PROCEEDINGS IPMU '08
Using Imprecise Complexes To Computationally Recognize Causal Relations In Very Large Data Sets
Lawrence J. Mazlack
Computationally recognizing causal relationships
in data is fundamentally important
to good decision-making. There
are vast amounts of computer stored,
multi-faceted data. Understanding how
stored data items affect each other is crucial
in making good decisions. The most
important decisional information is an
understanding of causal relationships. A
method to discover causality from large,
observational data sets would be transformative.
Broadly effective computational
methods are computationally untested.
An abundance of digital data riches
promise a profound impact in both the
quality and rate of discovery and innovation
in science and engineering, as well as
in other societal contexts. Worldwide, researchers
are producing, accessing, analyzing,
integrating and storing massive
amounts of digital data daily, through observation,
experimentation and simulation,
as well as through the creation of
collections of digital representations of
tangible artifacts and specimens. Modern
experimental and observational instruments
generate and collect large sets of
data of varying types (numerical, video,
audio, textual, multi-modal, multi-level,
multi-resolution) at increasing speeds.
Often, the data users are not the data producers,
and they thus face challenges in
harnessing data in unforeseen and unplanned
ways. In many science or engineering
applications, for example, in
mesoscale weather prediction or critical
infrastructure protection applications, the
ability to gather, organize, analyze,
model, and visualize large, multi-scale,
heterogeneous data sets in rapid fashion is
often crucial.
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