Characteristics Of Data Flow Research Articles

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Discussion on the New Trends of International Trade and Its Countermeasures in the Digital Era

With the advent of the digital economy era, international trade is undergoing profound changes. This study analyzes in detail the new trends and challenges of international trade in the era of digital economy, including the wide application of digital technology, the new characteristics of data flow, and the reconstruction of digital trade rules. On this basis, the corresponding countermeasures and suggestions are put forward, aiming to promote the sustainable development of international trade in the era of digital economy.

Ethical and Legal Implications of Data Sharing in SaaS Laboratory Management Systems

Over the years, the use of Software as a Service (SaaS) in a laboratory information management system has transformed sharing and management in the latter system. Such a transformation, however, brings along complex ethical and legal challenges for which scrutiny is supposed to be considered. Implication for adopting SaaS platforms includes fundamental concern about data privacy, security, and the overall integrity of scientific research. The paper systematically analyses ethical and legal implications associated with data sharing through SaaS platforms in the management of laboratories. This thus revolves around the understanding of how these systems can handle key aspects such as data ownership, respect for privacy, and compliance with international laws, and the resultant effects these would have on respective stakeholders across the scientific community. We, in this approach, have reviewed a wide range of literature, including fine details of case studies and views by experts, in light of current practices and challenges within SaaS-based laboratory management. We incorporate all these methods within the research to provide an integral view of the multidimensional ethical and legal landscaping, therefore delivering an approach with both depth and context to the analysis. It was mainly, regarding informed consent, and a very complex legal challenge emphasized in this study for compliance with GDPR, along with data confidentiality, respectively, for compliance with HIPAA. The study focused on the non-existence or lack of uniformity of regulatory frameworks that can provide for the special characteristics of SaaS data management and cross-border data flows.

Integrating multiscale data for digital twin-enhanced manufacturing systems: A conceptual and case demonstration

This paper explores the integration of data within digital twin-driven multiscale manufacturing systems, examining the core characteristics of data flows in digital twin (DT) environments tailored to manufacturing. In DT frameworks, data are inherently bidirectional, automated, and real-time, which are critical for maintaining the operational integrity of interconnected manufacturing subsystems. Different scales (from machine tool focus to supply chains) and timelines (from operations events to long term planning) make manufacturing system operation complex. This in turn means data integration across these scales and timelines is inherently complex. This study focuses on the dynamics of data flows in DT-driven manufacturing systems that support both forward and backward communication across DT modules and the central DT platform. The overall DT data scheme manages the supervision and cataloging of actions, which includes technical data related to machinery, processes, resources, and rules, alongside performance metrics across various scales and levels over time. A case study of a DT-driven manufacturing unit illustrates the data flow and interdependency in DT data framework. It highlights how data integration across multiple manufacturing stages can improve operational efficiency, fault diagnosis, and resource optimisation. This study lays the groundwork for the implementation of DT in complex, multiscale manufacturing systems by providing an extendible structured framework.

A Multi Model Approach: A Data Engineering Driven Pipeline Model for Detecting Anomaly in Sensor Data using Stacked LSTM

Abstract: This study uses a self-supervised learning technique based on auto encoders to find anomalous nodes. Only temporal variables have been taken into account and researched so far for use in identifying anomalies in wireless sensor networks (WSNs). This method fully utilises the geographic and temporal information of the WSN for anomaly identification by incorporating the extraction of geographic location features, intermodal WSN correlation features, and temporal WSN data flow characteristics into the design of the autoencoder. First, by focusing on a single mode from a local spatial perspective, a fully connected network is used to temporal nodes. Second, the spatial and temporal characteristics of the data flows of the nodes and their neighbours are retrieved by concentrating on a specific mode and seeing the WSN topology from a global spatial perspective for anomaly identification. The adaptive fusion method's weighted summation step is then used to extract the relevant features from the various models. An LSTM is used in this study to solve the problem of long-term dependence in the temporal dimension. The decoder's reconstructed output and the hidden layer representation of the are used to calculate the anomaly probability of the current system utilising a fully linked network.

ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows

Information-centric networking (ICN) is a promising solution that can meet the challenges of IP-based networks. ICN adopts the solution of separating name and address to solve the dual semantics problem of IP addresses, which has natural advantages in supporting mobility. These mobility support methods in ICN have realized route optimization to a certain extent. In fact, due to these methods not considering the real-time network environment, such as the bandwidth of the switching path and the characteristics of different mobile data flows, these approaches adopt the same routing optimization solution for all data flows generated by mobile users, resulting in a single switching path. The bandwidth of a single switching path may not meet the transmission requirements of mobile data flows, which will greatly affect the performance of mobile users. Therefore, how to efficiently allocate switching paths for multiple mobile data flows generated by mobile users is an urgent problem in ICN mobility support. In this paper, we first propose a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture and model the mobile data flow allocation problem as a late-binding node selection problem. We then propose a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. By simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT), we achieve late-binding node selection to minimize average packet delay. Finally, experimental results show that our proposal can achieve mobility support. Under the guarantee of a low-latency Name Resolution System (NRS), our method outperforms other methods in terms of average packet delay, packet loss rate, and handover delay.

Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-switched Inter-FPGA Networks

While FPGA accelerator boards and their respective high-level design tools are maturing, there is still a lack of multi-FPGA applications, libraries, and not least, benchmarks and reference implementations towards sustained HPC usage of these devices. As in the early days of GPUs in HPC, for workloads that can reasonably be decoupled into loosely coupled working sets, multi-accelerator support can be achieved by using standard communication interfaces like MPI on the host side. However, for performance and productivity, some applications can profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities here when extending the dataflow characteristics to their communication interfaces.In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA support and three missing benchmarks that particularly characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented for current boards with Intel and Xilinx FPGAs, we established a baseline for multi-FPGA performance. Additionally, for the communication-centric benchmarks, we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is currently only available for one of the boards. The evaluation with parallel execution on up to 26 FPGA boards makes use of one of the largest academic FPGA installations.

Multi-index evaluation learning-based computation offloading optimization for power internet of things

Cloud–edge-device collaborative computation offloading can provide flexible and real-time data processing services for massive resource-constrained devices in power internet of things (PIoT). However, the computation offloading optimization in PIoT still faces several challenges such as high computation offloading delay caused by uncertain information, coupling between task offloading and computation resource allocation, and degraded optimization performance due to the lack of multi-index consideration. To address the above challenges, we formulate a joint optimization problem of task offloading and computation resource allocation to minimize the average computation offloading delay. Specifically, a multi-index evaluation learning-based two-stage computation offloading (MINCO) algorithm is proposed to decouple the joint optimization problem into two-stage subproblems and solve them with evaluation and learning of multiple indexes including data flow characteristic, service priority, empirical average computation offloading delay, and empirical arm selection times. Simulation results show that compared with the baseline 1 and baseline 2 algorithms, MINCO improves the average computation offloading delay by 14.67% and 30.71%. Moreover, MINCO can evaluate different service priorities and data flow characteristics to meet different requirements of computation offloading delay.

Innovation, Technology, Knowledge Sharing in Agribusiness

International transfer of knowledge is a significant currency in IB. However, the ethical and legal issues that surround the grey area of innovation, technology, and knowledge sharing is seldom addressed in a systemic manner. Using Boisot’s Information Space model in knowledge management, literature review and semi-structured interview with field experts, we constructed a theoretical framework that captures the dynamics of data treatment (exploitation/exclusion), data value (high/ low) and data flow characteristics (undiffused/ highly diffused) of knowledge sharing in a global industry - Agribusiness. Four legal cases are employed to illustrate the respective quadrants’ characteristics. Building on this fundamental framework, further elements and characteristics in knowledge sharing are captured using professional imaginaries and selected dimensions of the technology acceptance model. The resultant framework is useful for management of innovation, data valuation, patenting strategy, knowledge sharing, and further research into technology adoption and knowledge sharing in both agribusiness and other global industries.

Study on the construction of smart tourism ecosystem and polycentric governance mechanism

<p>Building a smart tourism ecosystem is an important means for the tourism industry to strengthen resource sharing, realize value co-creation and promote healthy development of the industry. Based on the ecological governance perspective and the theory of smart tourism, the connotation of smart tourism ecosystem is proposed and the structural framework of smart tourism ecosystem is constructed by using semi-structured interview method. The framework of smart tourism ecosystem consists of smart tourism platform, smart government, smart tourism suppliers, smart tourism destination marketing organization, smart tourism consumers and external environment, defines the role of each element in the structural framework, and analyzes the characteristics of data flow between each element. The polycentric governance mechanism of the smart tourism ecosystem, including the ecological governance mechanism of smart government, smart tourism suppliers, smart tourism destination marketing organizations and smart tourism consumers, is proposed to provide a reference for the construction and development of regional smart tourism ecosystem.</p>

Real-time probing of control-flow and data-flow in event logs

Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Stream Framework (CDESF) tool to handle multiple attributes simultaneously. Our extension promotes CDESF to analyze both control-flow and data-flow characteristics in online event streams. Experiments used real and synthetic data for concept drift and anomaly detections. Results show that additional perspectives should be considered as they contain valuable information about processes.

