Abstract
Several solutions have been proposed over the past few years on data storage, data management as well as data retrieval systems. These solutions can process massive amount of data stored in relational or distributed database management systems. In addition, decision making analytics and predictive computational statistics are some of the most common and well studied fields in computer science. In this paper, we demonstrate the implementation of machine learning algorithms over an open-source distributed database management system that can run in parallel on a cluster. In order to accomplish that, a system architecture scheme (e.g. Apache Spark) over Apache Cassandra is proposed. This paper also presents a survey of the most common machine learning algorithms and the results of the experiments performed over a Point-Of-Sales (POS) data set.
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Sioutas, S. et al. (2017). Survey of Machine Learning Algorithms on Spark Over DHT-based Structures. In: Sellis, T., Oikonomou, K. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2016. Lecture Notes in Computer Science(), vol 10230. Springer, Cham. https://doi.org/10.1007/978-3-319-57045-7_9
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