Representation, uncertain imprecision, and dependency

Abstract

The paper is an initial exploration of representation, uncertain precision, and dependency as a connected concern that is not easily decomposed. Often for reasons of computational simplicity, the assumption is made that attributes are independent of each other. Similarly, whether dependency exists is classically expressed in terms of first order logic. The actuality is that often the assumptions produce results that are not good representations of reality. An integrated representation of multiple, related attributes is difficult. Usually, different attributes have different ranges and linearity. Sometimes, normalization is a first step in meaningfully representing different kinds of data, or, as a first step to the combination different kinds of data. Most values are not crisply known. A method of dependency representation is needed. The issue of how to represent both dependency and uncertainty needs to be resolved. In a very real sense, dependency representations and their imprecision both enriches and constrains how we approach the solutions to our problems.