A Method of Big Data Collection and Normalization for Electronic Engineering Applications

Data collection and storage have become the greatest challenges and tedious processes in data science engineering. This chapter mainly focuses on the data analytics of creating normalized data from unprocessed data. This reduces the manipulation of data when it is of a different form. Data science is an interdisciplinary field that fuses science and technologies by using algorithms, tasks and devices to extract usable data from raw unstructured data. Machine learning has a wide range of applications in the semiconductor industry. Data acquisition is the most important aspect of data analytics in the materials science domain. The enhanced database architecture and the data storage facilities facilitate the access of databases through larger query and its assessment. The normalization of the data helps the user to create a detailed analysis in terms of the processed data.