A Framework for Big Data Pre-processing and Search Optimization Using Hmgaaco: A Hierarchical Optimization Approach
Rama Satish K. V, Kavya N. P
Progress in Computing Applications, Vol. 7, No. 1, 2018.
DOI: 
Link: http://www.dline.info/pca/fulltext/v7n1/pcav7n1_2.pdf
Abstract:
The huge potential associated with big data has led to an emerging research field that has quickly attracted
tremendous interest from diverse sectors. Unlike traditional databases, optimized for fast access and summarization of
structured data and well defined queries, Big Data is believed to serve as a raw material for the creation of new knowledge.
We look at the complexity placed by big search spaces, dominated by the number of variables and domain of each variable,
in search and optimization problems. While a large, even infinite, search domain impairs the effectiveness and efficiency of
search, a complex structure of constraints further increases the difficulty in that the search space becomes highly irregular.
In order to overcome the above issues we propose a novel Hierarchical Manipulated Genetic Algorithm with Ant Colony
Optimization (HMGA-ACO) with data pre-processing which has the potential to optimize the data and retrieve the data with
more accuracy and precision. The proposed hierarchical optimization can help to boost the speed of search, and the effort of
search is reduced with the utilization of pre-processing.