Nowadays, new information technologies and management are indexed. Although in recent years, the companies had significant development, they still in a very competitive environment and facing different challenges. The companies are searching more efficient ways to manage their networks. And network digitalization is considered an innovative way to solve those issues. As consequence, there are many research papers related to this topic. This paper conducts a literature review from a novel perspective, using an innovative approach, based on a created framework of analysis, to identify the classification of digitalization. Additionally, we conducted an in-depth analysis of the application of different digital technologies in enterprise management. The proposed classification is used to highlight the current research stream and provide profound insights for future research.
This research recommends a taxonomy for categorizing the sensitivity of data produced and processed from smart buildings. This taxonomy is built through a methodological approach that includes target reviews on specific smart building data papers, analysis of ICO's official documents and interviews with data experts. The classification of sensitive information in smart building operations necessitates this taxonomy. It seeks to properly manage access to data, as well as ensure compliance with emerging privacy laws and operational needs, while enhancing security and efficiency in smart building environments.
The rapid advancement of Internet of Things (IoT) technologies presents significant opportunities to enhance agriculture and aquaculture practices. This paper proposed a software architecture designed based on microservices to address the complex and evolving requirements of contemporary agriculture and aquaculture systems locations. These systems demand a versatile platform capable of integrating a wide range of functionalities, including environmental monitoring, aquaculture management, and comprehensive record-keeping for crops and aquatic life. The proposed IoT system employs a microservice architecture that ensures modularity, scalability, and flexibility. This approach facilitates the seamless integration of new services and features, easy adaptation to existing systems, and efficient reuse of established components. The microservice-based architecture has been successfully implemented in the TC2 project at Can Tho University under the Japan International Cooperation Agency (JICA) initiative. The project is in remote areas through the deployment of a network of sensor and actuator nodes, supported by a robust backend and user-friendly Front End applications. This implementation demonstrated notable improvements in operational efficiency and resource management. By leveraging edge computing for real-time data processing and integrating cloud-based analytics, the system achieved reduced response times and minimized data transmission costs. The modularity of the microservices architecture also enabled easy scalability and adaptability to change environmental conditions. The success of this implementation highlights the potential of microservices-based IoT solutions to drive innovation and enhance practices in agriculture and aquaculture, laying a strong foundation for future advancements in these fields.
The paper presents a process mining based analysis model for document workflows. The model aims to improve document management processes by utilizing process discovery and visual analysis techniques, and flexible multilevel user interface. It focuses on creating visual models and tools to identify deviations in document workflows and uses Directly Follows Graphs (DFG) for visual modelling. The model is configurable for efficient use in organizations and companies of various velocities of document flow and varieties of workflow traces. Organizations can define watched document families and watched metrics both organization-wide and on user level. Workflow traces are split into several categories and two levels of detail are used to help reduce complexity of data visualizations.
The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects—commonly known as skylines—based on their preferences. The identification of skyline objects becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams. In this paper, we proposed the Dominance Analyses Reduction (DAR) framework, which aims at addressing the issues of redundant dominance analyses that arise while determining skylines over data stream. Dominance analyses are repeated for objects that are in the overlapped frames of two windows and for pairs of objects that later reappear in the stream. DAR employs the Apriori algorithm, one of the most prevalent data mining algorithms, to identify the frequently occurring dominance analyses. Instead of conducting the dominance analyses again, their results are stored and utilised in the subsequent derivation of skylines. The DAR framework has been validated through several experiments. Its results exhibit significant reduction in the number of pairwise comparisons at both object and dimension levels and execution time.
