Real-time Data Integration Research Articles

Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research.

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments

The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment.

An online COVID-19 self-assessment framework supported by IoMT technology

AbstractAs COVID-19 pandemic continued to propagate, millions of lives are currently at risk especially elderly, people with chronic conditions and pregnant women. Iraq is one of the countries affected by the COVID-19 pandemic. Currently, in Iraq, there is a need for a self-assessment tool to be available in hand for people with COVID-19 concerns. Such a tool would guide people, after an automated assessment, to the right decision such as seeking medical advice, self-isolate, or testing for COVID-19. This study proposes an online COVID-19 self-assessment tool supported by the internet of medical things (IoMT) technology as a means to fight this pandemic and mitigate the burden on our nation’s healthcare system. Advances in IoMT technology allow us to connect all medical tools, medical databases, and devices via the internet in one collaborative network, which conveys real-time data integration and analysis. Our IoMT framework-driven COVID-19 self-assessment tool will capture signs and symptoms through multiple probing questions, storing the data to our COVID-19 patient database, then analyze the data to determine whether a person needs to be tested for COVID-19 or other actions may require to be taken. Further to this, collected data can be integrated and analyzed collaboratively for developing a national health policy and help to manage healthcare resources more efficiently. The IoMT framework-driven online COVID-19 self-assessment tool has a big potential to prevent our healthcare system from being overwhelmed using real-time data collection, COVID-19 databases, analysis, and management of people with COVID-19 concerns, plus providing proper guidance and course of action.

Real-time data integration of an internet-of-things-based smart warehouse: a case study

PurposeCreating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.Design/methodology/approachThe study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT.FindingsThe experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission.Research limitations/implicationsWhile the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process.Originality/valueThe research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.

MaDCrow, a Citizen Science Infrastructure to Monitor Water Quality in the Gulf of Trieste (North Adriatic Sea)

Within the United Nations Sustainable Development 2030 agenda, sustainable growth in the marine and maritime sector needs sea water quality monitoring. This is a very demanding and expensive task which results in the sea being largely undersampled. MaDCrow is a research and development project supported by the European Regional Development Fund, that involves citizens as data collectors while aiming to improve public environmental awareness and participation in scientific research. Its goal is to create an innovative technological infrastructure for real-time acquisition, integration and access of data, thus generating knowledge on sea water quality and marine ecosystem of the Gulf of Trieste. Data acquisition is based on an autonomous and removable device, developed within the project, that can be deployed on any small size sailing boat, recreational vessel, or fishing boat. The device holds low-cost sensors to measure pH, temperature, dissolved oxygen and salinity and the hardware and software to acquire, georeference and transmit the environmental data without interfering with the activities of the boats. In this work we analyze the use, capabilities and advantages of low-cost sensors but also their limitations, comparing, with a special focus on pH, their performances with those of the traditional ones. Applying the paradigm in a highly anthropized area such as the Gulf of Trieste, which is characterized also by a very high spatial and temporal variability of environments, we point out that this new approach allows to monitor sea water quality and highlight local anomalies with a resolution and spatial and temporal coverage that was not achievable with previous procedures, but yet at very low costs. Once received, data are then processed and submitted to a mediation flow that contextualizes and disseminates them for public use on a website. The final products have been customized to reach stakeholders such as tourists, fishermen and policy makers. The availability of information understandable to everyone, while fostering environmental awareness, stimulates, at the same time, involvement and participation of citizen scientists in the initiative. In the future, while committing to enlarge the number of participants, we will extend the analysis also toward other types of sensors.

Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

The Impact of Data Science on Actuarial Science and Predictive Modeling for Insurance Risk Management

As data science increasingly integrates into actuarial science, it is reshaping risk assessment and predictive modeling in the insurance industry. This paper explores the transformative impact of data science on actuarial practices, focusing on advancements in risk assessment and predictive modeling within the insurance industry. By leveraging machine learning algorithms, big data analytics, and real-time data integration, data science has revolutionized traditional actuarial methods, enhancing accuracy and efficiency. The study presents a comparative analysis of data-driven models versus traditional approaches, revealing significant improvements in predictive accuracy and operational efficiency. However, it also identifies challenges such as data quality and model complexity. The paper concludes by discussing the implications of these developments for the insurance sector and proposing future research directions to further understand and leverage data science advancements in actuarial practices.

