Real-time Data Flow Research Articles

Capture of real-time data from electronic health records: scenarios and solutions.

The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated. Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues. The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare. Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.

Design of an Automatic Monitoring Model for Real-Time Data Flow in Network Based on Fuzzy Clustering Analysis for Health Analysis

Abstract The current automatic monitoring model of real-time data flow in network has poor data clustering ability, which leads to a poor data flow clustering effect and a high memory occupation rate when the model is applied. To solve this problem, based on the data processing point of view, this paper designs an automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis. The threshold sampling method is used to complete the dynamic sampling of real-time data in network. The Principal Component Analysis (PCA) method sorts out the eigenvectors in the data flow and solves them. According to the collected data flow samples, fuzzy clustering analysis algorithm combined with feature vector is used to realize the automatic monitoring of real-time data flow in network. So far, the design of automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis is completed. The experimental results show that the fuzzy clustering model is better than the current model.

Real-Time Data Pipelines for Enhancing Property Management Systems: Integrating AI Bots for Automated Scheduling

In an industry where operations are constantly evolving, real-time data flow has become increasingly crucial for streamlining processes and informed decision making at all levels of property management. Traditional systems often struggled with information bottlenecks, dispersed responsibilities, and tenant contacts left disconnected, resulting in redundancies and unsatisfactory experiences. This paper explores integrating real-time data pipelines within property management platforms to optimize workflow smoothly, concentrating particularly on incorporating AI-guided software for programmed scheduling. By implementing live data streams, managers can achieve consistent information transmission from sources such as smart devices, resident communications, and financial applications, enabling instant insights and proactive handling of issues. The AI application automates appointment planning, diminishing manual labor hours, minimizing errors, and enhancing interactions with tenants. The proposed framework not only refines the scheduling process but also heightens overall effectiveness in property administration by supplying a scalable and adaptive solution that develops alongside expanding needs. This research highlights the transformative potential of real-time data pipelines and AI-driven automation in modernizing property management systems to run efficiently

Exploring Attractive Quality Requirements for Short Food Supply Chain Digital Platforms

Perceptions towards unsustainable supply chain practices in global, mainstream food systems are motivating a shift towards short food supply chains. Short food supply chains are developed to remove the physical and social distances between producer and consumer. Advances in digital technologies offer promise for short food supply chains, including platforms that can enable real-time data flow, create visibility, and support sustainable practices. This research aims to prioritise attractive quality requirements of short food supply chain digital platforms. The methodology consists of a literature review and Kano analysis for requirements prioritisation. The results show that the requirements span across the four Kano categories, attractive quality encompassing the largest number of requirements. The attractive quality requirements identified offer increased levels of satisfaction when present and have limited negative impact when missing. Therefore, they are considered exciting for potential users of a system. The limitations of the research and areas of future work are presented.

Hyperledger for IoT: A Review of Reconstruction Diagrams Perspective

The convergence of blockchain with the internet of things (IoT) attracted widespread attention. Blockchain mainly solved the problem of secure storage and trusted transactions. The convergence of these two emerging technologies enhanced the security of smart services. However, there were some technical barriers to the deployment of practical IoT systems. In order to further promote the popularity and application of blockchain in the IoT, Hyperledger became the ideal technology to overcome these obstacles. In recent years, the mainstream application fields of IoT tried to carry out integration with Hyperledger to achieve high security, fine-grained privacy protection, real-time data flow, robustness, and other business requirements. However, there was a lack of literature review on this topic. This study obtained the latest related literature of Hyperledger in IoT from Web of Science, Wordlib, and EBSCO databases. To demonstrate more intuitive differences and provide a technology convergence process, this study proposes a reconstruction diagram analysis method. Reconstruction is the process of fusing the core design and the original architecture diagram in the literature and reconstructing the diagram so that it can show the core ideas of the literature. This approach aims to visualize the core ideas of the literature. Finally, this paper prospected and concluded by proposing four directions for future work, including a low-energy consensus algorithm, intelligent transaction validation, mixed on-chain and off-chain storage, and customized incentives.

