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Metaweb is a service that help you build your website around entities and not just words.

Data is present in all the systems and processes in the world. IBM helps analyze this multitude of information to make intelligent decisions, while enabling business efficiency and adding value to many industries.

In the first commercial application of CeNSE technology, HP and Shell will build a wireless sensing system to acquire high-resolution seismic data. By vastly improving the quality of seismic imaging, the new system will allow Shell to more easily and cost-effectively explore difficult oil and gas reservoirs.

We thought it would be useful to propose one possible taxonomy - we call it the Snice* taxonomy - of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret (or, if you like, OSEMN, which rhymes with possum).

A data analysis service for the learning science community

This document is designed as being a simple but comprehensive introductory publication for anybody trying to get into the Semantic Web: from beginners through to long time hackers. Recommended pre-reading: the Semantic Web in Breadth.

With so much technology and networking available at such low cost, what wouldn't you enhance? What wouldn't you connect? What information wouldn't you mine for insight? What service wouldn't you provide a customer, a citizen, a student or a patient?

I've been following developments in intelligent tutoring systems for a while,  and find it interesting to see how researchers are combining artificial intelligence, learning theory, affective computing, and sensor networks to create applications that might prove to be useful and effective.

Here we've got a classmate PC demo that shows off Richard Beckwith shows off Classmate Assist which supports users seated or standing at the desk or table. This is an example of context aware computing. There are visual sensors that recognize the items on the table and instruct and walk the students through a series of educational tasks. The goal is to support existing curriculum and practices of teachers.

Hans Rosling's famous lectures combine enormous quantities of public data with a sport's commentator's style to reveal the story of the world's past, present and future development. Now he explores stats in a way he has never done before - using augmented reality animation. In this spectacular section of 'The Joy of Stats' he tells the story of the world in 200 countries over 200 years using 120,000 numbers - in just four minutes. Plotting life expectancy against income for every country since 1810, Hans shows how the world we live in is radically different from the world most of us imagine.

An entity graph of people, places and things, built by a community that loves open data.

Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. EDUCAUSE's Next Generation learning initiative offers a slightly different definition "the use of data and models to predict student progress and performance, and the ability to act on that information". Their definition is cleaner than the one I offer, but, as I'll detail below, is intended to work within the existing educational system, rather than to modify it. I'm interested in how learning analytics can restructure the process of teaching, learning, and administration.

Today, we measure the size of the Web in exabytes and are uploading to it 15 times more data than we were 3 years ago. Technologies for sensing, storing, and sharing information are driving innovation in the tools available to help us understand our world in greater detail and accuracy than ever before. The implications of analyzing data on a massive scale transcend the tech industry, impacting the environmental sector, social justice issues, health and science research, and more. When coupled with astute technical insight, data is dynamic, accessible, and ultimately, creative. Marissa Mayer will speak to the power of data and the role it plays in Google's innovation. She will present on the technology trends that are changing our relationship with data, discuss fresh Google products that creatively put data to work, and offer her vision for the future of data in driving the Web forward.

Hilary Mason presents the history of machine learning covering some of the most significant developments taking place over the last two decades, especially the fundamental math and algorithmic tools employed. She also exemplifies how machine learning is used by bit.ly to discover various statistical information about users.

These, and other similar projects, promise the emergence of a new social and theoretical paradigm whose goal is to decipher the web of social interactions generated by social media.