Digital Transformation of a Production Line: Network Design, Online Data Collection and Energy Monitoring

The concept of Industry 4.0 originates from the will to introduce the benefits of digital computation into new and existing industrial plants to save time, materials and energy. The digital transformation requires that all machinery of the production line are connected together and with the enterprise applications, to capture and analyze data across all manufacturing stages. Then, such collected data can be exploited to take strategic decision on the production and to monitor it, reacting to unexpected behaviors and thus reducing downtime and maintenance costs. This article aims at supporting production engineers approaching digital transformation by exemplifying its key elements on a real life scenario, the Industrial Computer Engineering laboratory of the University of Verona. First of all, the article discusses network design, as communication is an enabler of the other technologies. Network is realized through automatic network synthesis from requirements and characteristics of the production line data flow. Then, the paper discusses data collection and the construction of a digital twin monitoring power consumption of the production line, with the goal of detecting any discrepancy between real time data and digital twin data. This allows to trigger an early intervention on the line, to guarantee an effective maintenance.

AI and machine learning for the analysis of data flow characteristics in industrial network communication security

AI and machine learning are revolutionary technologies being explored by the communication industry to integrate them into communication networks, provide modern services, improve network efficiency and user experience. The intrusion detection system is important for ensuring security of the industrial control system. Hence, in this paper, a machine learning assisted intrusion detection system (MLAIDS) has been proposed to analyse data flow characteristics in industrial network communication security. The progressive use of proposed ML algorithms will improve IDS functionality, especially in industrial control systems. Analysis of data flow characteristics given in this article involves the method of ensuring an adequate degree of security for a dispersed industrial network concerning some main elements, including system features, the present state of requirements, the implementation of suitable countermeasures that may lead to reducing the security risk under a predefined, acceptable threshold. The numerical results show that proposed MLAIDS method achieves high detection accuracy of 98.2%, a performance ratio of 97.5%, a prediction ratio of 96.7%, F1-score of 95.8%, and less root mean square error of 10.5% than other existing methods.

AI and machine learning for the analysis of data flow characteristics in industrial network communication security

AI and machine learning are revolutionary technologies being explored by the communication industry to integrate them into communication networks, provide modern services, improve network efficiency and user experience. The intrusion detection system is important for ensuring security of the industrial control system. Hence, in this paper, a machine learning assisted intrusion detection system (MLAIDS) has been proposed to analyse data flow characteristics in industrial network communication security. The progressive use of proposed ML algorithms will improve IDS functionality, especially in industrial control systems. Analysis of data flow characteristics given in this article involves the method of ensuring an adequate degree of security for a dispersed industrial network concerning some main elements, including system features, the present state of requirements, the implementation of suitable countermeasures that may lead to reducing the security risk under a predefined, acceptable threshold. The numerical results show that proposed MLAIDS method achieves high detection accuracy of 98.2%, a performance ratio of 97.5%, a prediction ratio of 96.7%, F1-score of 95.8%, and less root mean square error of 10.5% than other existing methods.

Intrusion Detection in Power Information Network Based on Deep Learning and Cloud Computing

With the electric power information network system has been popularized to all aspects of life, and its accompanying problems also come one after another, that is, its internal and external invasion ways are diversified, then the information security problems will affect the national econo-my, infrastructure and many other livelihood issues. On the one hand, the information security of power network needs the general characteristics of computer information security, while considering the characteristics of high security. According to the structure of power information network, this paper proposes an intrusion detection model to detect power information network, which uses both deep learning theory and cloud computing platform. This model not only uses parallel anomaly de-tection and misuse detection methods, but also can deal with the problem that a single misuse detec-tion model can't detect new attack variants. At the same time, it can capture a large number of data flows of power information network, learn and extract the essential characteristics of data flows through the deep feature learning and extraction ability of deep learning, and quickly and accurate-ly detect through the platform Invasion. Based on the analysis of all kinds of intrusion behaviors in power information network, this paper proposes a data extraction and analysis method based on Hadoop, which improves the accuracy of intrusion detection by analyzing massive captured data flow packets and generating characterization data. At the same time, considering the important factors affecting the detection of power information network intrusion feature selection, we study the auto-matic encoder algorithm using spark platform. According to the experimental results, it is found that this method can improve the feature selection of power intrusion detection system.

