Creating More Data Value than is Captured
Using Hierarchical Structures
Hierarchical structures are very unique not only in how well they organize data, but also in how they naturally capture more meaning than is stored with the data. Even more impressive is their ability to dynamically process this natural goldmine of meaning in unlimited ways that further increases the value of the stored data. With the increased use of hierarchical XML data structures today, there is an incredible amount of unused data value potential available.
Hierarchical structures are easily built and expanded naturally over time. Suppose we start building a hierarchical structure by creating and populating node A over node B. Then at some point we add node C also directly under node A and also start populating it. This is an example of reuse by reusing the already existing node A and its data. We also know what nodes node A has control over and how nodes B and C are related to each other. With the addition of each new node, the data value will increase in a nonlinear fashion. At this point, this multipath nonlinear hierarchical structure looks like the one below.
The interesting question is how are nodes B and C related. How does a query language handle this? Can node C be selected for output based on a data value in node B? Can node A be selected on data values in multiple nodes B and C? Does this capability dynamically increase data value?
The answers to these questions are: B is related to C and C is related to B through A. This means B can be used to select C as in SELECT B FROM ABC WHERE C=5 and visa versa. And this most certainly does increase the data value dynamically.