Research on Intelligent Flight Test Monitoring Technology Based on Expert System

The current manual-based traditional flight test monitoring technology can no longer fulfill the requirements of high integration, system complexity and technological advancement of new aircraft. In addition, the existing flight monitoring systems are mostly single-point, single-system applications, including ground-assisted systems such as meteorology, radar, and response system. It is easy to cause problems such as data islands, which is not conducive to data sharing and comprehensive evaluation and analysis. This paper proposes a new method for analyzing and mining existing test data based on expert system technology to build a flight test expert system library. A new real-time monitoring system is designed that can meet the real-time processing and integration of multi-source data, comprehensive evaluation and analysis, intelligent warning and assistant decision-making. This system expands the function and intelligence of the existing system, and further improves the quality, safety and efficiency of the flight test monitoring system.

Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.

Co-Clinical Trials: An Innovative Drug Development Platform for Cholangiocarcinoma

Cholangiocarcinoma (CCA), a group of malignancies that originate from the biliary tract, is associated with a high mortality rate and a concerning increase in worldwide incidence. In Thailand, where the incidence of CCA is the highest, the socioeconomic burden is severe. Yet, treatment options are limited, with surgical resection being the only form of treatment with curative intent. The current standard-of-care remains adjuvant and palliative chemotherapy which is ineffective in most patients. The overall survival rate is dismal, even after surgical resection and the tumor heterogeneity further complicates treatment. Together, this makes CCA a significant burden in Southeast Asia. For effective management of CCA, treatment must be tailored to each patient, individually, for which an assortment of targeted therapies must be available. Despite the increasing numbers of clinical studies in CCA, targeted therapy drugs rarely get approved for clinical use. In this review, we discuss the shortcomings of the conventional clinical trial process and propose the implementation of a novel concept, co-clinical trials to expedite drug development for CCA patients. In co-clinical trials, the preclinical studies and clinical trials are conducted simultaneously, thus enabling real-time data integration to accurately stratify and customize treatment for patients, individually. Hence, co-clinical trials are expected to improve the outcomes of clinical trials and consequently, encourage the approval of targeted therapy drugs. The increased availability of targeted therapy drugs for treatment is expected to facilitate the application of precision medicine in CCA.

English

The rise in disruptions to supply chain is a major concern to most organizations. Supply chain managers need an innovative framework to respond to them. This research considers the supplier selection stage a strategic point to act. With careful selection, resilience is built in their operations. However, the challenge becomes what criterial factors to consider in the selection process, especially with global uncertainty. This paper makes use of multiple case studies, interviews and literature to answer this question. The result identified four major criteria: Steady information dissimilation via real-time data integration, robust disruption management plan continuously improved with experience, useful cooperation and alliances agreements and the interests of government and their regulators in the sector. The result also identifies issues which procurement managers need to address, internally. In conclusion, it accepts that it is possible to build a system which responds positively and mitigates the effects of disruptions, but this requires redesigning the process of supplier selection, after taking a macro-level re-evaluation of the supply network.   Key words: Disruptions, supply chain, suppliers, resilience, uncertainty.

An openBIM Approach to IoT Integration with Incomplete As-Built Data

Digital Twins (DT) are powerful tools to support asset managers in the operation and maintenance of cognitive buildings. Building Information Models (BIM) are critical for Asset Management (AM), especially when used in conjunction with Internet of Things (IoT) and other asset data collected throughout a building’s lifecycle. However, information contained within BIM models is usually outdated, inaccurate, and incomplete as a result of unclear geometric and semantic data modelling procedures during the building life cycle. The aim of this paper is to develop an openBIM methodology to support dynamic AM applications with limited as-built information availability. The workflow is based on the use of the IfcSharedFacilitiesElements schema for processing the geometric and semantic information of both existing and newly created Industry Foundation Classes (IFC) objects, supporting real-time data integration. The methodology is validated using the West Cambridge DT Research Facility data, demonstrating good potential in supporting an asset anomaly detection application. The proposed workflow increases the automation of the digital AM processes, thanks to the adoption of BIM-IoT integration tools and methods within the context of the development of a building DT.

