Elasticsearch-driven decision support systems for optimizing material selection in the rocket science

The study is dedicated to analyzing the key role of decision support systems (DSS) in modern science and industry, emphasizing their ability to automate complex processes, reduce the risks of erroneous decisions, and efficiently handle large volumes of data. The work explores the advantages of document-oriented databases (DODBs) over traditional relational databases for managing diverse datasets in materials science and aerospace engineering. The application of Elasticsearch (ES), a distributed search and analytics engine, is investigated for optimizing material selection and design processes in the aerospace industry. Attention is also given to the ethical aspects of using software for making critically important decisions and the synergy between advancements in computational technologies and materials science. The integration of DSS, DODBs, and ES demonstrates the potential to enhance the accuracy, efficiency, and reliability of data-driven decision-making.