Optimizing Material Data Management and Analysis Using Engineering Web Applications

Abstract

The development of technological devices has been accompanied by a significant increase in
the generation of data, as well as the evolution of terminology and formats used to store it. In the past, data
was typically stored in a variety of formats, such as relational databases, Excel, .hdf5, .xml, .csv, .docs, .rtf,
.odf, and others, each offering distinct methods of data organization. Among the most complex types of
data are material data, which are crucial for engineers when performing analyses and designing
experiments. Engineers can leverage this data to manipulate it in ways that answer specific questions posed
by a company or solve particular challenges, enabling them to recycle and reuse data efficiently.
However, with the rapid advancement of technology, accessing older file formats has become more
challenging. To address this, web applications have emerged to manage and store material data more
effectively. These platforms allow engineers to perform statistical analyses while optimizing the storage,
recycling, and management of material data.
The objective of this paper is to provide a comprehensive review of existing academic and scientific
literature on material data. It will also include a survey targeting engineers to identify the best practices for
storing material data, based on its type, physical properties, mechanical characteristics, and standard
classifications. Furthermore, the paper will explore how material databases are structured and demonstrate
the process of reading large hdf5 files using Django and Python, with a focus on achieving optimal
performance.