Beyond Building Energy Simulation Tools

Various energy simulation tools are used to predict energy consumption in buildings at different stages from design to post-occupancy and maintenance. The inaccuracy and insufficiency of inputs used for building energy simulation (BES) often cause a discrepancy between the predicted and actual energy consumption. Inaccurate energy consumption estimations affect the accomplishment of the sustainability goals and reduction of energy consumption and CO2 emissions in buildings. The review of the existing literature suggests that the potential causes of the aforementioned uncertainty in building energy predictions are divided into 2 categories: human error (in design, construction, energy modelling, etc.) and the inaccuracy and insufficiency of inputs in BES. This research proposes the way forward for BES tools to improve their accuracy by enhancing the precision of various energy simulation inputs, integration of real-time data and use of machine learning and other emerging technologies.

Conceptualization and Prototype of an Anti-Erosion Sensing Revetment for Levee Monitoring: Experimental Tests and Numerical Modeling

The problem of levee embankment control during high flows is crucial for flood risk management in floodplains. Levee defense lines are often hundreds of kilometers long and surveys during emergencies are not easy tasks. For these reasons, levees monitored with in situ sensors and a suitable Information Technology (IT) real-time data communication and integration infrastructure, so-called “smart levees”, are gaining increasing interest as a crucial protection technology in floodplains. The paper presents the conceptualization of a prototype of a levee smart revetment, based on the integration of an optical fiber (OF) cable into a steel double-twisted wire mesh. In this paper the feasibility of this kind of revetment is firstly assessed. The flow pattern of overtopping water on the embankment is discussed, thus producing a raw estimation of the shear stress acting on the revetment in the field. A sample case is then analyzed in both numerical and laboratory tests. For this purpose, a numerical Finite Element Model (FEM) to describe the mechanical behavior of a double-twisted wire mesh when loaded along its own plane is presented. Numerical results indicate that the related strain, relatively low as compared to the steel wire yield stress, can be fully detected by the optical fiber continuous Brillouin sensor. This has been validated by the experimental activity performed and a digital twin of the prototype of the smart revetment, suitable for virtually testing the product under any load and constraint conditions and tailoring the production process, has been created.

THE IMPACT OF THE DETERMINANTS OF FOURTH INDUSTRIAL REVOLUTION ON COMPETITIVE ADVANTAGE AND SUSTAINABILITY – A STUDY FOR MALAYSIAN SMES

Small and medium-sized enterprises (SMEs) constitute one of the leading economic factors with strong consumer and stakeholder aspirations. The Fourth Industrial Revolution may also be defined as Industry 4.0, because it has evolved through automation and technical innovation that can transform products and manufacturing processes by real-time data integration, allowing consumers to be satisfied through customized products. It is important to examine the uniqueness of Industry 4.0 and the inherent difficulty in understanding the determinants, as most recent studies address the technological dimension of the concept. This study addresses the effect of the core determinants of Industry 4.0 in achieving sustainability as well as competitive advantage. The findings will serve to offer valuable insights for the SMEs to adopt smart technologies in the production system concerning Industry 4.0. This paper presents a conceptual model including hypotheses that can be tested further through a quantitative analysis.

Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins. However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable digital twins. More specifically, we propose a general machine-learning-based architecture for sensor validation built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behaviour and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world data-sets is conducted and the performance of the proposed architecture validated under hard and soft synthetically-generated faults.

APPLICATION OF DISTRIBUTED URBAN SENSOR NETWORKS FOR ACTIONABLE AIR QUALITY DATA

Abstract. Ambient air pollution continues to be a major human health burden around the world. Cities with existing smart data infrastructure, and those with smart city aspirations, would benefit from the integration of real-time data from an air quality sensor network. AirSENCE™ is one such sensor which monitors eight common pollutants at low cost. It has been deployed in Canada in cities of the Greater Toronto Area, e.g. the City of Oshawa, to augment the existing urban data network and study the impacts of traffic flow and land usage on air quality. Results reveal that distributed sensors are highly useful for detecting localized pollution events that would otherwise go undetected, providing policymakers with a valuable, actionable data for protecting public health. Coupling air quality sensors with other smart city data (traffic monitors in this case) was shown to provide a more comprehensive representation of how air pollutant levels are affected by human activity, which can better inform city planning decisions.

