Cutting-Edge and Advanced Data Structures for Addressing Complex Computational Challenges

Abstract

Data structures are the backbone of computational systems, enabling the efficient organization and manipulation of data. As the complexity of modern computational problems increases, there is a growing need for novel and advanced data structures that go beyond traditional methods. These new structures can improve performance in terms of time complexity, space utilization, and adaptability to evolving real-world applications. This review article explores the latest developments in advanced and cutting-edge data structures, focusing on their applications across a variety of domains such as big data, artificial intelligence, machine learning, real-time systems, and cloud computing. We delve into hybrid data structures, self-adjusting structures, geometric data structures, and more, emphasizing their role in solving complex computational problems that traditional data structures fail to address. Additionally, we discuss the trade-offs and challenges faced in their design and implementation, as well as the future directions in data structure research.