Using real-time process data of domain-specific learning processes to provide adaptive support for learning and instruction: Challenges and opportunities

For Global Navigation Satellite System (GNSS) data processing, voluminous real-time Continuously Operating Reference Stations (CORS) data processing is a challenging problem. There are many methods have been proposed for regional network processing, such as parallel computing. However, they are mainly used for post-processing or near real-time processing. Due to the magnitude-increased and epoch-related of large geographic area CORS data processing, it brings huge challenges for real-time reception and efficient processing. Therefore, a real-time distributed Precise Point Positioning (PPP) platform is designed based on the idea of distributed computing and message queue to solve voluminous real-time CORS data processing, and it decomposed the real-time data processing into three processes: Input/output (I/O) multiplexing for real-time stream data acquisition, parallel PPP computing, and Weight Round Robin task scheduling. The real-time data of 5 International GNSS Service (IGS) stations is processed, the results show that it generally takes 30 min to achieve accuracy within centimeter. When the platform is applied for 1414 CORS real-time data processing in China, it can perform PPP calculations with stability and high precision. Application of the real-time Precipitable Water Vapor (PWV) monitoring is also provided.

Among various nano-scale linear displacement sensors, grating scales have been widely used in industrial fields due to their strong anti-interference ability, cost-effectiveness and compactness. The main development directions of the scale are: high precision, large range, high speed, high dimension and absolute type. Our team has completed the optical layout design of the absolute two-dimensional grating rule, which brings new challenges to the design data acquisition and processing hardware system due to the requirements of multiple dimensions and high speeds. There is no real-time data processing system suitable for absolute two-dimensional scales. This paper presents our latest progress in designing and implementing an absolute two-dimensional grating distance measurement real-time data processing platform. Our platform mainly contains four different functional modules. First, the circuit conditioning module performs I-V conversion and signal amplification and filtering on the weak current signal output from the photodiode. Secondly, an 8- channel high-speed data acquisition module with 14-bit resolution and 80 MSPS maximum sampling rate was designed to convert analog laser pulse signals into digital signals. Third, we have established a real-time data processing module that allows 16 bits of data to be entered in the FPGA to calculate the absolute two-dimensional scale distance. Finally, a data transfer module based on 128MB DDR SDRAM and USB 2.0 was added so that we can easily debug the platform on a PC. The performance of our system is evaluated in real time. The test platform consists of a laser, a twodimensional grating optical path, and our data processing system. The absolute two-dimensional scale has a moving speed of 1m/s, a signal frequency of 10MSPS, a laser emitting signal wavelength of 540nm, and a moving distance of 10-15mm. Experimental results show that our system can output at a rate of 2500 points per second. Measurement results, measurement deviation is less than 50nm.

Human beings keep exploring the physical space using information means. Only recently, with the rapid development of information technologies and the increasing accumulation of data, human beings can learn more about the unknown world with data-driven methods. Given data timeliness, there is a growing awareness of the importance of real-time data. There are two categories of technologies accounting for data processing: batching big data and streaming processing, which have not been integrated well. Thus, we propose an innovative incremental processing technology named after Stream Cube to process both big data and stream data. Also, we implement a real-time intelligent data processing system, which is based on real-time acquisition, real-time processing, real-time analysis, and real-time decision-making. The real-time intelligent data processing technology system is equipped with a batching big data platform, data analysis tools, and machine learning models. Based on our applications and analysis, the real-time intelligent data processing system is a crucial solution to the problems of the national society and economy.

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

PurposeParticularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions.Design/methodology/approachAiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts.FindingsRT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself.Originality/valueThis paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.

Data type and amount in human society is growing in amazing speed which is caused by emerging new services as cloud computing, internet of things and location-based services, the era of big data has arrived. As data has been fundamental resource, how to manage and utilize big data better has attracted much attention. Especially, with the development of internet of things, how to processing large amount real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention with high-performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. This paper studied the challenges of big data firstly and concludes all these challenges into six iss ues. In order to improve the performance of real-time processing of large data, this paper builds a kind of real-time big data processi ng (RTDP) architecture based on the cloud computing technology and then proposed the four layers of the architecture, and hierarc hical computing model. This paper proposed a multi-level storage model and the LMA-based application deployment method to meet the real-time and heterogeneity requirements of RTDP system. We use DSMS, CEP, batch-based MapReduce and other processing mode and FPGA, GPU, CPU, ASIC technologies differently to processing the data at the terminal of data collection. We structured the dat a and then upload to the cloud server and MapReduce the data combined with the powerful computing capabilities cloud architecture. This paper points out the general framework for future RTDP system and calculation methods, is currently the general method RTDP system design.

Internet of Things (IoT) devices have generated a record volume of real-time data that demands scalable and effective processing frameworks. This research presents scalable machine learning (ML) methods for real-time large-scale data analytics in IoT networks. For real-time applications, IoT data is too fast and complex for standard analytics systems; therefore, models must be accurate and computationally efficient. We propose a hybrid ML framework with distributed learning, edge-cloud coordination, and stream processing pipelines. Federated learning ensures anonymity, and Apache Kafka-based communications handle real-time data processing and ingestion. We evaluate the model's latency, throughput, and accuracy on numerous IoT datasets. Our results show that hybrid online learning methods with parallel processing improve system responsiveness and resource utilisation. A bar chart and a multi-line graph illustrate model performance and scalability. Performance matrices and comparison Tables confirm the approach's efficacy. This paper explains how to utilise machine learning to scale vertically and horizontally in IoT contexts, thereby driving smart infrastructure. We conclude by considering energy utilisation, data heterogeneity, and future research directions such as federated transfer learning, light neural networks, and quantum-aided ML for IoT contexts.

