Ashish Kumar Mourya

The paper explores the intricate relationship between Master Data Management (MDM) and Expert Systems (ES) and their combined impact on modern enterprises. It elucidates MDM’s role in consolidating and harmonizing data across disparate sources to establish a unified and authoritative data foundation. Additionally, it delves into the functionalities of ES, emphasizing their capacity to replicate human expertise and domain-specific knowledge for intelligent decision-making. Highlighting their synergy, the research demonstrates how the fusion of MDM and ES optimizes data governance, enhances data quality, and facilitates access to invaluable insights from organizational data repositories. Through real-world case studies, it illustrates how this integration empowers businesses to streamline operations, innovate, and make informed decisions, ultimately positioning this amalgamation as a strategic driver for digital transformation and heightened competitive advantage within today’s data-driven landscape.

Journal of Clinical and Diagnostic Research aims to publish findings of doctors at grass root level and post graduate students, so that all unique medical experiences are recorded in literature.

Big data technology has gained attention in all fields, particularly with regard to research and financial institutions. This technology has changed the world tremendously. Researchers and data scientists are currently working on its applicability in different domains such as health care, medicine, and the stock market, among others. The data being generated at an unexpected pace from multiple sources like social media, health care contexts, and Internet of things have given rise to big data. Management and processing of big data represent a challenge for researchers and data scientists, as there is heterogeneity and ambiguity. Heterogeneity is considered to be an important characteristic of big data. The analysis of heterogeneous data is a very complex task as it involves the compilation, storage, and processing of varied data based on diverse patterns and rules. The proposed research has focused on the heterogeneity problem in big data. This research introduces the hybrid support vector machine (H-SVM) classifier, which uses the support vector machine as a base. In the proposed algorithm, the heterogeneous Euclidean overlap metric (HEOM) and Euclidean distance are introduced to form clusters and classify the data on the basis of ordinal and nominal values. The performance of the proposed learning classifier is compared with linear SVM, random forest, and k-nearest neighbor. The proposed algorithm attained the highest accuracy as compared to other classifiers.

Journal of Clinical and Diagnostic Research aims to publish findings of doctors at grass root level and post graduate students, so that all unique medical experiences are recorded in literature.