Service Composition Research Articles

User perception as a factor for improving Trustworthiness in e-commerce systems

The User Interface (UI) is the first artefact that the user interacts with while developing a sense of trust that motivates him to use software applications more effectively. A badly designed UI can deceive users and bring the system into disrepute. Trustworthiness in UI is mandatory, as a poorly implemented UI can lead to the user misusing the system and jeopardizing the expected result. Trust in computational systems involves not only technical aspects, such as computational infrastructure, storage space, and service composition, but also aspects of Human-Computer Interaction (HCI). While technological aspects have received considerable attention, there are few research on human-computer interaction in terms of trust. This paper describes a way for assessing a system’s trustworthiness based on the user’s perception. The approach relies on a quality model to aggregate interface quality criteria in order to get a trustworthiness score. Three sets of experiments involving more than 300 individuals were carried out to validate the suggested methodology. A comparison was made between the trustworthiness score obtained through the methodology and the answers to open questions obtained through the users’ questionnaires. The results were consistent, and statistical analysis corroborated the positive assessment. Based on these results, examples of improvements were developed to highlight the usefulness of the approach for developing more trustworthiness interfaces.

UHI based Healthcare Management System through Digital Transformation

The Unified Health Interface (UHI) represents a pivotal initiative within the Indian healthcare landscape, offering an open and interoperable IT ecosystem tailored for modern and innovative health service delivery. In response to the challenges posed by the COVID-19 pandemic, UHI emerges as a strategic solution aimed at ensuring enhanced accessibility and quality of healthcare services. Through its seamless interface, UHI facilitates various functionalities including online search, lab test bookings, virtual medical consultations via video, voice, or chat, digital transfer of health records, and secure digital payments. This paper explores the transformative potential of UHI in addressing the evolving needs of healthcare delivery in India. By fostering ease of application and improving access to quality healthcare services, UHI stands as a cornerstone in the nation's healthcare infrastructure. The emergence of enterprise- and national-level healthcare systems underscores the growing demand for comprehensive and cost-effective public healthcare solutions. Leveraging classic telecommunications and internet services such as presence, voice, video, and messaging, along with service composition solutions and enterprise networks, UHI lays the groundwork for unified health applications. This research delves into the implications of UHI in driving healthcare accessibility, affordability, and efficiency, while also examining its role in shaping the future trajectory of healthcare delivery in India. By elucidating the integration of UHI within existing healthcare frameworks, this paper provides valuable insights into the transformative potential of digital technologies in revolutionizing public health services.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Mechanical and Thermal Analysis of Duroplastic Matrix Composites over a Range of Temperatures.

It is commonly acknowledged that polymer composites in service are often subjected to not only intricate mechanical loads but also harsh environmental conditions. The mechanical and thermal properties of five particular composites are explored here. The composites are composed of laminates of glass cloth type "E" sheet infilled with a duroplastic matrix. This is a thermoset polymer-epoxy resin with different molecular weights. The composites were fabricated by IZOERG company, which is based in Poland. The final articles were 1.5 mm thick by 60 cm long and 30 cm wide, with the glass layers arranged parallel to the thickness. Young's modulus and tensile strength were measured at room temperature. Using the thermal analysis of dynamic mechanical properties (DMTA), the values of the storage modulus and the loss modulus were determined, and the damping factor was used to determine the glass transition temperature (Tg). It was revealed that the nature of changes in the storage modulus, loss modulus, and damping factor of composite materials depends on the type of epoxy resin used. Thermal expansion is a crucial parameter when choosing a material for application in cryogenic conditions. Thanks to the TMA method, thermal expansion coefficients for composite materials were determined. The results show that the highest value of the coefficient of thermal expansion leads the laminate EP_4_2 based on brominated epoxy resin cured with novolac P. Duroplastic composites were characterized at cryogenic temperatures, and the results are interesting for developing cryogenic applications, including electric motors, generators, magnets, and other devices.

A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering

Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.

A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm

The concept of a “Smart City” emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively.

Telehealth common in MH clinics, but varies geographically

Although studies have examined the use of telehealth throughout the pandemic, little is known about the availability and composition of mental health telehealth services, such as appointment access and the mental health conditions that are treated, according to RAND researchers who found differences based on geography and types of services offered.

Assessing the Influence of Land Use and Land Cover (LULC) Changes on Hydrological Response of the Timah-Tasoh Reservoir

Changing patterns in land use and land cover (LULC) is potentially due to development pressure. Due to the increase in LULC change, it alters natural ecosystems and changing the composition of the natural ecosystem services. This study provides evidence to support the hypothesis that LULC can influence both inflow and discharge. The objectives of the study were to measure the impact of LULC change on inflow of the Timah-Tasoh reservoir. The study area land cover was reclassified into five categories which included agriculture, built-up areas, forest, vacant land, and water bodies. Based on the effect of LULC on hydrological response, it is observed that the catchment is not sensitive to changes in LULC. Two mains of LULC scenarios were used including deforestation and afforestation. With an increase in CN to 89.26 (deforestation), the inflow rate increases by 2% (342.20 m3/s) compared to the present inflow rate. Meanwhile, the inflow rate will decrease to 0.21% (334.80 m3/s) if the CN value is 80.02 (afforestation). Thus, by using this study’s results, a better method and parameters compatible with another hydrological modelling can be developed for future study.

