A Hadoop based Maching Learning Technique for Semantic Indexing of Learning Objects in Big Data Environment

Today with the big data generated every lapse of time, BIG DATA comes to offer several opportunities to store and manage this huge amount of data. The processing and analysis of these data becomes essential in order to draw the most relevant information and knowledge that can help us to make our decisions. This set of treatment is possible via different machine learning techniques which is interested in automatically learning the treatments so take into account the old experiences. The link between BIGDATA & Machine learning exists today via digital analytics which is interested in taking advantage of these techniques to locate a specific resource or to make managerial decisions concerning the management of universities or to propose new courses answering to the needs of different interlocutors. The problem that persists, there is a lack of solution according to our research that is interested in doing pretreatment in an educational BIGDATA environment in order to extract the semantic metadata through machine learning algorithms. This metadata will be very useful later to generate the knowledge that will improve the teaching process. Our solution is used to answer this problem through various techniques of HADOOP for BIG DATA and MACHINE LEARNING. As a result, our application is largely valid according to the techniques that integrates that are confirmed in this field.