Bio-Inspired Advanced Warehouse System for Data Handling and Management

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.