Katarzyna Pietrucha-Urbanik, Rzeszow University of Technology, Poland

In the contemporary realm of hybrid communication, the integration of various data resources presents an intriguing opportunity, allowing us to conceptualize these resources as distributed streams of information. However, a significant challenge emerges: how to quickly and accurately extract meaningful results from these original, often vast, data sets. One particularly effective solution to this problem is the continuously k-nearest neighbors (k-NN) query, which is a well-recognized approach in the field of data retrieval. This method operates by continuously identifying the k-nearest objects to a specified query point, enabling real-time data access that is essential for applications requiring immediate insights. The k-NN query is especially valuable in distributed systems where multiple data sources are involved, and rapid retrieval of relevant information is paramount. In this paper, we will thoroughly examine the nuances of this distance-sensitive estimation query, highlighting its practical applications and the underlying principles that drive its effectiveness. Additionally, we propose the development of a robust filtering model tailored to this query type. This model is designed to strategically track a secure distance threshold, ensuring that the continuous processing of data remains efficient and reliable while minimizing the risk of inaccuracies or irrelevant results. By addressing these critical aspects, we aim to enhance the overall effectiveness of distributed data retrieval systems in hybrid communication environments.

APA:
Pietrucha-Urbanik, K. (2021). Enhancing User Authentication in Cloud Computing with Knowledge Base Configuration. Journal of Science and Engineering Management, 2(3), 23-32. https://doi.org/10.33832/jsem.2021.2.3.03