An optimization method of cloud manufacturing service composition based on matching-collaboration degree

An optimization method of cloud manufacturing service composition based on matching-collaboration degree

Diverse Top-k Service Composition for Consumer Electronics With Digital Twin in MEC

Diverse Top-k Service Composition for Consumer Electronics With Digital Twin in MEC

Verification of transaction-aware web services composition through formal methods

Verification of transaction-aware web services composition through formal methods

An improved ACO based service composition algorithm in multi-cloud networks

In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible and intelligent service composition algorithms are urgently need to study. Based on this, we propose a service composition strategy under the multi-cloud environment, and we propose an ant colony optimization algorithm (ACO) based on the multi-pheromone mechanism to optimize the quality of service (QoS). To avoid the occurrence of local optima, we further introduce the mutation operation of the genetic algorithm. Finally, the simulation results show that the proposed algorithm can achieve better QoS parameters such as latency and response time while ensuring the stability of services.

A multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning in hybrid cloud manufacturing

Cloud manufacturing (CMfg) is a new network manufacturing mode, where one of the essential issues is service composition and optimal selection (SCOS), attracting a wide range of scholars' attention and research. At present, scholars at home and abroad mainly focus on studying the SCOS model in a single type of cloud environment, which integrates and optimizes large group enterprises' internal resources and capabilities. However, since the characteristics and objectives of the SCOS problem are different in different cloud environments (private, public, and hybrid cloud), there are many dynamic switching scenarios of manufacturing resources in different cloud environments. At this time, SCOS models in a single type of cloud environment are no longer applicable. Thus, how to realize the maximization of multi-stakeholder benefits through the adaptive partitioning of manufacturing resources in different cloud environments is an urgent problem to be solved. To fill the gap, a multi-objective service composition optimization method considering multi-user benefit and adaptive resource partitioning (SCOS-MUB-ARP) is proposed for quickly obtaining the optimal service composition that maximizes the benefits of multi-stakeholder in hybrid cloud manufacturing. To address the SCOS-MUB-ARP, a multi-objective optimization algorithm based on a combination of the NSGA-II algorithm and the mayfly algorithm (MMA-NSGA2) with several optimization strategies is presented, which contains several problem-specific optimization strategies. Numerical experiments and application cases show the effectiveness of the proposed MMA-NSGA2 in optimizing and balancing multi-objectives, compared with some well-known algorithms.

Multi-Objective Service Composition in Uncertain Environments

Web services have the potential to offer the enterprises with the ability to compose internal and external business services in order to accomplish complex processes. Service composition then becomes an increasingly challenging issue when complex and critical applications are built upon services with different QoS criteria. However, most of the existing QoS-aware service composition techniques are simply based on the assumption that multiple QoS criteria, no matter whether these multiple criteria are conflicting or not, can be combined into a single criterion to be optimized, according to some utility functions. In practice, this can be very difficult as these utility functions or weights are not well-known a priori. In addition, the existing approaches are designed to work in certain environments, where the QoS parameters are well-known in advance. These approaches will render fruitless when facing uncertain and dynamic environments, e.g., cloud environments, where no prior knowledge of the QoS parameters is available. In this paper, two novel multi-objective approaches are proposed to handle QoS-aware Web service composition with conflicting objectives and various restrictions on the quality matrices. The proposed approaches use reinforcement learning in order to deal with the uncertainty characteristics inherent in open and dynamic environments. Experimental results reveal the ability of the proposed approaches to find a set of Pareto optimal solutions, which have the equivalent quality to satisfy multiple QoS-objectives with different user preferences.

Capability Reconciliation for a CSP Approach to Virtual Device Composition

The dynamic composition of systems of networked appliances, or virtual devices, enables users to generate complex, strong, specific systems. Leading mobile ad hoc network (MANET)-based composition schemes currently make use of service discovery mechanisms that depend on periodic service advertising by controlled broadcast. The result is the unnecessary depletion of node resources such as battery and processing power. The dynamic and ad hoc nature of the discovery and composition of services is also not addressed by current schemes; this inevitably leads to capability differences that need to be reconciled when the input of one service is not compatible with the output of another. Our approach addresses the distributed constraint satisfaction problem in virtual device composition in MANETs; our simulation shows its effectiveness and efficiency.

Services in public long-term care insurance in Japan.

In Japan, a public long-term care insurance system was launched in 2000. Residents can receive long-term care according to their care needs, as determined by a nationally standardized certification system. The present report describes the details of the long-term care services covered by public insurance. The Long-Term Care Insurance Act categorizes services into three major types: in-home, nursing-home, and community-based long-term care services. In-home care services include visiting, commuting, short-stay, and other services. Welfare, health, and medical facilities provide nursing-home care services for the elderly. Community-based care services were categorized into visiting, commuting, nursing-home, and composite services.

Game Theory Based Dynamic Event-Driven Service Scheduling in Cloud Manufacturing

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

A Hybrid MCDM Model for Service Composition in Cloud Manufacturing using O-TOPSIS

A Hybrid MCDM Model for Service Composition in Cloud Manufacturing using O-TOPSIS

Web Service Composition in Mobile Environment: A survey of Techniques

Web Service Composition in Mobile Environment: A survey of Techniques

Using the SCRM Method to Repair a Damaged Planning Graph for Service Composition

Using the SCRM Method to Repair a Damaged Planning Graph for Service Composition