Oceanographic studies generates lot of sub-surface data sets from different instruments. Quality Control (QC) is performed on them. Some time bad data are flagged as good and vice versa. As these data are huge in number handling can be cumbersome. Alternatively graph theoretical methods can be applied to QC them. Oceanographic data has dimension like time, depth, longitude and latitude for any given parameter. The parameter when plotted against the dimensions like longitude or latitude projects an inherent patterns. For a given depth with the observed pattern a polygon with least area can be derived and with the help of Point-in-Polygon methods the outliers can be detected. But as the ocean is 3 dimensional, it would be ideal to obtained a n sided, m dimensional structure with least volume with in which the oceanographic profile can be cross checked for its correctness.
Availability of such an application could be useful for detecting outlier from huge amount of data from all instruments. This eliminates manual intervention and speed up the process of quality control.
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