Abstract: Apparatus and associated method for constructing a quantifiable model, comprising: an object definer for converting user input into at least one cell having inputs and outputs, a relationship definer for converting user input into relationships associated with said cells such that each said relationships is associatable with said cells via one of said inputs and outputs, a quantifier for analyzing a data set to be modeled to assign quantitative values to said relationships and to associate said quantitative values with said associated inputs and outputs, thereby to generate a quantitative model. The model is useful in automatic decision-making and process control and for process simulation and study. The model building methodology provides for structured and quantity reduced investigation of process data since a qualitative model is used to guide the data analysis. The methodology also allows for obtaining new information regarding such a process through the resulting quantitative model.

Abstract: In an automatic decision-making system, a method and a tool for the reduction of the dimension of data mining, which is automatically coupled to an empirical predictor of the system. The method includes a qualitative modeling of the interrelations between various objects whose attributes are relevant to a score made by the predictor according to which decisions are made, wherein this relevancy is determined by an input of a domain expert to the problem in hand. The model is called a Knowledge-Tree and its conclusions are represented by a graphical symbolization called the Knowledge-Tree map. Data mining, which follows the construction of the Knowledge-Tree map regards only datasets which are associated with logical and validated branches of the knowledge tree. Because the expert input which reduces the dimension of data mining was completed prior to data mining, interception by human reasoning is not needed after data mining and the decision making process can proceed automatically.