Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds

The traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. This paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the Data Mining Cloud Framework (DMCF). The way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. These innovative methodologies include Edge Computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. Thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms.