Abstract
An estimated 2.5 quintillion bytes of data are created every day. This data explosion, along with new datatypes, objects, and the wide usage of social media networks, with an estimated 3.8 billion users worldwide, make the exploitation and manipulation of data by relational databases, cumbersome and problematic. NoSQL databases introduce new capabilities aiming at improving the functionalities offered by traditional SQL DBMS. This paper elaborates on ongoing research regarding NoSQL, focusing on the background behind their development, their basic characteristics, their categorization and the noticeable increase in popularity. Functional advantages and data mining capabilities that come with the usage of graph databases are also presented. Common data mining tasks with graphs are presented, facilitating implementation, as well as efficiency. The aim is to highlight concepts necessary for incorporating data mining techniques and graph database functionalities, eventually proposing an analytical framework offering a plethora of domain specific analytics. For example, a virus outbreak analytics framework allowing health and government officials to make appropriate decisions.
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Rousidis, D., Koukaras, P., Tjortjis, C. (2021). Examination of NoSQL Transition and Data Mining Capabilities. In: Garoufallou, E., Ovalle-Perandones, MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_11
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DOI: https://doi.org/10.1007/978-3-030-71903-6_11
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