Middleware Components Research Articles

Ambient-aware continuous aid for mountain rescue activities

Ambient-awareness in conjunction with pervasive computing is a significant challenge for system designers. It follows the necessity of gathering raw, massive and heterogeneous environmental data which we obtained, while middleware processes must merge context modelling and reasoning seamlessly. We proposed a system supporting mountain rescuers which is demanding due to the large number of environmental objects interacting, as well as high data variability. We presented complex context processing embedded in the proposed context life cycle and implemented it in a difficult mountain environment. We introduced five weather scenarios which are a basis for contextual and perceptual processing during the validation of our model. The system merges a message streaming broker for massive data transport, low and high-level processing algorithms, repositories and a logical SAT solver. It constitutes a Context-Aware-as-a-Service (CAaaS) system, offering advanced support for mountain rescue operations. The provided software model defines middleware components which act on a predicted context and transform in situ sensor data into smart decisions, and which could operate as a platform-based cloud computing model. It is an enabler yielding a synergy effect with different software components orchestration when providing pro-activeness and non-intrusiveness concerning smart decisions.

Integration of 5G Wireless into Hybrid Media Production Including SMPTE ST 2110

Professional media production will in the future require more flexibility over where and how its operations are provided, including where there are multiple networks, which could include 5G, wired, cloud-based, and home networks. The broadcast industry’s adoption of live networked media systems is still relatively immature, especially in wireless and there is not yet a consistent and complete approach to describing architectures and the interfaces required for interoperability. This article describes a set of gateway functions for a professional media production using both 5G and wired (SMPTE ST 2110-based) media networks, in particular a scenario from the EU Horizon IC-41 Project 5G-RECORDS ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.5g-records.eu/</uri> ) about a multicamera 5G-enabled broadcast studio. The Media Gateway (MG) converts media streams between the wireless and wired environments and the Media Orchestration and Control Gateway (MOCG) provides discovery, configuration, connection, and control of broadcast equipment deployed in various locations. In a ST 2110 environment, the latter is done in part through Advanced Media Workflow Association (AMWA) Networked Media Open Specifications (NMOS) APIs; however, this approach needs to be adapted for use in a 5G network, through the introduction of middleware components that decouple the wired and wireless. This article gives a detailed description of the wireless studio use-case and the approach taken by 5G-RECORDS in developing the MG and MOCG.

Trade‐offs to using standardized tools: Innovation enablers or creativity constraints?

AbstractResearch SummaryIn platform ecosystems, the creation of new products is often based on standardized development tools. Complementors often have a choice between either using these tools or creating the functionality themselves. In this paper, we study how the use of standardized development tools is related to the type of products created. By using data on the use of middleware (e.g., game engines) in the console video game market, we show that the use of development tools is associated with products that are less novel but with higher sales on average. We exploit a policy change that affected the ability of U.S.‐based developers to hire foreign workers as an instrument for the use of development tools and find further support for these patterns.Managerial AbstractWhen developing new products, firms often have to decide whether they base their technology on preexisting components and standardized tools or develop that technology themselves. In general, it has not been clear how using standardized third‐party tools that may be available to all firms in an industry affects the nature of the products that are created. Using data on middleware components, such as game engines, in the console video game industry, this paper shows the use of such standardized tools is associated with the creation of products that are less novel but generate higher sales on average. This is an important strategic consideration for firms, but also for platforms that make decisions regarding whether such tools should be allowed on their platform.

HealthStack: Providing an IoT Middleware for Malleable QoS Service Stacking for Hospital 4.0 Operating Rooms

Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, Quality of Service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: 1) a middleware for operating rooms with automatic QoS support for real-time data transmission and 2) a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultrawideband (UWB) real-time location systems (RTLSs) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.