Computer Architectures for Incremental Learning in Water Management

This paper presents an architecture and a platform for processing of water management data in real time. Stakeholders in the domain are faced with the challenge of handling large amounts of incoming sensor data from heterogeneous sources after the digitalization efforts within the sector. Our water management analytical platform (WMAP) is built upon the needs of domain experts (it provides capabilities for offline analysis) and is designed to solve real-world problems (it provides real-time data flow solutions and data-driven predictive analytics) for smart water management. WMAP is expected to contribute significantly to the water management domain, which has not yet acquired the competences to implement extensive data analysis and modeling capabilities in real-world scenarios. The proposed architecture extends existing big data architectures and presents an efficient way of dealing with data-driven modeling in the water management domain. The main improvement is in the speed (online analytics) layer of the architecture, where we introduce heterogeneous data fusion in a set of data streams that provide real-time data-driven modeling and prediction services. Using the proposed architecture, the results illustrate that models built with datasets with richer contextual information and multiple data sources are more accurate and thus more useful.

A Learning-Based Data Placement Framework for Low Latency in Data Center Networks

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

Implementing Vertical Integration in the Industry 4.0 Journey: Which Factors Influence the Process of Information Systems Adoption?

One of the key principles of Industry 4.0 is the implementation of vertical integration, which considers the integration of information systems from different hierarchical levels in a company to support decision-making with real-time data flows. Companies face challenges when they want to implement vertical integration, which is not trivial due to the risks inherent to the decision stages of adoption. We investigate the main factors influencing the different stages of adoption of vertical integration to provide a clearer view of what managers should consider at each stage. We adopt a multi-case study approach based on the investigation of ten companies that followed this adoption process. We develop a framework with 22 factors deployed in the three stages of decision (knowledge, persuasion, and final decision) and three main dimensions of analysis: technology, organization, and environment. We analyze the potential tensions between these factors and show how managers should balance such factors during the decision stages.

Efficient rate and power allocation in wireless sensor networks with big data constraint

Wireless sensor networks(WSNs) have attracted a lot of interest and been applied to various fields because of it’s Low cost and low power character. The applications position sensor nodes and collect their data for a long period with real-time data flow, which is considered as big data. Big data may be structured or unstructured, and then it needs to be stored for further processing and analyzing. But with the increasing of the environment and the lack of the spectrum, the efficiency of the network has become very difficult to improve. The resource allocation problem with big data model in WSNs is investigated in this paper, where the objective is to maximize the received rate and minimize the consumed power with big data. The rate constraint is time-varying, which is more suitable for the real sensor networks. The projection gradient method is used to solve the NP-complete problem, which leads the proposed algorithm easy to implement. The convergence analysis is also given in this paper. Numerical examples show that our algorithm can guarantee fast convergence within a few iterations and outperform the existing approaches in terms of throughput and energy efficiency.

Advances in Big Data Applications for transportation: airline, highway, and railway

Aim: The purpose of this article is to present the latest advances in big data applications in the industries of the transportation sector such as airline, highway, and railway. It is difficult to analyze data in transportation because there is continuous real-time data flow. Since the improvements made are fast with the same logic, it is necessary to catch up with the new developments. Data should be analyzed with the big data concept because data stacks highly contain non-structural data types in transportation data. Although the mentioned industries are complementary to each other, the applications differ depending on the needs of the industry. Thus, solutions to specific problems in different industries using big data applications should be addressed.&#x0D; Design / Research methods: In accordance with the purpose of the study, big data studies that provide added value to the transportation sector were examined. Studies have been filtered through some criteria which are whether the application is adaptable to the industry, the study is available online in full-text, and its references are from respectable sources.&#x0D; &#x0D; Conclusions / findings: All the big data application studies in the academy are not adaptable in real-life problems or suitable for all situations. For this reason, trying all of the applications will lead to moral and material losses for firms. This study is a guideline for companies to follow the developments in the big data concept and to choose the one that suits their problems. Thus, the gap between academia and industry was tried to close.&#x0D; Originality / value of the article: Although studies are referring to big data applications in the transportation sector, this study differs from others in terms of specifically analyzing big data applications in different industries such as airline, highway, and railway in the transportation sector

A New Compact Metal Mountable Dual-Band UHF RFID Tag Antenna With an Adapted Middleware for Transport and SCM Fields

