The importance of materials in our life is well known to the world. Material science is one of the crucial research domain which deals with planning, development, synthesis and analysis of materials and its use in the real applications. The scientists and researcher are using highly advance technologies for the production and design of new materials in present era; however the process is time consuming. Machine Learning can be defined as a set of techniques that learn from the large amount of available data and make predictions for the new data. Recently, machine learning has gained a great attraction in material science research and has been used in several research studies. Machine learning techniques have the potential to learn from the large amount of materials data and make predictions about different properties for new materials of good quality. These predictions can help in designing new materials. In this paper, we present a comprehensive review on the machine learning applications in different material science domain. We believe that the paper would be useful for the researchers, academicians and students who are involved in the design of new materials.
Big data is large volume, heterogeneous, distributed data. Big data applications where data collection has grown continuously, it is expensive to manage, capture or extract and process data using existing software tools. With increasing size of data in data warehouse it is expensive to perform data analysis. In recent years, numbers of computation and data intensive scientific data analyses are established. To perform the large scale data mining analyses so as to meet the scalability and performance requirements of big data, several efficient parallel and concurrent algorithms got applied. For data processing, Big data processing framework relay on cluster computers and parallel execution framework provided by MapReduce. MapReduce is a parallel programming model and an associated implementation for processing and generating large data sets. In this paper, we are going to work around MapReduce, use a MapReduce solution for handling large data efficiently, its advantages, disadvantages and how it can be used in integration with other technology.