Biomedical Engineering Reference
In-Depth Information
In contrast to many other workfl ow or pipelining tools, KNIME nodes
fi rst process the entire input table before the results are forwarded to
successor nodes. The advantages are that each node stores its results
permanently and thus workfl ow execution can easily be stopped at
any node and resumed later on. Intermediate results can be inspected
at any time and new nodes can be inserted and may use already
created data without preceding nodes having to be re-executed. The
data tables are stored together with the workfl ow structure and the
nodes' settings. The small disadvantage of this concept is that
preliminary results are not available as soon as possible as in real
pipelining (i.e. when single rows are sent along and processed as soon as
they are created).
Alternatively, many tasks performed via workfl ows can also be (often
very simply) accomplished by applying simple spreadsheet programs or
hand-written scripts. However, a workfl ow is a much more powerful
method. In contrast to spreadsheets, it allows access to intermediate
results at any time and can handle many more data types than just
numbers or strings - to name only two advantages. In comparison to a set
of scripts, a workfl ow is much more self-documenting, thus even non-
programmers can easily understand which tasks are performed. Moreover,
KNIME offers many more useful features, some of which we briefl y
describe in the following sections.
6.1.1 Hiliting
One of KNIME's key features is hiliting. In its simple form, it allows the
user to select and highlight several rows in a data table with the result
that the same rows are also hilited in all other views that show the same
data table (or at least the hilited rows). This type of hiliting is simply
accomplished by using the 1:1 correspondence between the tables' unique
row IDs. There are, however, several nodes that completely change the
input table structure and yet there is still some relation between input and
output rows. A nice example is the MoSS node, which searches for
frequent fragments in a set of molecules. The node's input are the
molecules, the output the discovered frequent fragments. Each of
the fragments occurs in several molecules. By hiliting some fragments in
the output table, all molecules in which these fragments are contained are
hilited in the input table. Figure 6.2 shows this situation in a small
workfl ow. In this fl ow, public nodes from Indigo (see also below) are used
to display the molecular structures.
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