Retraction Note to: A novel user interaction middleware component system for ubiquitous soft computing environment by using fuzzy agent computing system

Retraction Note to: A novel user interaction middleware component system for ubiquitous soft computing environment by using fuzzy agent computing system

Anomaly Detection of Smart Grid Equipment Using Machine Learning Applications

Many application systems in today’s smart grid network comprise a variety of middleware components, such as vibrating or spinning electrical equipment, data bases, storage, caches, and identification services, among others. Each component is a discrete bundle of physical or virtual computers that will generate a large amount of data in the form of logs and metrics, and failure of these high-vibrating machines will result in the system’s entire shutdown. As a result, the condition monitoring system for this smart grid equipment is more dependable and efficient in predicting the machine’s health ahead of time. To analyze the data and derive any inferences for future analysis and anomaly detection, we’ll need a separate system, which will take longer to handle data given by each component and will demand additional processing resources. As a result, in this chapter, machine learning methods are used to identify abnormalities or condition monitoring for smart grid equipment and machines. Here, we used two separate methods: multivariate statistical analysis by calculating Mob distance and auto encoders, which is an artificial neural network approach. Furthermore, the findings demonstrate that these apps are effective in identifying anomaly ahead of time, i.e. before a few days.

The Architecture of a Feasibility Query Portal for Distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) Patient Data Repositories: Design and Implementation Study.

BackgroundAn essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing.ObjectiveThis study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data.MethodsWe analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service.ResultsWe implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.ConclusionsWe designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.

A data management infrastructure for the integration of imaging and omics data in life sciences

BackgroundAs technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.ResultsWe propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.ConclusionsWe describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.

Immersive Intelligent Tutoring System for Remedial Learning Using Virtual Learning Environment

Intelligent Tutoring System (ITS) has been widely used in supporting personal learning. However, there is an aspects that have not become focus in ITS, namely immersive. This research proposes an Immersive Intelligent Tutoring (IIT) model based on Bayesian Knowledge Tracing (BKT) for determining the learner’s characteristics and learning content delivery strategies using genetic algorithms. The model uses remedial learning with a faded worked-out example. This study uses a 3-Dimensional Virtual Learning Environment (3DMUVLE) that implements immersive features to increase intrinsic motivation. This model was built using a client / server architecture. The server side component uses the MOODLE, the client side component uses OpenSim and its viewers, and the middleware component uses the Simulation Linked Object Oriented Dynamic Learning Environment (SLOODLE). Model testing is performed on user acceptance using a combination of Technology Acceptance Model (TAM) and Hedonic-Motivation System Adoption Model (HMSAM) and the impact of the model in learning using statistical test. The results showed 83% of the learners felt happy with the learning. Meanwhile, the evaluation of the impact on learning outcomes shows that the use of this model is significantly different from traditional learning.

The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic.

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data‐driven knowledge discovery and more sophisticated spatial analysis central to the COVID‐19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county‐level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high‐quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user‐centered design to ground the direction and design of Atlas application development.

Using Web Service Middleware to Smart Smart Multi-Test System in CMOS Image Sensing Devices

The current CMOS image sensor industry has lots of challenges including product life cycle shortening, manufacturing cycle shortening, strategically vertical integrated operation and precisely quality. There are lots of difficulties in customized test system including duplicated function develop, less flexible, and hard to integrate heterogeneous systems. Currently, collect the test data in the test system is easy to make mistakes and causes the poor quality control. In this research, I have designed a web service middleware of software component structure and XML data structure to collect, integrate, and manage device test data. It provides an extendable, flexible, standardized and reusable web service middleware component test software platform. To use this software platform build a CMOS image sensor Smart Multi-Test System, then to integrate MES system, provide functions of automatic collect test process data, automatic hold and release process lot which has abnormal quality. It provide database and file system handling service, XML data format translate handling service, basic data maintain service, test result translate service, quality of production control service and abnormal event handling service. The result of this research, it automatically collects process data to save data entering time and prevent the making mistake in operation, and improve efficiency of operator operation. It also provides a quality control mechanism to improve efficiency including the quality of product and the inspection quality up to 95percent. And it makes the more flexible producing and save 75 percent of the new equipment data format develop time.

