Research Information System
Balkan Journal of Electrical and Computer Engineering, vol.10, no.4, pp.388-396, 2022 (Peer-Reviewed Journal)
Big data is defined as data sets that are too large and/or complex to be
processed by classical data processing methods. Big data analysis is
essential because it enables more competent business movements, more
efficient operations, and higher profits by using the data of
institutions and organizations. However, large datasets are difficult to
analyze because they are produced quickly, require large storage areas
in computer systems, and the diversity of their data. In this study, a
new approach using the denormalization method is proposed to accelerate
the response time of the database in database systems where large
volumes of data containing historical information are stored.
Denormalization is defined as the process of adding rows or columns that
are not needed to increase the reading performance of the database to
the database system that has been normalized. In the proposed approach
in this study, a large-volume dataset consisting of real spatial data
belonging to Kayseri Metropolitan Municipality, containing temporal
information and having approximately 96,000,000 row records, was used.
In the proposed approach, the response time of the query is accelerated
by recording the time information as numbers to increase the query
performance of large volumes of data recorded in date format due to the
temporal query process. The performance of the proposed method is
compared with the performance of the normalization method using actual
data on Microsoft SQL Server and Oracle database systems. The method
proposed in the experimental evaluations shows that it works
approximately eight times faster. In addition, the experimental results
showed that the proposed method improves query performance more than the
normalization-based method as the data size increases.