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Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [ABSTRACT FROM AUTHOR]

People have always tried to understand natural phenomena. In computer science natural phenomena are mostly used as a source of inspiration for solving various problems in distributed systems such as optimization, clustering, and data processing. In this paper we will give an overview of research in field of computer science where fireflies in nature are used as role models for time synchronization. We will compare two models of oscillators that explain firefly synchronization along with other phenomena of synchrony in nature (e.g., synchronization of pacemaker cells of the heart and synchronization of neuron networks of the circadian pacemaker). Afterwards, we will present Mirollo and Strogatz׳s pulse coupled oscillator model together with its limitations. As discussed by the authors of the model, this model lacks of explanation what happens when oscillators are nonidentical. It also does not support mobile and faulty oscillators. Finally, it does not take into consideration that in communication among oscillators there are communication delays. Since these limitations prevent Mirollo and Strogatz׳s model to be used in real-world environments (such as Machine-to-Machine systems), we will sum up related work in which scholars investigated how to modify the model in order for it to be applicable in distributed systems. However, one has to bear in mind that there are usually large differences between mathematical models in theory and their implementation in practice. Therefore, we give an overview of both mathematical models and mechanisms in distributed systems that were designed after them. [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]