BitmapAligner: Bit-Parallelism String Matching with MapReduce and Hadoop

Aksa, Mary; Rashid, Junaid; Nisar, Muhammad Wasif; Mahmood, Toqeer; Kwon, Hyuk-Yoon; Hussain, Amir

doi:10.32604/cmc.2021.016081

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…is is often considered a critical methodology in cloud-based systems, structures, and research. Hash-based homomorphic encipherment validates the confidentiality of users' sensitive information in the directive of determining the protection back issue of storage space or administering users' confidential information by unauthorized parties [14,15,[25][26][27][28][29][30]. With the affordances of WWW, cloud consumers are capable of utilizing cloud customer services just about every time and in any place.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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Topics such as computational sources and cloud-based transmission and security of big data have turned out to be a major new domain of exploration due to the exponential evolution of cloud-based data and grid facilities. Various categories of cloud services have been utilized more and more widely across a variety of fields like military, army systems, medical databases, and more, in order to manage data storage and resource calculations. Attribute-based encipherment (ABE) is one of the more efficient algorithms that leads to better consignment and safety of information located within such cloud-based storage amenities. Many outmoded ABE practices are useful for smaller datasets to produce fixed-size cryptograms with restricted computational properties, in which their characteristics are measured as evidence and stagnant standards used to generate the key, encipherment, and decipherment means alike. To surmount the existing problems with such limited methods, in this work, a dynamic nonlinear poly randomized quantum hash system is applied to enhance the safety of cloud-based information. In the proposed work, users’ attributes are guaranteed with the help of a dynamic nonlinear poly randomized equation to initialize the chaotic key, encipherment, and decipherment. In this standard, structured and unstructured big data from clinical datasets are utilized as inputs. Real-time simulated outcomes demonstrate that the stated standard has superior exactness, achieving over 90% accuracy with respect to bit change and over 95% accuracy with respect to dynamic key generation, encipherment time, and decipherment time compared to existing models from the field and literature. Experimental results are demonstrated that the proposed cloud security standard has a good efficiency in terms of key generation, encoding, and decoding process than the conventional methods in a cloud computing environment.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Dynamic Nonlinear Polynomial Integrity-Based QHCP-ABE Framework for Big Data Privacy and Security

Singamaneni

Juneja²,

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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“…Spark is a Resilient Distributed Dataset (RDD) computing model based on the traditional MapReduce computing framework, which is more suitable for handling distributed parallel computing and data reuse. Therefore, it significantly improves the operational efficiency of data reading, writing, analysis, and mining [4,5]. In meteorology data mining, common analysis methods include regression analysis, cluster analysis, classification analysis, and time series analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of CLARANS Algorithm for Weather Data Based on Spark

Zhang,

Wang

2023

Computers, Materials &Amp; Continua

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With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China's climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance, which improves clustering accuracy. Furthermore, the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion. Compared to the k-means clustering algorithm already implemented in the Spark platform, the proposed algorithm has strong robustness, can reduce the interference of outliers in the dataset on clustering results, and has higher parallel performance than the frequently used serial algorithms, thus improving the efficiency of big data analysis. This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China, and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center. This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities.

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