Assessing meander belt width of Bhagirathi-Jalangi river system in lower Ganga delta, India

The present work is an attempt to assess the meander belt width of the river Bhagirathi (frequently oscillating) and river Jalangi (relatively stable) in West Bengal, India based on geospatial data and field data (lithological composition and flow characteristics). Belt width has been computed using four major considerations in increasing order of scale- (i) individual loops of the present channel (MBw1), (ii) maximum loop width of the present channel (MBw2), (iii) palaeo channel characteristics identified through normalized difference water index and pixel-based classification coupled with field observation (MBw3) and (iv) future channel based on the factor of safety which is a space-time averaged safe belt width (MBw4). The study found MBw4 of 11.76 km for Bhagirathi and 6.64 km for Jalangi considering the 100-year factor of safety. The higher meander belt width of Bhagirathi in comparison to Jalangi was found to be correlated with higher monsoon discharge, bed slope, stream power, shear stress, suspended surface sediment concentration, D50, and lower relative bed stability.

MEDIA PEMBELAJARAN E- LEARNING PADA MATA PELAJARAN BAHASA INGGRIS UNTUK SISWA KELAS VII SMP ADVENT MANOKWARI

The learning process is a teaching-learning process carried out by students and teachers in educational units. It takes a determination from various parties to achieve togetherness and the same vision in creating integrated integration in learning objectives. The Process of Teaching and Learning Activities (KBM) at Advent Manokwari Junior High School teachers only use printed books and do not utilize learning media, so the learning process becomes boring and makes students bored and learning is also less interesting. The purpose of this research is to design and develop E-Learning Learning Media in English Subjects for Class VII Students in order to facilitate teachers and students in learning and teaching activities at Manokwari Adventist Middle School. The design is done by making Learning Media that makes it easy for teacher teaching in the classroom. The design of this process includes Use Case Diagrams, Activity Diagrams, Sequance Diagrams and Class Diagrams that can explain the data flow and system characteristics that are processed to produce the desired information. The research results show the success rate of English Learning Media made is in accordance with the design made and also the learning media as a means of learning that is more interesting and interactive because it is more bring students closer to ICT for learning English. and can function as a means of gathering and discussion for students.

Research on economic function data and entrepreneurship analysis based on machine learning and computer interaction platform

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education; integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data collection.At the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy.

Research on Intelligent Configuration Method of Mine IoT Communication Resources Based on Data Flow Behavior

In order to ensure production safety and improve production efficiency, mining enterprises are constantly accelerating the construction of mine Internet of Things systems. In the context of a substantial increase in the number of devices with network communication capabilities in the mine, the mine network communication facilities are under tremendous pressure. We propose a device business classifier based on convolutional neural networks to improve the service quality of mine network communication infrastructure. The classifier uses wavelet transform to extract the data flow and construct behavior characteristics to classify device business categories. According to the classification results, the system flexibly adjusts the parameters of network services provided to the terminal equipment. In this way, the network resources of the system can be allocated reasonably. We evaluate the performance of the classifier model through the test data set. The performance evaluation results show that the comprehensive recognition rate of the classifier model reaches 97.2%. We optimize and adjust the classifier model according to the hardware environment in which the classifier is actually deployed.

АНАЛІЗ МОДЕЛЕЙ ТА ПРОГНОЗУВАННЯ РИЗИКІВ ФУНКЦІОНУВАННЯ СИСТЕМИ УПРАВЛІННЯ SDN АРХІТЕКТУРИ

The article deals with the analysis of models and algorithms of the system control SDN of architecture for providing of more quality work of network. Great diversity of most hardware implementation of the data devices SDN architecture leads to the fact that different switches may not support some features or support them with limited performance. In the process network, this can significantly affect the throughput of an individual data flow or whole network domains. Routing flow transfer is carried out by criterion of quality of service and the criterion of balanced loads of network resources the network. Improving the quality of service is subject to the classification of the flow that greatly restricts the ability of flow control. Most models of such networks do not take into account the characteristics of multi-service data flow, which leads to deterioration in the quality of service and increase the likelihood of blocking the channels of transmission network. The inability to implement differentiated management for individual data streams for individual clients and take them into account quality requirements, leads to low efficiency of channel routing, sub-optimal distribution of network load, the deterioration of the quality of service high-priority data streams. Tools control over the process of transmission of individual data flow do not exist, whereby the control system has the ability to determine service degradation for these data streams, and therefore will not be able to guarantee the necessary level of control. New models and algorithms of functioning of a software – managed network will allow for routing data streams of network, differentiating them according to the sensitivity to mixing of packet and connection drops. At created networks try to dissociate C-plane from the plane of transmission and put considerable efforts to overcoming of the nascent problems related to complications of migration from traditional networks to architecture of SDN. The aim of this article is realization of analysis of models and algorithms of functioning of control system of architecture of SDN for more reliable work of network. It gives possibility to the users to get services with necessary quality, by authenticity and will allow overcoming the nascent problems related to complications of migration from traditional networks to architecture of SDN.