Development of a methodology for analyzing big data in order to predict changes in the phases of the life cycle of elements of engineering equipment of buildings and structures

Abstract

Introduction. The roots of modern predictive analytics go back to the 1940s, when governments began using the first computational models: Monte Carlo, neural network computational models, and linear programming. In the 1960s, corporations and research institutions began the era of computer-assisted commercialization of analytics. Then in the 1970s - 1990s it became more widespread in organizations. Tech startups have made Prescriptive Analytics and real-time analytics a reality. Materials and methods. Data from open sources was used. The subject of the study is the history, current state of predictive analytics systems and the prospects of the developed methodology for analyzing big data in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures. The preparation and visualization of information was carried out using Microsoft Office Excel. Results. The terms, history of appearance, development and current state of predictive analytics systems are studied. The perspectives developed in the dissertation work, the methodology for analyzing big data in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures, have been studied. The use of “Shewhart Control Charts”, methods of cluster and qualimetric analysis in scenarios unusual for them in the dissertation work allows us to count on the positive prospects of the developed methodology. Conclusions. Predictive analytics in the construction field is one of the most promising areas of big data analysis. The big data analysis technique developed in the dissertation in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures is based on the use of modern algorithms. The scientific novelty lies in the approach to analysis, which uses a combined scheme in which cluster and qualimetric methods of analysis are used to search for equipment elements close to changing the stage of the life cycle.