises take place in one ormore places on the Earth’s surface and there is a wealth of survey data, images, maps and reports that relate to specific places or regions.
Despite the prevalence of geographical context, existing web search facilities are poorly adapted to help people find information that relates to a particular location. When the name of a place is typed into a typical search engine, web pages thatinclude that name in their text will be retrieved, but it is likely thatmany resources that are also associated with the place may not beretrieved. Thus resources relating to places that are inside the specified place may not be found, nor may be places that are nearby or that are equivalent but referred to by another name. Specification of geographical context frequently requires the use of spatial relationships concerning distance or containment for example, yet such terminology cannot be understood by existing search engines.
I spent some time today reflecting on how to cluster the messages of the STAMPS system. So I started back from the main goal, which is to support navigation and information retrieval. Therefore the goal is to find messages that have some similarities, that either describe the same resource in a different place or either that characterize the same place with content that is related.
In the system we can expect different degrees and kinds of similarities. We will have direct similarity when a search keyword will match directly to one of the keyword of the message. Else, a bit indirect will be to have a search keyword that matches one of the stemming alternative of the messages keywords.
Then a variety of degrees of relevance can be visualized on the messages that are connected by threads to the original messages.
A different case is a search string that contains multiple keywords, in which case we can imagine a plain match in a single message but also a match on different message. In this last case also, this search set the ground for a new connection between the keywords. The same thing can goes for messages that have different keywords but that are in physical proximity. In this case is the spatial proxemics that sets the relevance between the messages.
I still have in mind that an external referrer can be used to add semantics to the playground.
Another concept keep bouncing in my mind: is it the case to have a kind of permanence of this keywords concentration in a certain spot? Can we use this information to define landmarks that characterize a certain cluster? But then the point is that a cluster needs dimensions and I cannot figure out a proper way to find these parameters along the way.
Tags: spatial clustering
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