Using distributed machine learning algorithms on big data analysis

Using Distributed Machine Learning Algorithms on Big Data Analysis The increase in the variety, volume and the velocity of the data produced by different sources caused new technologies and approaches to be born. It is not possible for the big data to be stored and processed by standard computers. We need high computational powers to study and analyze big data. For these reasons computer clusters and distributed file systems instead of traditional computing approaches and frameworks such as Hadoop and Spark instead of traditional programs and programming languages are becoming more common. These technologies which are used to store and process data have also affected the machine learning approaches used for data analysis. Machine learning algorithms are used for many areas such as sentiment analysis, accuracy detection, recommendation systems, social network discovery, discovery and classification of web content etc. In this thesis big data and cloud computing technologies, running parallel algorithms on big data and the application of distributed machine learning algorithms on big data are investigated. Keywords: Big data, distributed machine learning