WestBox: An object-oriented software component for WEST CODAC

Abstract COntrol, Data Acquisition and Communication (CODAC) real-time software codes are key elements for the operation of a fusion device both for the machine protection and for the optimization of the experiments. In 2013, following the WEST (W -for tungsten- Environment Steady-state Tokamak) upgrade of Tore-Supra, the whole legacy acquisition system has been re-factored. The WEST CODAC framework which inherited the older implementations used over 20 years on TORE Supra has been reorganized into primary software components corresponding to basic technical features, such as Database Access, Inter-process Communications, Real-time Device Management, Timing System Network, Finite-State Machine, and Shared Memory Network. In addition, a new modern middleware component named Westbox has been developed, to simplify developments for native diagnostic upgrades and cross-platform data acquisition hardware and software from guest collaborators.

Virtual Connection: Selective Connection System for Energy-Efficient Wearable Consumer Electronics

Wearable consumer electronics such as smartwatch and smartglasses generally use peer-to-peer (P2P) communications to connect to their companion devices such as smartphones. The recent wearable consumer electronics are equipped with multiple heterogeneous network interfaces, which have different power consumption and transfer bandwidths. To optimize energy efficiency, a network interface must be selected for communication depending on workload or system state. Such a technique is referred to as selective connection. However, the current selective connection systems cannot support the P2P-based wearable devices. We propose a protocol-independent user-level system, called virtual connection, that decouples applications from network-specific operations for selective connections. The proposed virtual connection system consists of user-level API and user-level middleware components. To establish the usefulness of our virtual connection, we present a case study of the selective P2P connection for wearable consumer electronics equipped with Bluetooth and Wi-Fi Direct. We demonstrate that, compared to existing selective connection systems, the P2P-based selective connection system implemented with the virtual connection can reduce the energy consumption of the wearable consumer electronics by 1.26–2.31 times.

RETRACTED ARTICLE: A novel user interaction middleware component system for ubiquitous soft computing environment by using fuzzy agent computing system

Ubiquitous Computing Environment (UCE) has become increasingly crucial for a component-based Middleware for Ubiquitous Soft Computing Environment by using Fuzzy Agent Computing System (USCE–FACS) that can fully exploit interactive service characteristics to support user-centric services in UCE. It has posed some challenges, such as high Memory Consumption and high Component Building Time to users who are hiring services in a heterogeneous environment. To control the UCE for the user's benefit, FACS works non-rudely in an online deep-rooted learning way to get familiar with the user behavior. The point of incorporation of previously mentioned advances is to make more extensive the connection between individuals and data innovation gear through the use of an undetectable system of UCE devices making dynamic computational-environments equipped for fulfilling the users' prerequisites. Due to a large number of available services without ontology and Metadata Repository (MR), it becomes time-consuming for end-users to find appropriate services to satisfy their various needs. To help end-users obtain their desired services, the USCE–FACS proposed, it reduces MC and CBT in a heterogeneous environment. This paper concentrated on the communication from users to devices to enable a general and quick access to accessible element and administrations gave by the UCE.

A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0

The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.

Paradox of Middleware: Innovation Enabler and Creativity Constraint

A critical determinant of the success of digital platforms is the availability of third-party complements. In order to foster the creation of such complements, platforms often rely on boundary resources: software tools and libraries that support and lower the costs of developing complementary innovations. However, while such boundary components are increasingly widespread, little is understood about how these components shape subsequent innovation outcomes and the overall platform generativity. Here, we focus on a particularly important form of boundary resource for digital platforms: Middleware components for software development. We theorize how middleware may shape both the novelty of products being created and their subsequent market value, and propose a number of mechanisms for these effects, for which we provide evidence. We test for these different mechanisms by looking at the development of console games during 6th and 7th generation of gaming consoles. We find that the introduction of licensed middleware such as third-party game and graphics engines lead to the creation of less novel, but more commercially successful products. We attribute this to the fact that middleware allows firms to reallocate resources from developing basic functionality (such as programming how the graphics will be rendered for the target platform), to other elements that make games more valuable and successful on the market (such as story or game mechanic innovation). Our results have implications for how we think about the impact of boundary resources such as middleware on digital platforms, and recombinant innovation more broadly.

