## 9 Processing of Complex Data Analysis Models Using Intelligent Methods
### Deep Knowledge Modeling and Constraint-Based Fast Preprocessing  
The preprocessing of data is effectively utilized in different domains, such as number theory, cryptography, intelligent measurement, education, and bioinformatics. The chances of making an improvement will be quite slim without a comprehensive description of the surroundings. It is clear from working with description logic that logic-based modeling is challenging to control, that it is challenging to merge the logical and statistical phases of the data science cycle, and that their algorithmic complexity is often considerable. On the other hand, it is possible to reason using the body of knowledge, and this capability enables the development of knowledge- and data-driven open systems. It is simple to combine the suggested study with the mentioned non-classical logical methodologies. More information is available in [25]. When discussing data-driven methodologies, artificial neural networks (ANNs), such as deep learning, are frequently used. In order to enhance the quality of human-like reasoning, this paper investigated a novel data-driven methodology that makes use of modeling and constraint fulfillment characteristics. The constraint satisfaction methods are frequently used in data-preprocessing issues. A selection of data was applied aiming to complete preprocessing more efficiently, and the considered deep-modeling constraint satisfaction methods significantly improved this process. Currently, the following groups of novel logically-inspired constraints have been investigated in [25; 26; 27]. The mema method for control is named Puzzle but it significantly differs from the methods constructed for solving puzzles, such as [26; 27; 28; 29]. The latter are ineffective because of the usage of random number strategies. The proposed Puzzle approach is easily combined with these and other methods [30] with the aim to increase their efficiency. Initially the classical constraint satisfaction methods can be applied with the purpose to form a closed focus area where certain data may be logically connected. Let us focus on the considered two objects, ones of the many enclosed in the area from Figure 9. The closed 'focus/selection area' helps to reveal new knowledge concerning the enclosed objects. The data analysis concerning this case can reveal new knowledge, for example, that \(M\) implies \(N\) or that \(M\) has some relationship to \(N\). The second case is the result of the classical link analysis and/or corresponding data mining applications. Some general disadvantages: the classical approach works with a priori given data and is not intended for the elaboration of open systems. In practice, the constraints should be dynamically changed depending on the current knowledge/data. This becomes possible after the introduction of new types of non-classical constraints. Generally speaking, there should be new types of constraints introduced. Constraint violation is impossible in the classical case but this case should be reconstructed. Every rule/constraint could be defeated depending on the conditions. The new types of constraints make it possible. Somebody who exposed their body to radiation generally does not think about cancer that could occur 20 years later. Many other application problems arise in medical practice where the problems are gradually accumulating. With new conditions, additional questions arise: **why** the constraints are imposed, **what** and when violates it, **where** it could be defeated, and other use cases. Binding, pointing, and crossword are the new groups of constraints. There exist many binding situations, and some of them have been researched in this article. The first case is when the maximum binding possibility is concentrated in the center of the area, and the distance from it is a function diminishing its value.