In today's technological environments, the vast majority of big data-driven applications and solutions are based on real-time processing of streaming data. The real-time processing and analytics of big data streams play a crucial role in the development of big-data driven applications and solutions. From this perspective, this paper defines a lifecycle for the real-time big data processing. It describes existing tools, tasks, and frameworks by associating them with the phases of the lifecycle, which include data ingestion, data storage, stream processing, analytical data store, and analysis and reporting. The paper also investigates the real-time big data processing tools consisting of Flume, Kafka, Nifi, Storm, Spark Streaming, S4, Flink, Samza, Hbase, Hive, Cassandra, Splunk, and Sap Hana. As well as, it discusses the up-to-date challenges of the real-time big data processing such as “volume, variety and heterogeneity”, “data capture and storage”, “inconsistency and incompleteness”, “scalability”, “real-time processing”, “data visualization”, “skill requirements”, and “privacy and security”. This paper may provide valuable insights into the understanding of the lifecycle, related tools and tasks, and challenges of real-time big data processing.

This study investigates how real-time health data processing and personalized AI-based recommendations affect the effectiveness of remote health monitoring systems for the elderly. It also examines the role of digital literacy in moderating the link between real-time health data collection and systems effectiveness. using a quantitative approach, data were gathered through an online survey with 385 participants, including elders, caregivers, and health professionals. Responses were measured on a 5-point Likert scale, and the sample was selected using purposive stratified sampling to ensure diversity. Reliability and validity were tested using Cronbach’s alpha, exploratory factor analysis, and multiple linear regression. Findings show that both real-time data processing and personalized recommendations significantly enhance system effectiveness. notably, digital literacy strengthens the positive impact of data processing on systems performance, underlining the importance of user skills in maximizing AI’s benefits for eldercare. The study adds to existing research by applying Cognitive Fit Theory, Socioemotional Selectivity Theory, and Digital Divide Theory to AI-driven health systems. It offers practical insights for developers, healthcare providers, and policymakers, emphasizing the need for user-centered design and digital inclusion. Overall, it highlights how aligning technology with user capability can improve outcomes in elderly care support more accessible, intelligent health solutions.

With the enrichment of human social life, tourism is becoming more and more popular. The development of cloud computing, big data, internet of things technology makes smart tourism gradually evolve from the concept to a technology which can thoroughly change people's lives. Through smart tourism, a large number of rich and comprehensive real-time data can be available, including source of tourists, travel information, travel routes and other data which can achieve real-time monitoring of the scenic spots and precision marketing to customers, thus promote the development of tourism services and improve tourism. This paper will take smart tourism as the research object, and introduces large quantities of real-time data analysis and processing technology in smart tourism, and real-time processing data modeling methods. On this basis, scenic passenger flow monitoring model, scenic tourist analysis model and scenic passenger flow warning model will be established respectively. The technology presented in this paper has the characteristics of high real-time, high reliability, high accuracy of data processing, and has strong applicability, which be extended to other large data real-time processing scenarios.

In order to meet the need of image shooting with CCD in unmanned aerial vehicles, a real-time high resolution CCD data processing system based on variable frame rate is designed. The system is consisted of three modules: CCD control module, data processing module and data display module. In the CCD control module, real-time flight parameters (e.g. flight height, velocity and longitude) should be received from GPS through UART (Universal Asynchronous Receiver Transmitter) and according to the corresponding flight parameters, the variable frame rate is calculated. Based on the calculated variable frame rate, CCD external synchronization control impulse signal is generated in the control of FPGA and then CCD data is read out. In the data processing module, data segmentation is designed to extract ROI (region of interest), whose resolution is equal to valid data resolution of HDTV standard conforming to SMPTE (1080i). On one hand, Ping-pong SRAM storage controller is designed in FPGA to real-time store ROI data. On the other hand, according to the need of intelligent observing, changeable window position is designed, and a flexible area of interest is obtained. In the real-time display module, a special video encoder is used to accomplish data format conversion. Data after storage is packeted to HDTV format by creating corresponding format information in FPGA. Through inner register configuration, high definition video analog signal is implemented. The entire system has been implemented in FPGA and validated. It has been used in various real-time CCD data processing situations.

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

The dramatic development of IT technology has increased absolute amount of data to store, analyze, and process for computers and it has also rapidly increased the amount of realtime processing for data stream which contains various information with the development of sensor devices. The existing real-time data processing method using RDBMS can generate partial data loss or processing delay when overload occurred. In this paper, applying IMDG, which is one of the spotlighted in in-memory computing area, recently, it is proposed a structure to be able to increase performance of spatial data processing in realtime. In addition, with the benchmark, it is checked that IMDG structure has high performance for spatial data processing speed with each other.

With the development of data achieving ability of high resolution remote sensing satellites and the enhancement of data receiving ability on the ground, the data processing workload of the existing ground application system for remote sensing satellites is growing, and the demand of real-time data processing is increasingly higher. In recent years, stream computing has become a research hotspot due to the high performance for real-time concurrent processing and distributed computing. In this paper, a new real-time processing method of remote sensing satellite data is proposed by the framework of stream computing. Firstly, according to the characteristics of remote sensing data processing, the stream computing is modeled, the processing time of the instance is abstracted, and the multi-task optimization and scheduling methods are given. Then the real-time data processing system of remote sensing satellite is implemented by using this method, and the data tuples, processing components and task topology are redesigned. Finally, the data processing time and throughput rate of the system are tested and analyzed. Experimental results show that the real-time performance of the system is greatly improved.