Paradox of Middleware: Innovation Enabler and Creativity Constraint

A critical determinant of the success of digital platforms is the availability of third-party complements. In order to foster the creation of such complements, platforms often rely on boundary resources: software tools and libraries that support and lower the costs of developing complementary innovations. However, while such boundary components are increasingly widespread, little is understood about how these components shape subsequent innovation outcomes and the overall platform generativity. Here, we focus on a particularly important form of boundary resource for digital platforms: Middleware components for software development. We theorize how middleware may shape both the novelty of products being created and their subsequent market value, and propose a number of mechanisms for these effects, for which we provide evidence. We test for these different mechanisms by looking at the development of console games during 6th and 7th generation of gaming consoles. We find that the introduction of licensed middleware such as third-party game and graphics engines lead to the creation of less novel, but more commercially successful products. We attribute this to the fact that middleware allows firms to reallocate resources from developing basic functionality (such as programming how the graphics will be rendered for the target platform), to other elements that make games more valuable and successful on the market (such as story or game mechanic innovation). Our results have implications for how we think about the impact of boundary resources such as middleware on digital platforms, and recombinant innovation more broadly.

PICO: A platform independent communications middleware for heterogeneous devices in smart grids

This paper presents a data-centric middleware responsible for real-time communication and data storage in smart grids. The middleware offers a high level programming model that provides ways of storing/getting information from/to the grid and encrypts messages thus providing a secure message exchange. The design has taken into account the heterogeneity of devices, software platforms and stakeholders involved in this kind of Cyber-physical System (CPS). A modular vision is followed in such a way that middleware components can be easily adapted to different platforms and a simple data interface is provided by using REST (Representational State Transfer) web services and a high level asynchronous API. The middleware has been used in the context of a European project (e-balance) where soft real-time requirements, security and low capacity devices were some of the requirements. The demonstration scenarios are detailed in this paper together with the validation tests that show that the use of this programming abstraction is feasible.

PanDA and RADICAL-Pilot Integration: Enabling the Pilot Paradigm on HPC Resources

PanDA executes millions of ATLAS jobs a month on Grid systems with more than 300,000 cores. Currently, PanDA is compatible only with few high-performance computing (HPC) resources due to different edge services and operational policies; does not implement the pilot paradigm on HPC; and does not dynamically optimize resource allocation among queues. We integrated the PanDA Harvester service and the RADICAL-Pilot (RP) system to overcome these limitations and enable the execution of ATLAS, Molecular Dy-namics and other workloads on HPC resources. This paper offer two main con-tributions: (1) introducing PanDA Harvester and RADICAL-Pilot, two systems independent developed to support high-throughput computing (HTC) on high-performance computing (HPC) infrastructures; (2) describing the integration between these two systems to produce a middleware component with unique functionalities, including the concurrent execution of heterogeneous workloads on the Titan OLCF machine. We integrated Harvester and RP by prototyping a Next Generation Executor (NGE) to expose RP capabilities and manage the execution of PanDA workloads. In this way, we minimized the reengineering of the two systems, allowing their integration while being in production.

A parallel unidirectional coupled DEM-PBM model for the efficient simulation of computationally intensive particulate process systems

The accurate modeling of the physics underlying particulate processes is complicated and requires significant computational capabilities to solve using particle-based models. In this work, a unidirectional multi-scale approach was used to model the high shear wet granulation process. A multi-dimensional population balance model (PBM) was developed with a mechanistic kernel, which in turn obtained collision data from the discrete element modeling (DEM) simulation. The PBM was parallelized using a hybrid OpenMP+MPI approach. The DEM simulations were performed using LIGGGHTS, which was parallelized using MPI. Speedups of approximately 14 were obtained for the PBM simulations and approximately 12 for the DEM simulations. The uni-directional coupling of DEM to PBM was performed using middle-ware components (RADICAL-Pilot) that did not require modifications of the DEM or PBM codes, yet supported flexible execution on high-performance platforms. Results demonstrate scaling from 1 to 128 cores for the PBM and up to 256 cores for the DEM. The proposed method, implementations and middle-ware enable the modeling of high shear wet granulation process faster than existing approaches in literature.