A data-visualization tool that lets users highlight aberrations and possible patterns in the graphical display.
The most exciting
thing when it comes to data-vizualization is its ability to help us in
real-time to capture powerful knowledge from massive volume of data; to extract
actionable insights and intelligence from relevant information and to get critical
signs from accurate data.
But the way
visualizations are traditionally produced, faces outliers. This means lot of aberrant
results related to any bad reductions, where you can lose information about where
those output data points came from relative to the input data set.
Based on
this critical reality, the Database Group at MIT’s Computer Science and
Artificial Intelligence Laboratory has released a data-visualization tool that aims
to let users highlight aberrations and possible patterns in the graphical
display.
Connectikpeople.co,
soon #Retinknow, can observe that, the tool automatically can determine the bad
data sources.
The new
visualization tool, called DBWipes is a work of Eugene Wu, a graduate student
in electrical engineering and computer science who developed DBWipes with
Madden and adjunct professor Michael Stonebraker.
This novel “provenance tracking” system for large
data sets can provide a compact representation of the source of the summarized
data so that users can easily trace visualized data back to the source.
The
algorithm behind this technology identifies those that most influenced the
outlier values, and summarizes those data entries in human readable terms.
