Integrated benchmarking standard and decision support system for structured, semi structured, unstructured retail data

Abstract

Technological development in the organization made society entirely dependent on the internet for their needs, leading to further growth in the variety of data. Initially, the prominent organization faces difficulties due to the exponential development of data. The benchmarking is required to allow data mining tools such as Rapid Miner and Big Data in the environment. Structured, unstructured, and semi-structured data are the types of Big Data. Structured data are used to develop a page by giving enough information. Unstructured data will not have a predefined model to arrange the data in a particular way. Semi-structured data has some structure, but it will not have any data model. The extensive data system is the more apt method in evaluating the benchmark. The proper research and analysis must be carried out to provide a standardized benchmark. The organization has to develop a clear-cut idea about the strategy to overcome the consequence effectively. The benchmark is usually classified into two types internal and external benchmarking. Internal benchmarking includes SWOT, performance metrics, functional values, a practice carried out, financial information, and external benchmarking, including collaborative benchmarking of process, product, corporate, strategic, and global. The primary purpose of the internal benchmark is to compare the performance of an organization internally. The external benchmark gives information about the organization's performance with its competitor companies in this industrial world. The primary big bench model for the product retailer includes a synthetic data maker with variety, speed, and extensive data systems, which contains structured and unstructured data. The significant bench workload is performed with a set of queries. The queries occupy various departments of comprehensive data analysis based on a business perspective. Data sources, processing types of queries, and analytic techniques are included in designing the queries from a technical perspective. Transaction Processing Performance Council decision support (TPC-DS) is adapted to develop a standard benchmark for a big bench data model.

Human Bone Marrow-Derived Mesenchymal Stem Cell Applications in Neurodegenerative Disease Treatment and Integrated Omics Analysis for Successful Stem Cell Therapy

“…Nevertheless, most omics analyses have been limited to a single dataset, which is accompanied by difficulty in reflecting the actual phenotype [60]. In addition, the datasets used for computational analysis have advanced from structured one to big data with various unstructured and semi-structured characteristics, and the relationship between omics data is expected to become more complex [61]. Artificial intelligence (AI) is increasingly essential in big data mining, including the biological and medical fields [62,63].…”