Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

Abstract

Effective storage structure is essential in distributed data processing systems as it determines the users' consistent, scalable and reliable
data accessibility. A flexible distributed database system's major aspects are data fragmentation and subsequent allocation. An efficient
data fragmentation technique must support both the horizontal and vertical categorization of the tuples so that the frequency and type
of queries for the data statistics are learned along with the data pattern and the attributes that impact these patterns. However, most
existing fragmentation methods are based only on the query frequency and type for the data statistics, leading to less effective analysis
of the data patterns and attributes in large databases. To avoid the data loss and imperfect grouping of large databases, this paper
presents a hybrid data fragmentation model using an advanced optimization-based clustering model for performing horizontal and
vertical fragmentation. This proposed model, called Genetic Killer Whale Optimization based Clustering (GKWOC), is developed by
integrating the Killer Whale Optimization (KWO) and Genetic Algorithms (GA) into the standard subspace clustering algorithm. This
optimization-based hybrid fragmentation model performs fragmentation in terms of the tuples and the attributes. Since the databases
can be high dimensional, subspace-based clustering can improvise good fragments for distributed databases and reduce the data access
time for the users. This proposed model showed better fragmentation performance than the state-of-the-art methods during the
evaluations, thus suitable for large distributed databases with competent access time and reduced error rates.