Towards online adaptation of digital twins

Abstract Digital twins have gained a lot of attention in modern day industry, but practical challenges arise from the requirement of continuous and real-time data integration. The actual physical systems are also exposed to disturbances unknown to the real-time simulation. Therefore, adaptation is required to ensure reliable performance and to improve the usability of digital twins in monitoring and diagnostics. This study proposes a general approach to the real-time adaptation of digital twins based on a mechanism guided by evolutionary optimization. The mechanism evaluates the deviation between the measured state of the real system and the estimated state provided by the model under adaptation. The deviation is minimized by adapting the model input based on the differential evolution algorithm. To test the mechanism, the measured data were generated via simulations based on a physical model of the real system. The estimated data were generated by a surrogate model, namely a simplified version of the physical model. A case study is presented where the adaptation mechanism is applied on the digital twin of a marine thruster. Satisfactory accuracy was achieved in the optimization during continuous adaptation. However, further research is required on the algorithms and hardware to reach the real-time computation requirement.

IoT streaming data integration from multiple sources

The Internet of Things (IoT) has recently received considerable interest due to the development of smart technologies in today’s interconnected world. With the rapid advancement in Internet technologies and the proliferation of IoT sensors, myriad systems and applications generate data of a massive volume, variety and velocity which traditional databases and systems are unable to manage effectively. Many organizations need to deal with these massive datasets that encounter different types of data (e.g., IoT streaming data, static data) in different formats (e.g., structured, semi-structured) coming from multiple sources. Several data integration mechanisms have been designed to process mostly static data. Unfortunately, these techniques are not able to deal with and integrate IoT streaming datasets from multiple sources. In this paper, we identify the challenges of IoT Streaming Data Integration (ISDI) and present a formal approach for the real-time integration of such IoT streaming datasets. We address one of the important issues of timing conflict/alignment among streaming data coming from multiple sources. A generic window-based ISDI approach is proposed to deal with IoT data in different formats and algorithms are developed to integrate IoT streaming data from multiple sources. In particular, we extend the basic windowing algorithm for real-time data integration and to deal with the timing alignment issue. We also introduce a de-duplication algorithm to deal with data redundancy and to demonstrate the useful fragments of the integrated data. We conduct several sets of experiments and quantify the performance of our proposed window-based approach. In particular, we compare our local experimental results with a real setup for streaming data, using Apache Spark. The results of the experiments, which are performed on several IoT datasets, show the efficiency of our proposed solution in terms of processing time. The results are also used to provide an integrated data view to the users.

A Case Study of Highly-Engaged Educators' Integration of Real-Time Seismic Data in Secondary Classrooms

The Incorporated Research Institutions for Seismology (IRIS) Seismographs in Schools (SIS) program has supported teachers in the use of educational seismometers and real-time seismic data in the classroom for the past 20 years. To better understand how the seismographs and seismic data are being used in the classroom, we sent a survey to 770 past or current program participants. The survey asked about their seismometer, seismic data use, software use, the impact of seismic data on their classroom, and what additional seismology-related resources or instruction they provide to their students. Four highly-engaged teachers were then recruited from this larger pool for a case study. The purpose of this study was to better understand these highly-engaged teachers, their experiences with seismic instrumentation, software and data in the classroom, how and to what degree seismic data has impacted their curriculum and instruction, and their perceptions of the impact of the SIS program on their students’ understanding of Earth Science concepts. The case study results show that each of the highly-engaged teachers values data driven instruction, instrument science, and the integration of seismic data into the classroom more than just during their earthquake units. They have made it part of everyday class activities by having students be aware of data coming in, noting earthquakes in the news, and helping students learn more about analyzing data for advanced investigations. While there were differences in their implementations, in all cases a critical feature of their engagement with students was use of a variety of seismology-related resources which connected the seismic data to the rest of the curriculum. Thus, the use of seismic data was just one component of their seismology-related teaching. This study also highlights the value of a local sensor, as all four highly-engaged teachers stressed that students as stewards of the seismometer linked them to the science in an engaging and dynamic way. Thus, while the highly-engaged teachers had a primary responsibility to promote learning, their focus on student engagement is also helping to create young citizen scientists.