Treffer: A systematic review of recommender systems for realtime data streams.

Inestimable data is generated in abundance from the Internet and various online sources of information. Real-time analysis of this big data and the gathering of valuable insights using various mining application platforms is also an area of research and industry. Nevertheless, data stream analysis faces other problems that distinguish it from traditional data analysis. This has complicated existing data mining tools and methods due to the internal dynamic properties of big data, directly in large data streams. Recently, several studies have solved existing problems associated with large volumes of data, and proposed several methods that give impressive results. However, we found that no significant research has been devoted to analysing a large data stream in real time. Much attention was paid to the preliminary processing stage of large data streams with limited tools and technologies to transmit big data which can perform all batch, transmission, and repetitive tasks. In this paper, the issues like scalability, privacy and load balancing as well as empirical analysis of recommender systems is analysed for data streams and technologies that are open for further research efforts. Also the data stream mining characteristics, the challenges and research issues was analysed. Finally, various platforms for data stream mining is listed with requirements and solutions. [ABSTRACT FROM AUTHOR]

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