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A key to the process of turning
data into information is to identify the patterns. Patterns can
be simple clusters of existence or absence, as shown in
Figure 10. Patterns can be compound
interrelationships derived from regression analysis.
Regression analysis is a type of mathematical filtering.
Geologists take a satellite image and filter it
with an edge detection algorithm. Then look for circulars
and linears and use this information to interpret subsurface
geology. Circulars might represent a salt dome
surface erosion, or an eroded anticline, or a meteor impact
crater. Linears might represent the impact of a fault scarp, or
erosion of an outcropping rock layer. Patterns can also be
complex, derived from clustering, factoring, or ordination.
Each of these three types of pattern finding have application on
both axes of the science-religion matrix.
The concept is patterns provide
context to data. Information is data in context, related to a
specific purpose (see Figure 11).
Information is a third dimension on the science-religion matrix,
which can help in understanding rips or tears in the matrix.
Alan Kay stated “the 3-D spread-sheet should be the universal
language for problem definition.”1.47 Once data is collected
and entered into the appropriate databases, the first step in
pattern finding is to sort the data. Text files can be sorted
by the number of occurrences of a word or phrase, as well as
spatially, temporally, and by activity.
In my work, as illustrated
in Appendix IV, spatial data are
formally organized against Infinite GridSM data
types. A data type is an index. For instance, ASCII characters
have a binary data type, which means we can type a letter or a
number into a computer and not have to learn the binary code.
Temporal data are formally organized against the
TimedexSM data types. Activities, or processes, are
formally organized against the Knowledge BackboneSM
data types. Patterns or information begin to emerge simply by
reviewing data stored in one of these 3-D (three-dimensional) or
N-D spread-sheets.
Geologic and geophysical maps are a
practical example of these 3-D spread-sheets. Landmark Graphics
Corporation developed automatic horizon pickers (for example the
Zoned-Auto-Picker (ZAP)), which reduced 3-D volumes of
reflection seismic samples to surfaces. There are numerous
region growing algorithms, which are similar to ZAP. Some of
the most sophisticated have been developed by companies like
General Electric to support medical imaging tools they sell to
hospitals. These algorithms are used to extract boundaries
around organs from CAT-Scan (Computer-Aided-Tomography) or
similar non-intrusive imaging tools.
Religious scholars have been
developing similar pattern finding tools. One of the most
interesting is summarized in “The Bible Code,”1.48 a book
about patterns within the original Hebrew text of the Bible.
I believe these efforts are in their infancy, and that we
will see many new developments in this area over the next
few decades. There are man new patterns, and as a result
a lot of new information, yet to be discovered in existing
data. As we use this information we gain experience, and an
accumulation of this experience provides knowledge and a
deeper understanding of the
matrix.
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paradoxes