@book{scholars15036,
           pages = {123--143},
       publisher = {Springer International Publishing},
         journal = {Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics: Theories and Applications},
           title = {Indexing in big data mining and analytics},
            year = {2021},
             doi = {10.1007/978-3-030-66288-2{$_5$}},
            note = {cited By 1},
          author = {Abdullahi, A. U. and Ahmad, R. and Zakaria, N. M.},
            isbn = {9783030662882; 9783030662875},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135025983&doi=10.1007\%2f978-3-030-66288-2\%5f5&partnerID=40&md5=11a2dc5a1985001257fa0aaa7934d7f9},
        abstract = {Big data analytics is one of the best ways of extracting values and benefits from the hugely accumulated data. The rate at which the global data is accumulating and the rapid and continuous interconnecting of people and devices is overwhelming. This further poses additional challenge to finding even faster techniques of analyzing and mining the big data despite the emergence of specific big data tools. Indexing and indexing data structures have played an important role in providing faster and improved ways of achieving data processing, mining and retrieval in relational database management systems. In doing so, index has aided in data mining by taking less time to process and retrieve data. The indexing techniques and data structures have the potential of bringing the same benefits to big data analytics if properly integrated into the big data analytical platforms. A lot of researches have been conducted in that direction, and this paper attempts to bring forward how the indexing techniques have been used to benefit the big data mining and analytics. Hence, this can bring the impact that indexing has on RDBMS to the folds of big data mining and analytics. {\^A}{\copyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.}
}