Big Data Processing and Analytics Inside DBMS

           Mikhail Zymbler(B)[0000−0001−7491−8656] , Sachin Kumar,
           Yana Kraeva, Alexander Grents, and Anastasiya Perkova

                South Ural State University, Chelyabinsk, Russia
       mzym@susu.ru, sachinagnihotri16@gmail.com, kraevaya@susu.ru,
                    grentsav@susu.ru, perkovaai@susu.ru

    In the era of Big Data, there are two basic challenges for humans: how to
effectively manipulate and analyze huge amounts of data. Currently, relational
DBMSs remain the most popular tool for processing large tables in various data
intensive domains, despite the widespread use of numerous NoSQL systems. At
the same time, most of modern tools for mining the large data sets are non-
DBMS and based on the MapReduce paradigm. If we consider DBMS only as
a fast and reliable data repository, we get significant overhead for export large
data volumes outside a DBMS, changing data format, and import results of
analysis back into DBMS. That is why integration of data mining methods with
relational DBMS is a topical issue.
    There exist parallel DBMSs that can efficiently process transactions and SQL
queries on very large databases. Such DBMSs could be a subject for integration
of data mining methods but they are expensive and oriented to custom hardware
that is difficult to expand. Open-source DBMSs are now being a reliable alter-
native to commercial DBMSs but there is a lack of open-source parallel DBMSs
since the development of such software is rather expensive and takes a lot of
time.
    In the talk, we will consider an approach to deal with the problems described
above. A parallel DBMS can be developed not from scratch but by small-scale
modifications of the original codes of an open-source serial DBMS to encapsulate
parallelism. Large- and small-scale data mining problems can be solved inside
such a parallel DBMS.


Acknowledgments
This work was financially supported by the Russian Foundation for Basic Re-
search (grant No. 17-07-00463) and by the Ministry of Science and Higher
Education of the Russian Federation (government orders 2.7905.2017/8.9 and
14.578.21.0265).




 Copyright © 2019 for this paper by its authors. Use permitted under Creative
 Commons License Attribution 4.0 International (CC BY 4.0).




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