Hybridized fragmentation of very large databases using clustering

Abstract

Due to the ever growing needs of managing huge volume of data, together with the desire for consistent, scalable, reliable and efficient retrieval of information, an intelligent mechanism to design the storage structure for distributing the databases has become inevitable. The two critical facets of distributed databases are data fragmentation and allocation. Existing fragmentation techniques are based on the frequency and type of the queries as well as the statistics of the empirical data. However, very limited work is done to fragment the data based on the pattern of the tuples and the attributes responsible for such patterns. This paper presents a unique approach towards hybridized fragmentation, by applying subspace clustering algorithm, to come up with a set of fragments which partitions the data with respect to tuples as well as attributes. Projected clustering is the one that determines the clusters in the subspaces of high dimensional data. This concept leads to find the closely correlated attributes for different sets of instances thereby giving good hybridized fragments for distributed databases. Experimental results show that fragmenting the database based on clustering, results in reduced database access time as compared to the fragments chosen at design time using certain statistics.

Fragmentation Techniques for Ideal Performance in Distributed Database – A Survey

“…The relative table is separated into arbitrary sections, facilitating the queries ' use of the attribute and tuple use. One specific site may be allocated to each fragmentation (Harikumar and Ramachandran, 2015).…”

“…A mixed fragmentation of clustered, huge databases was proposed in (Harikumar and Ramachandran, 2015). This research provides an exclusive method of hybridized fragmentation thru implementing the subspace clustering algorithm to create a set of fragments that partition the data apart from tuple attributes.…”

“…(2015) and Lwin and Naing (2018). The fragmentation techniques can also be applied based on the clustering approach as in Harikumar and Ramachandran (2015), Rahimi et. al.…”

An optimized approach for simultaneous horizontal data fragmentation and allocation in Distributed Database Systems (DDBSs)

“…On the other hand, a hybridized fragmentation was proposed in ( Harikumar and Ramachandran, 2015 ) to reduce database access time based on subspace clustering algorithm. Data fragments were generated with respect to tuple and attribute patterns that the closely correlated data were assembled together.…”

Real time Face Detection and Optimal Face Mapping for Online Classes

“…For example, in face detection methods, a human face is detected from an image including various objects such as cars, buildings, and so on. Face Recognition can be done using supervised and unsupervised technique 6 . The first and the essential step for any face recognition system is face detection.…”