(untitled) (Journal of Pharmaceutical Negative Results, 2022)
Effective storage structure is essential in distributed data processing systems as it determines the users' consistent, scalable and reliabledata accessibility. A flexible distributed database system's major aspects are data fragmentation and subsequent allocation. An efficientdata fragmentation technique must support both the horizontal and vertical categorization of the tuples so that the frequency and typeof queries for the data statistics are learned along with the data pattern and the attributes that impact these patterns. However, mostexisting fragmentation methods are based only on the query frequency and type for the data statistics, leading to less effective analysisof the data patterns and attributes in large databases. To avoid the data loss and imperfect grouping of large databases, this paperpresents a hybrid data fragmentation model using an advanced optimization-based clustering model for performing horizontal andvertical fragmentation. This proposed model, called Genetic Killer Whale Optimization based Clustering (GKWOC), is developed byintegrating the Killer Whale Optimization (KWO) and Genetic Algorithms (GA) into the standard subspace clustering algorithm. Thisoptimization-based hybrid fragmentation model performs fragmentation in terms of the tuples and the attributes. Since the databasescan be high dimensional, subspace-based clustering can improvise good fragments for distributed databases and reduce the data accesstime for the users. This proposed model showed better fragmentation performance than the state-of-the-art methods during theevaluations, thus suitable for large distributed databases with competent access time and reduced error rates.