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Academic literature on the topic 'Middleware-independent deploy'
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Journal articles on the topic "Middleware-independent deploy"
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Ardizzone, Valeria, Roberto Barbera, Antonio Calanducci, et al. "The DECIDE Science Gateway." Journal of Grid Computing (2012) 10 (September 13, 2013): 689–707. https://doi.org/10.1007/s10723-012-9242-3.
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The motivation of this work fits with the general vision to enable e-health for European citizens, irrespective of their social and financial status and their place of residence. Services to be provided include access to a high-quality early diagnostic and prognostic service for the Alzheimer Disease and other forms of dementia, based both on the European Research and Education Networks and the European Grid Infrastructure. The present paper reports on the architecture and services of a Science Gateway developed in the context of the DECIDE project, which aims to support the medical community in its daily duties of patients’ examination and diagnosis. The implementation of the Science Gateway is described with particular focus on the standard technologies adopted to ease the access by non IT-.expert users. The work leverages on an authentication and authorization infrastructure based on Identity Federations and robot certificates and on the adoption of the SAGA standard for middleware-independent Grid interaction. The architecture and the functionalities of the digital repository for medical image storage and analysis are also presented.
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Alam, Aftab, and Young-Koo Lee. "TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud." Sensors 20, no. 12 (2020): 3581. http://dx.doi.org/10.3390/s20123581.
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In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.
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Storetvedt, Maxim, Latchezar Betev, Håvard Helstrup, Kristin Fanebust Hetland, and Bjarte Kileng. "The ALICE Grid Workflow for LHC Run 3." EPJ Web of Conferences 295 (2024): 04042. http://dx.doi.org/10.1051/epjconf/202429504042.
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In preparation for LHC Run 3 and 4 the ALICE Collaboration has moved to a new Grid middleware, JAliEn, and workflow management system. The migration was dictated by the substantially higher requirements on the Grid infrastructure in terms of payload complexity, increased number of jobs and managed data volume, all of which required a complete rewrite of the middleware using modern software languages and technologies. Through containerisation, self-contained binaries, managed by the JAliEn middleware, we provide a uniform execution environment across sites and various architectures, including accelerators. The model and implementation have proven their scalability and can be easily deployed across sites with minimal intervention. This contribution outlines the architecture of the new Grid workflow as deployed in production and the workflow process. Specifically shown is how core components are moved and bootstrapped through CVMFS, enabling the middleware to run anywhere fully independent of the host system. Furthermore, we examine how new middleware releases, containers and their runtimes are centrally maintained and easily deployed across the Grid, also by the means of a common build system.
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Fuentes, Lidia, and Nadia Gámez. "Configuration Process of a Software Product Line for AmI Middleware." JUCS - Journal of Universal Computer Science 16, no. (12) (2010): 1592–611. https://doi.org/10.3217/jucs-016-12-1592.
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Developing Ambient Intelligence applications is a very complex task since it implies dealing with low-level software and hardware resources. The use of a middleware platform may alleviate this task by providing a set of high-level and platform-independent services to these kinds of applications. Nevertheless, the tendency is that the middleware deployed in each device has a flat and homogeneous architecture, although these devices and the requirements of intelligence environments are heterogeneous. This implies the middleware software deployed in each device normally contains more functionality than strictly required, leading to waste resources so scarce in lightweight devices. But the configuration and deployment of a minimal middleware customized to a target platform is a complex task, due to the diversity of hardware and software present in devices and the variable requirements of ambient intelligence applications. In order to solve these shortcomings, we propose to customize the piece of software related to the middleware platform by using a Software Product Line engineering approach. This paper presents an innovative configuration process for a software product line for ambient intelligence middleware where a minimal set of high-level parameters needs to be specified. So, the software engineers for this kind of systems can automatically obtain customized middleware by simply specifying this high-level information.
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Künzel, A., A. Puchta, P. Gönnheimer, and J. Fleischer. "Modular and flexible Automation Middleware based on LabVIEW and OPC UA." IOP Conference Series: Materials Science and Engineering 1193, no. 1 (2021): 012109. http://dx.doi.org/10.1088/1757-899x/1193/1/012109.
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Abstract The increasing automation level of processes in production systems leads to new technical challenges, especially in the implementation and maintenance of software architectures. New requirements arise regarding the interface between Programmable Logic Controllers (PLC), robots, Human Machine Interfaces (HMI) and superordinate information systems (e.g. ERP). Industry 4.0 demands, among other things, an increase in flexibility, adaptability and transparency to achieve vertical and horizontal interoperability and a continuous integration. The innovative automation middleware is capable of replacing the heterogeneous interface landscape, which currently exists in many companies and institutions. The basic idea is the implementation of a modular and standardized middleware. Due to relevant characteristics, such as dataflow orientation and graphical programming interface, using LabVIEW as a programming language turned out to be the most suitable solution. The system deploys OPC Unified Architecture (OPC UA) to connect all required components across multiple enterprise levels. Moreover, the software solution controls the workflow and collects process data for further analysis. In contrast to software products available on the market, which usually come along with manufacturer dependencies, the established middleware based on the combination LabVIEW and OPC UA is transparent, extensible and independent.
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Habib, Kaiser, Mohamad Hanif Md Saad, Aini Hussain, Mahidur R. Sarker, and Khaled A. Alaghbari. "An Aggregated Data Integration Approach to the Web and Cloud Platforms through a Modular REST-Based OPC UA Middleware." Sensors 22, no. 5 (2022): 1952. http://dx.doi.org/10.3390/s22051952.
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The Internet of Things (IoT) empowers the development of heterogeneous systems for various application domains using embedded devices and diverse data transmission protocols. Collaborative integration of these systems in the industrial domain leads to incompatibility and interoperability at different automation levels, requiring unified coordination to exchange information efficiently. The hardware specifications of these devices are resource-constrained, limiting their performance in resource allocation, data management, and remote process supervision. Hence, unlocking network capabilities with other domains such as cloud and web services is required. This study proposed a platform-independent middleware module incorporating the Open Platform Communication Unified Architecture (OPC UA) and Representational State Transfer (REST) paradigms. The object-oriented structure of this middleware allows information contextualization to address interoperability issues and offers aggregated data integration with other domains. RESTful web and cloud platforms were implemented to collect this middleware data, provide remote application support, and enable aggregated resource allocation in a database server. Several performance assessments were conducted on the developed system deployed in Raspberry Pi and Intel NUC PC, which showed acceptable platform resource utilization regarding CPU, bandwidth, and power consumption, with low service, update, and response time requirements. This integrated approach demonstrates an excellent cost-effective prospect for interoperable Machine-to-Machine (M2M) communication, enables remote process supervision, and offers aggregated bulk data management with wider domains.
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Vandenberk, Thijs, Valerie Storms, Dorien Lanssens, et al. "A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study." JMIR mHealth and uHealth 7, no. 10 (2019): e12586. http://dx.doi.org/10.2196/12586.
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Background Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use. Objective The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies. Methods DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting. Results A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization. Conclusions The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.