  The ability to collect and analyze large amounts of data is a growing problem
within the scientific community. The growing gap between data and users calls
for innovative tools that address the challenges faced by big data volume,
velocity and variety. One of the main challenges associated with big data
variety is automatically understanding the underlying structures and patterns
of the data. Such an understanding is required as a pre-requisite to the
application of advanced analytics to the data. Further, big data sets often
contain anomalies and errors that are difficult to know a priori. Current
approaches to understanding data structure are drawn from the traditional
database ontology design. These approaches are effective, but often require too
much human involvement to be effective for the volume, velocity and variety of
data encountered by big data systems. Dimensional Data Analysis (DDA) is a
proposed technique that allows big data analysts to quickly understand the
overall structure of a big dataset, determine anomalies. DDA exploits
structures that exist in a wide class of data to quickly determine the nature
of the data and its statical anomalies. DDA leverages existing schemas that are
employed in big data databases today. This paper presents DDA, applies it to a
number of data sets, and measures its performance. The overhead of DDA is low
and can be applied to existing big data systems without greatly impacting their
computing requirements.

Databases;Distributed, Parallel, and Cluster Computing