A Study to Renovate Image Data Using Data Analytics Methodologies

Abstract

In the present scenario storing voluminous data becomes a challenging issue. This research work is an initiative to convert the already existing image analysis data of a pipeline study for future use. Image data is obtained by the process of digitized data concept with the application of k-means cluster algorithm and morphological operator. Data obtained through this process is voluminous and hence a new technique should be adapted to store and retrieve the data for any further use and reference. As data analytics is emerging as one of the methods to analyse data and store, this study helps to deal with the same. Data collected from oil pipeline image processing with pattern recognition which used the technique of unsupervised learning is to be converted under the process of Machine learning. The key step for this process is to first determine the data set for evaluation and then to later convert these date to the required base set, this helps to save the data in large volumes for future use. To perform this task a key study on Data Analytics and Machine Learning are required. Data analytics refers to the quality and quantity of data extracted and categorized from various resources, these data are collected to analyze the structural behaviour and pattern, which differs accordingly to the need of the organizations. The process of machine learning starts with the collection and scrutiny of data that can be obtained through direct or indirect evaluation of data, which will give a pattern for the defined image for further process. The proposed machine learning algorithm has to be fixed to study the data that is been used in the pipeline image conversion task. Structural element concept based on mathematical morphological operator helps to define the size of the data to be stored. Algorithms based on structuring element paves way for redefining the concepts to shrink and save the data for further use. This study will enhance the users to save the data from pattern recognition to Machine learning using data analytics techniques. The overall concept is to save the existing image data in data analytics techniques for future use and references. This research paper spotlights a way for an energy firm to congregate, identify and sway the distinct statistics obtained through image analysis and processing. It also helps to build a strong and confident efficient productivity thus giving an opportunity for best practices of data management breaking down the question of data security and enhancement. This helps to avoid data theft by securing the data obtained through the image analysis and processing concepts.