Applications of artificial intelligence in smart distributed processing and big data mining
Fazal Wahab, Anwar Ali Shah, Inam Ullah, Deepak Adhikari, Ijaz Muhammad Khan · 2024
The exponential expansion of data in the contemporary digital era has produced significant challenges and opportunities for various businesses, including distributed processing and data mining. “Big data” is a term used to describe datasets that contain enormous amounts of data that are challenging for standard databases to process. Currently, it comprises a significant amount of personal data that is generated and gathered from various sources, including a large amount of intricately organized, slightly organized, and unorganized data. With the aid of two revolutionary technologies, artificial intelligence (AI) and big data, organizations can now gather, store, and analyze enormous volumes of data. Exciting new opportunities in the search for insights and the use of data to support choices have been created by the merging of AI with big data. Particularly in the context of big data mining and distributed processing, AI plays a significant role in helping businesses get the most value out of their data assets. In some research fields, this chapter examines how AI influences data-driven innovation. With its numerous legitimate uses, data mining offers crucial tools for filtering through enormous datasets in search of insightful information. However, conventional data mining methods begin with the assumption that the data exists in a static, central location, such as a database or a computer’s memory. When working with a restricted set of resources, it might not be easy to organize and process massive datasets. Current data processing techniques and their associated software are inadequate for managing the exponential growth of data. Consequently, there is a pressing need to create effective big data mining strategies. The chapter sheds light on the challenges and possibilities offered by these technologies by addressing the fundamental principles of big data mining and distributed processing. Also discussed are the distributed processing frameworks’ roles in the administration of large datasets and the extraction of valuable intelligence. The chapter also explores the possible uses of big data mining and AI in several fields, focusing on the significance of efficient and scalable distributed processing techniques.