Dynamic Data Mining Based on the Stability of Dynamic Models

This work presents a new approach based on the use of stable dynamic models for dynamic data mining. Data mining is an essential technique in the process of extracting knowledge from data. This allows us to model the extracted knowledge using a formalism or a modeling technique. However, the data needed for knowledge extraction is collected in advance, and it can take a long time to collect. The objective is therefore to move towards a solution based on the modeling of systems using dynamic models and to study their stability. Stable dynamic models provide us with a basis for dynamic data mining. In order to achieve this objective, the authors propose an approach based on agent-based models, the concept of fixed points, and the Monte-Carlo method. Agent-based models can represent dynamic models that mirror or simulate a dynamic system, where such a model can be viewed as a source of data (data generators). In this work, the concept of fixed points was used in order to represent the stable states of the agent-based model. Finally, the Monte-Carlo method, which is a probabilistic method, was used to estimate certain values, using a very large number of experiments or runs. As a case study, the authors chose the evacuation system of a supermarket (or building) in case of danger, such as a fire. This complex system mainly comprises the various constituent elements of the building, such as rows of shelves, entry and exit doors, fire extinguishers, etc. In addition, these buildings are often filled with people of different categories (age, health, etc.). The use of the Monte-Carlo method allowed the authors to experiment with several scenarios, which allowed them to have more data to study this system and extract some knowledge. This knowledge allows us to predict the future situation regarding the building's evacuation system and anticipate improvements to its structure in order to make these buildings safer and prevent the greatest number of victims.