Optimization of Big Data Using Rough Set Theory and Data Mining for Textile Applications

Prediction of any property of the material has attracted the attention of many scientists all over the world. In order to produce better products, building construction, remote sensing, prediction of water availability, etc., information technology (IT) has played a dominant role. Textile industries are one of the sources to decide our economy of our country. Among various fabrics, single jersey finished cotton knitted fabrics are liked by many people because of its comfort. It has many comfort properties such as fabric width, fabric weight, fabric shrinkage, and fabric handle. Although attempts have been made to predict fabric width and fabric weight, the type of network that used was old and no new approaches have been made. In order to remedy this situation, new techniques such as rough set theory and data mining techniques have been applied with a view to predicting fabric width and this research paper addresses this issue in depth. We propose a new algorithm scalable rough priority prediction model (SrPPM) to predict fabric width of single jersey finished cotton knitted fabric with real-time textile big dataset over Hadoop MapReduce framework. Our results show that our proposed framework works well, in terms of time efficiency and scalability.