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Open data initiatives such as Linked Data have begun to see adoption (e.g. NYT), with the objective of opening up and creating relationships between databases. This presents opportunities to break down the barriers that separate individual websites and their content, creating unprecedented opportunities for the reuse and combination of different data sources. A common critique of apps has been the walled-garden approach to storing data and communicating with other apps-open data promises to change that. As a consequence we will begin to see more apps that aggregate a multitude of sources, rather than remaining tethered to any particular set of content (though those will likely continue to exist). These will be apps designed for a particular scenario and user experience-their primary point of differentiation. They will use whatever data sources are available in order to best satisfy the goals of whatever experience or scenario is being designed.

For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.

GigaPan Time Machine enables simultaneous exploration of space and time across massive datasets that could not previously be interactively explored at full spatial and temporal resolution.