Automatically Increasing Data Value Using Hierarchical Data Structures
Automatically increasing data value is just one of the many reasons that hierarchical data structures are so powerful and useful. Let us suppose that I start building a hierarchical structure by creating and populating node A over node B. Then at some point I add node C directly under node A and also start populating it. At this point, this nonlinear (multipath) hierarchical structure view that I have named “ABC” looks like this: A / \ B C Now that I have built this structure to use query paths A/B and A/C separately, I can also use this separately populated structure to also specify the following nonlinear hierarchical structure queries involving multiple pathways because they each reference nodes B and C: SELECT C FROM ABC WHERE B=7 SELECT B FROM ABC WHERE C=9 SELECT A FROM ABC WHERE B=7 AND C=9 SELECT B, C FROM ABC WHERE A=3
The above nonlinear queries automatically utilize the nonlinear structure semantics that exist automatically between the separate linear path additions. They utilize more semantics than linear path queries because every node added by a linear path addition is also related to every node in every other pathway in the structure and each has a separate meaning associated with it. This adds considerable potential data value and significantly increases the possible queries to the data structure considerably increasing the overall data value of the entire hierarchical structure with no additional effort.
The above nonlinear hierarchical queries also demonstrate the increased level of query processing power added by selecting data from one path by utilizing data from another path. This is also used for advanced decision support queries.
All the ubiquitous nonlinear hierarchical structures that are being used linearly today are utilizing only a fraction of their data value. Nonlinear hierarchical processing automatically allows any combination of pathways to be queried to derive the desired result. In addition, nonlinear queries also support navigationless processing which enables even nontechnical users to specify these more powerful queries making them even more useful. Deriving this additional information from already available data is similar to utilizing the information contained in unstructured data except with hierarchical structures the additional information is free for the taking. This is because it involves no additional work by the user. So it just makes good business sense to utilize this capability.