SUMMARY. Qualitative variables take symbolic values such as cat, orange, California, Africa. Often these values can be arranged in levels of deeper detail. For example, the vari-able place_of_birth takes as level-1 values Africa, Asia... as level-2 values Nigeria, Japan... as level-3 values California, Massachusetts... These values are organized in a hierarchy H, a mathematical construct among these values. Over H, the following are defined: (1) the function confusion resulting when using a symbolic value instead of another; (2) the close-ness to which object o fulfills predicate P; (3) a method which allows precision-controlled retrieval for relational databases whose objects have symbolic values.

An emerging area of digital data processing is the computer-based intelligent analysis and synthesis of information flows/streams. These include in particular processing of audio, hypertext, image, text, video, mixed, etc. data that travel over the Internet. The final goal of most current research efforts is to build the Semantic Web in the sense defined by the World Wide Web inventor Tim Berners-Lee. One of the main problems for success of this project is how to automatically extract (and then interpret) semantic components from unordered data flows/streams. This problem was already stated by Gestalt school in early 1920s in case of the human visual perception. Note that an efficient human-machine interaction is the stumbling block of modern computer technologies. In this chapter, we are going to discuss the foundations of semantic associative data analysis and synthesis to approach the solution of these fundamental problems, whose solutions are indispensable for the Semantic Web development. Data semantic associative analysis and synthesis consists of two main components – semantic-mind data analysis and object-oriented data integration (synthesis). They will be described in this chapter. Think about meaning… The words are becoming themselves. (Carroll, L.)