{"title":"Bio-Inspired Advanced Warehouse System for Data Handling and Management","authors":"Sohit Reddy Kalluru, Prasanna Kumar Reddy Gurijala, Venkata Obula Reddy Puli, Lohit Reddy Kalluru","doi":"10.1002/adts.202400980","DOIUrl":null,"url":null,"abstract":"Big data analytics involves gathering data in a variety of forms and sources cleaning it up, customizing it, and then loading it into a data warehouse. Transformation algorithms are required for processing but this raises computation costs because it is stored across all locations in the data warehouse and has redundancy issues. Therefore, Extract, Transform, Load (ETL) is crucial to extract the data from various sources, transform it to meet analytical needs, and load it into a data warehouse. Hence, a novel Chimp-based K-means Tabu Warehouse System (CbKTWS) is proposed to handle large data in an ETL process based on cloud architecture. The key novelty of this work is managing the data resources in the data warehouse system with the help of chimp fitness. Moreover, the data is extracted using the chimp optimization's searching function and significant dimensional elimination of the information is executed by employing the habits created by a chimp optimizer during the transformation phase of the ETL process. Finally, the taboo search with the k-means technique is used to create efficient data with a variety of nodes. Ultimately, the effectiveness of the suggested approach is assessed and contrasted with the earlier approaches using metrics like error, data accuracy, and processing time.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"78 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/adts.202400980","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Big data analytics involves gathering data in a variety of forms and sources cleaning it up, customizing it, and then loading it into a data warehouse. Transformation algorithms are required for processing but this raises computation costs because it is stored across all locations in the data warehouse and has redundancy issues. Therefore, Extract, Transform, Load (ETL) is crucial to extract the data from various sources, transform it to meet analytical needs, and load it into a data warehouse. Hence, a novel Chimp-based K-means Tabu Warehouse System (CbKTWS) is proposed to handle large data in an ETL process based on cloud architecture. The key novelty of this work is managing the data resources in the data warehouse system with the help of chimp fitness. Moreover, the data is extracted using the chimp optimization's searching function and significant dimensional elimination of the information is executed by employing the habits created by a chimp optimizer during the transformation phase of the ETL process. Finally, the taboo search with the k-means technique is used to create efficient data with a variety of nodes. Ultimately, the effectiveness of the suggested approach is assessed and contrasted with the earlier approaches using metrics like error, data accuracy, and processing time.
期刊介绍:
Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including:
materials, chemistry, condensed matter physics
engineering, energy
life science, biology, medicine
atmospheric/environmental science, climate science
planetary science, astronomy, cosmology
method development, numerical methods, statistics