Today, RFID, "Radio Frequency Identification", is a key technology for tracking assets in real-time. It has become one of the areas for improvement in several applications. An RFID system consists of three parts: RFID Tags, RFID Readers, and RFID Middleware. This study aims to improve the advantages of RFID applications as an applied technology in several fields, first, by modeling and designing a metal mountable tag antenna that is applied to cover two major UHF RFID bands, i.e., European (865-867 MHz) and U.S. bands (902-928 MHz), secondly, by setting out the RFID Middleware main tasks and suggesting a new RBAC &amp; NoSQL-Based RFID Middleware architecture as a solution to manage the accessibility to RFID data, thus rendering the authentication process much more efficient. The proposed tag antenna configuration utilizes microstrip configuration with open bent stub feed network in order to attain conjugate matching w.r.t. Monza R6 chip impedance. The proposed microstrip patch-based tag antenna structure is simple and does not require any shorting pin/holes, making it easy and inexpensive to manufacture. Additionally, the proposed tag's impedance can be easily tuned in order to achieve conjugate matching concerning employed chip impedance. The proposed tag antenna is fabricated and experimentally characterized to measure its read range performance in the desired bands. The Proposed Middleware Platform architecture combines RFID technology with MongoDB to monitor and process a large number of inbound RFID events generated by RFID tags in order to provide high volume real-time data flow processing and better interoperability through the data presented in JSON format.

Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things

Software Defined Networking (SDN) provides a new perspective for the Internet of Things (IoT), since, with the separation of the control from the data planes, it is viable to optimise the traditional networks operation management. In particular, the SDN Controller has a global vision of the network of sensors/actuators domain, allowing real-time network nodes and data flows reconfiguration. As a consequence, devices, usually facing limited communications and computing resources, are relieved of the route selection task in a distributed and, thus, suboptimal way. This paper proposes a SDN-IoT architecture, specifically focusing on the Controller design, which dynamically optimises in real time the end-to-end flows delivery. In particular, the dynamic routing policy adaptation is based on the real-time estimation of the network status and it allows jointly minimising the end-to-end latency and energy consumption and, consequently, to improve the network life time. The performance of the proposed approach is analysed in terms of the average latency, energy consumption and overhead, pointing out a better behaviour in comparison with the existing distributed approaches.

Governance by Data

Law and social science scholars have long elucidated ways of governing built around state governance of populations and subjects. Yet many are now grappling with the growing prevalence of practices of governance that depart, to varying degrees, from received models. The profusion of digital data, and the deployment of machine learning in its analysis, are redirecting states’ and international organizations’ attention away from the governance of populations as such and toward the amassing, analysis, and mobilization of hybrid data repositories and real-time data flows for governance. Much of this work does not depend on state data sources or on conventional statistical models. The subjectivities nurtured by these techniques of governance are frequently not those of choosing individuals. Digital objects and mediators are increasingly prevalent at all scales. This article surveys how scholars are beginning to understand the nascent political technologies associated with this shift toward governance by data.

Analysis and Design of Distribution Systems Information Flow in the Sugar Supply Chain in Indonesia

Today's modern supply chain represents a complex and real-time, organization, resource, activity, information, and data source that is involved in the distribution of products and services ranging from upstream to downstream of the supply chain. In the past 4.0 supply chain technology was not just a linear business function, but as the center of the main process of ecosystems that are in a blind spot chained by value. With information as a foundation in the decision-making process so that information can create integrated and efficiently coordinated supply chains. So that it can show continuity from planning to production, inventory, quality, and price control in each chain. An inefficient distribution that results in mistrust among stakeholders, because it has an impact on the decline and loss of value chain in quality and quantity. Integrity problems from the data collected were found in this study. These findings include the identification of various stakeholders, including farmers, importers to customers, and regulators, as well as their needs, which will be described through the use case, and BPMN. The results obtained are that the main actors (stakeholders) of the system are divided into farmers, importers, processing factories, headquarters, hauling services, and markets (customers) in the distribution of product information flow systems. Suggests tracking and tracing based on real-time data flow of product information coming from each actor in the sugar supply chain that is equipped with an accurate data distribution information support system.

