In the present contribution, new methods for power grid data acquisition, for smart grid monitoring, for large-scale data exploration, and for deep data analysis are developed.
Interval aggregation is useful for data reduction and comparison, but in our vision, deep data analysis should be enabled considering full observation possibilities.
These techniques include unsupervised learning and clustering techniques, supervised neural network-based classification, and deep data analysis of physically relevant multivariate statistics data.
The need comes from the Catch-22 that often arrests deep data analysis, in that you have to understand the data to analyze it, but you must analyze it to understand it.
And why should that growth slow down, as we find ever more ways to employ deep data analysis tasks in both the investigation of the natural world and the creation of new tools and services?
Posts so far include a deep data analysis of tweets from Wimbledon (Andy Murray's celebration tweet now has over 92,000 RTs), a visualization of NBA Finals tweets, and the story of a tweeting tightrope walker.
Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.
This exemplifies the ability of deep data analysis not only to highlight statistically significant traits in multidimensional data, but also to extract physically and chemically relevant behaviors, preserving the units of measurement in the process.
DataSift, a data analysis company that provides developers and third parties with access to Twitter, Facebook and other social data sources, is teaming up with NewsCred to allow clients to perform deep data analysis and monitoring of social media and news sources.
But in addition to being an interesting business in its own right, Blue Water Satellite is an example of the deep data analysis industry which in a way has always been with us, but more recently has been flourishing due to the sheer volume of useful data.
