Intelligent Approach for Heterogeneous Data Integration: Information Processes Analysis Engine in Clinical Remote Monitoring Systems

Khovrichev, Mikhail A.; Elkhovskaya, Liubov; Fonin, Vladimir; Balakhontceva, Marina A.

doi:10.1016/j.procs.2019.08.188

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Aiming to design and develop an intelligent approach, he integrated heterogeneous data and knowledge sources into personalized healthcare. e integrated profile proposed by Khovrichev et al mainly focused on remote monitoring of patients with arterial hypertension and chronic heart failure [2]. Park et al conducted two surveys independently for certain regions and proposed a measurement error model approach to integrate mean estimates obtained from the two surveys.…”

Section: Related Workmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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With the development of technology, more and more enterprises have begun to use data integration analysis to make decisions. The decision spatial information system is an information system that provides decision support after analyzing and processing the spatial information. Data integration refers to a method of reasonably centralizing different or multisource data for data sharing, which can assist in decision management. This paper aims to study a more efficient data integration method for decision space information system. In this paper, an emergency information decision-making system for digital water network is designed, and a specific data integration algorithm is proposed. And the performance test of the early warning capability and power consumption of the system is carried out. The results show that the early warning time of the system designed in this paper is within 1 to 4 seconds, while the early warning time of the traditional system is within 1.5 seconds to 8 seconds. The power consumption of the system designed in this paper is not more than 25 W, while the traditional system power consumption is up to 50 W. The power consumption of the system in this paper is about half of the traditional system. Compared with the traditional system early warning capability, the system designed in this paper is significantly faster, the system power consumption is lower, and the system is more stable.

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Section: Related Workmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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“…The system could collect a huge amount of data but in the specific emergency only. The data integration technique must have the ability to filter and present just the relevant data to be processed (Khovrichev et al, 2019). Neville et al (2016) introduced EMS with spatial database management system.…”

Section: Approaches For Integration Collaboration and Communicationmentioning

confidence: 99%

Knowledge-Based Decision Support System for Emergency Management: The Pandemic Framework

Mohamed

2021

Journal of Information and Communication Technology

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Emergency management systems (EMS) assist emergency managers to resolve emergencies on hand, through analyzing the emergency characteristics and consolidating data from different departments that are involved in resolving the emergency. Many countries have adopted various forms of EMS that are specialized in resolving one type of emergency, and studies demonstrate their effectiveness in producing better decisions. However, the COVID-19 pandemic uncovered the lack of a comprehensive framework that could deal with different emergencies. It also revealed the inability of the current systems to communicate with each other to retrieve the needed data. The aim of this study is to show the current state of EMS in emergency departments by constructing a framework for a knowledge-based decision support system for emergency management focusing on resolving pandemics. Qualitative approach was adopted in this research, where the authors reviewed emergency management in general and pandemics in specific. Existing EMS systems were investigated, and knowledge- based decision support systems were explored. Approaches for integration, communication, and collaboration were also studied. As a result of this study, a comprehensive framework, i.e., a knowledge- based decision support system for emergency departments, focusing on resolving pandemics was introduced. The framework was validated by a domain expert who provided insights and suggestions for future research. While the primary research focus is to assist emergency managers in resolving the COVID-19 pandemic, the proposed framework is unique by adopting different approaches and techniques that enable the system to deal with various emergencies not limited to the current pandemic.

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“…Remote monitoring in today's data centres has become very essential due to the large compute infrastructure and storage volumes. In order to maintain high availability of data, all components of the data centre infrastructure must carry out their assigned tasks, hence maintaining and monitoring the health of the data centre components including power, environment is of utmost importance [1]. All physical infrastructure failures have a significant impact on the effectiveness of data centre services.…”

Section: Introductionmentioning

confidence: 99%

Design of Monitoring Tools for Data Centre Downtime Reduction

2023

IJBMIEH

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This paper presents a new monitoring tool and event management method for data centre compute, network and storage infrastructure based on node event processing. The uptime of highly classified data centres are not only to be maintained at the highest level of reliability and availability of the operation, but also fast, specific event identification and rectification, which altogether improves availability of the resources is important. The new method, using a tree node for each element of the data centre resources provides information about the compute, network and storage file system configuration in a specific node. Its major advantage is that in our case where a large number of heterogeneous computers are present, it helps us in monitoring all the elements of the computer resources and gives information for alerting the associated work centres before any of the identified events that might occur. By monitoring and informing apriori to the concerned work centres the state of the systems, it lowers errors in data centre physical infrastructure operating costs, improving at the same time the level of operations efficiency. This method resulted that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data centre physical infrastructure. In this paper, the design and development of two customised dashboards to monitor the compute, storage and network elements of the heterogeneous data centre for uptime maintenance and optimal performance is presented. The dashboards are designed, keeping in view the nature of tasks carried out and the resource requirements of various work centres in the data centre. One dashboard displays dynamically created icons for each of the compute resources in the data centre. On clicking any of the icon, complete details of the corresponding server is fetched showing the status, usage, configuration and available resources. Furthermore, a unique colouring scheme is followed wherein the icon is displayed green if the server is healthy and orange if the server is facing a resource crunch (disk, memory, etc.) and red if the server is not reachable. The dashboard GUI refreshes every 5 min (is configurable), displaying the latest status details of the servers in the data centre. The second Dashboard is developed with the capability to monitor the storage, cloud and network infrastructure components. The dashboard collects data from different elements of the storage i.e. Meta Data Servers, Storage, Core and Edge switches etc. and processes the collected data to a customized format for display. It delivers details like availability of Storage Meta Data Servers, switches and file systems, disk space capacity monitoring, file system backup status, Monitoring of the hierarchical Storage including Tape Library and the availability of Production ESXi hosts cluster. The GUI is updated with new requirements to further fine-tune and reduce manual intervention for monitoring operations.

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Intelligent Approach for Heterogeneous Data Integration: Information Processes Analysis Engine in Clinical Remote Monitoring Systems

Cited by 10 publications

References 6 publications

Data Integration Method Design of Decision Spatial Information System

Data Integration Method Design of Decision Spatial Information System

Knowledge-Based Decision Support System for Emergency Management: The Pandemic Framework

Design of Monitoring Tools for Data Centre Downtime Reduction

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