PUBLIC MARKS with search machine

I have gathered some notes on my use of machine tag for biology taxonomy. So you can express easily the classification on web services using tagging (like flickr or del.icio.us) a machine tag that can be read by human and by machine (read information automated systems) :

En effet, la numérisation massive des échanges et de la diffusion transforme en profondeur la relation entre l'usager et les médias, a la fois au sens de données de toutes natures (textes, images fixes et animées, sons, ...) et au sens d'instances éditoriales de masse ou communautaires. Internet, le Web et les blogs, mais aussi la Télévision Mobile Personnelle, les pod-casts, la V.O.D., les services accessibles en téléphonie mobile, etc. modifient les contenus et les programmes, impulsent l'émergence de nouvelles écritures, mais bouleversent aussi les économies en place ainsi que les pratiques de communication interne et externe des organisations et le travail collaboratif , créent des espaces contributifs d'expression individuelle et collective, redéfinissent les frontières entre professionnels et amateurs.

# A scalable threaded asynchronous state machine model. On a typical setup, 2 or 3 threads per core is enough to drive a large amount of traffic.

# Feature-rich HTTP/1.0 and HTTP/1.1 support. We fair well in various tests like CoAdvisor.

# Plugin architecture, making it easy (well, easier) to extend and customize your server.

# Well documented.

It occurred to me that Wikipedia could be the basis of an all-purpose machine tag format to describe just about anything in the universe. Wikipedia aspires to have topics on everything, so creating tags associated with Wikipedia topics could be a foolproof way to disambiguate content.

A significant weakness of HTTP in my view is its dependence on the MIME standard for media type identification and on the related iana registry. This registry is a limited bottleneck that does not have the capacity to deal with the media type definition requirements of individual enterprises or domains. Machine-centric environments rely in a higher level of semantics than the human-centric environment of the Web. In order for machines to effectively exploit information, every unique schema of information needs to be standardised in a media type and for those media types to be individually identified. The number of media types grows as machines become more dominant in a distributed computing environment and as the number of distinct environments increases.

Every time that you login into your local Unix-like machine or a remote hosting server through a FTP client to upload a file or use SSH to get your stuff done, you’re leaving behind a trail of evidence showing your online behaviour: where and when you log in, how often and how long your online sessions are, in short: your modus operandi. This visualization tool unveils this hidden data, which is gathered by running a few builtin UNIX commands and is analyzed onsite.

The Reality Checking Device is a poetic information machine that stands in the tradition of the ancient Greek oracle. It confronts people with their own self and their personal anxieties. It helps to identify all those scenarios we really do not need to fear – despite being told the opposite by mass media.

Reality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.

The original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.

Apple's Time Machine is a great feature in their OS, and Linux has almost all of the required technology already built in to recreate it. This is a simple GUI to make it easy to use.