Smart city infrastructure protection: real-time threat detection employing online reservoir computing architecture

The most important problems that occur during the extraction of knowledge from data streams are related to the properties characterizing “BigData,” namely high speed of information flow (velocity), variety of used forms, variability of data and diversity of information accuracy diagnosis methods (veracity). The use of online or sequential learning methods offers a specialized solution for solving real-time data processing problems. Data are provided without a clear knowledge of their particular inherent characteristics. Conventional approaches focus on applying heuristic or logical analysis rules. They fail to effectively handle new patterns (produced as a function of time) and to consider the dynamic change rate of their characteristics. In most cases, these methods approximate, by creating general rather than clear imprints of knowledge, which is hidden in the flows. Moreover, their function requires significant computational resources. This paper introduces (to the best of our knowledge, for the first time in the literature) the implementation of a specialized online reservoir computing architecture for smart city infrastructure protection which has low requirements in computing resources; it is efficient and suitable for real-time data flow analysis. More specifically, it describes the development of an echo state network, comprised of analog neurons with sparse random connections at the input levels and at the dynamical reservoir. Its training at the output level is performed with the recursive least square method. A complex data set was selected for the testing of the proposed model, which fully simulates the digital attacks that can be faced by the mechatronic equipment used in smart water supply networks located in the front end of the smart cities.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Hierarchical diagnostic model synthesis for dataflow real-time computing system

Subject of Research. The paper considers design issues for diagnostic tools of fault detection in addressing information exchanges between software modules for real-time dataflow computing systems. Despite the decomposition of the design processes in such systems, the issues of diagnostics and fault tolerance remain relevant for each hierarchy level. Method. The proposed synthesis procedures for the hierarchical model of a dataflow computing system are the result of the test diagnostics method development based on the parallel model application. Main Results. The paper presents a brief description of the test diagnostics method based on the parallel model. An algorithm for hierarchical diagnostics model synthesis is developed. The model minimizes the amount of diagnostic data transmitted through the exchange channels, reducing the redundancy level introduced into the system and thereby increasing the level of reliability. Practical Relevance. The developed hierarchical model reduces significantly the design time for diagnostic tools as a result of reducing the required number of diagnostic modules included in it.

Research on Attack Identification Method and Device Method Based on Random Forest Algorithm

Because the energy storage system has the function of stabilizing the voltage and frequency of the grid, when the energy storage system is connected to the grid, it is necessary to obtain its operating status information in a timely and accurate manner. The operating status data of the energy storage system is uploaded to the higher-level dispatch center via the energy storage coordination control device. However, with the intelligence and informationization of the power grid, various cyber attack methods have emerged endlessly. There are various security risks in the way of information interaction based on the network. Attackers can analyze the communication protocols within them, or implement Eavesdropping attacks, Dos attacks, and tampering with sensitive data, etc., causing the energy storage coordination control device to obtain the wrong data sent by the dispatch center. The wrong data causes the energy storage system to output the wrong power, which may have the opposite effect when adjusting the voltage and frequency of the power grid, causing an imbalance in the voltage and frequency of the power grid, resulting in great economic losses, and even causing casualties due to equipment failure. Attackers can also gradually invade the upper-level dispatch center by using the energy storage coordination control device as a springboard. In view of the above problems, this paper uses a random forest-based network attack detection module is used to make real-time judgments on all data flows of the energy storage coordination control device. That is, when the energy storage coordination control device is subjected to a network attack, all data flows of the energy storage coordination control device pass a trained random forest attack detection model, thereby implementing attack detection on real-time data flows.

Damage Localization in a Building Structure during Seismic Excitation

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.

Space Data in the Fight against Pandemics: Privacy Concerns and Sharing of Benefits from the Use of Space Technology for Decision-Making

The fast and continuous collection and distribution of information are essential for decisionmaking in the first-response phase, as well as in the constant monitoring during and after the peaks of the Coronavirus Disease-2019 (COVID-19) outbreak. Particularly in times of emergency that require immediate reaction on behalf of local and global authorities, it is important that their reaction is based on reliable information. The unique features of satellite technology, which enable the steady flow of accurate near real-time data, have granted it a vital role in the fight against the COVID-19 pandemic. This article will address the uses of space data for public health and their legal implications, particularly in terms of privacy and access to data. remote sensing, geolocation, tracing applications, privacy, data access