Efficient execution of data warehouse query using look ahead matching algorithm

Prakash, Kale Sarika; Prathap, P. M. Joe

doi:10.1109/icacdot.2016.7877613

Cited by 3 publications

(1 citation statement)

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“…The data is converted as of a higher-dimensional space into a lower-dimensional space in the dimensionality reduction; thus, the lower-dimensional data is calculated effortlessly [29]. The GK-FDA is utilized to mitigate the data's dimensionalities.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A Novel Approach to Design Distribution Preserving Framework for Big Data

Prince¹,

Prathap²

2023

Intelligent Automation &Amp; Soft Computing

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In several fields like financial dealing, industry, business, medicine, et cetera, Big Data (BD) has been utilized extensively, which is nothing but a collection of a huge amount of data. However, it is highly complicated along with time-consuming to process a massive amount of data. Thus, to design the Distribution Preserving Framework for BD, a novel methodology has been proposed utilizing Manhattan Distance (MD)-centered Partition Around Medoid (MD-PAM) along with Conjugate Gradient Artificial Neural Network (CG-ANN), which undergoes various steps to reduce the complications of BD. Firstly, the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function; subsequently, the missing data are handled by substituting or by ignoring the missed values. After that, the data are transmuted into a normalized form. Next, to enhance the classification performance, the data's dimensionalities are minimized by employing Gaussian Kernel (GK)-Fisher Discriminant Analysis (GK-FDA). Afterwards, the processed data is submitted to the partitioning phase after transmuting it into a structured format. In the partition phase, by utilizing the MD-PAM, the data are partitioned along with grouped into a cluster. Lastly, by employing CG-ANN, the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user. To analogize the outcomes of the CG-ANN with the prevailing methodologies, the NSL-KDD openly accessible datasets are utilized. The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN. The proposed work outperforms well in terms of accuracy, sensitivity and specificity than the existing systems.

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Section: Dimensionality Reductionmentioning

confidence: 99%