Automatic Service Composition with Heterogeneous Service-Oriented Architectures

Álamo, José M. Reyes; Yang, Hen‐I; Wong, Johnny; Chang, Carl K.

doi:10.1007/978-3-642-13778-5_2

Cited by 9 publications

(16 citation statements)

References 7 publications

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“…Apart from the projects presented in Table 3, We have also investigated other smart home applications in this domain such as Aura [15], MavHome [16], Gaia [17] and MISS [18] projects in order to define state of the art. Also based on comparisons given in Table 3, we can draw out some state of the art solutions below.…”

Section: Methodsmentioning

confidence: 99%

Complex Event Processing Using IOT Devices Based on Arduino

Kaya¹,

Cetin-Kaya²

2017

IJCCSA

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Section: Methodsmentioning

confidence: 99%

Complex Event Processing Using IOT Devices Based on Arduino

Kaya¹,

Cetin-Kaya²

2017

IJCCSA

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“…In the next step, we inspected the languages used to describe the functionalities of services. Only 13 studies described services using WSDL (6 articles, [71,72,[75][76][77]87]), REST (4 articles, [68,69,96,99]), and BPEL (3 articles, [89,100,101]), while 25 studies did not disclose details about their service description languages. To facilitate the composition activities, services were semantically annotated in 15 studies (39.47%), unannotated in 7 studies (18.42%), and unspecifc in 16 studies (42.10%).…”

Section: Services and Domains Of Compositionmentioning

confidence: 99%

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

Namoun

Tufail

Nawaz

et al. 2023

Computational Intelligence and Neuroscience

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Integrating smart heterogeneous objects, IoT devices, data sources, and software services to produce new business processes and functionalities continues to attract considerable attention from the research community due to its unraveled advantages, including reusability, adaptation, distribution, and pervasiveness. However, the exploitation of service-oriented computing technologies (e.g., SOC, SOA, and microservice architectures) by people with special needs is underexplored and often overlooked. Furthermore, the existing challenges in this area are yet to be identified clearly. This research study presents a rigorous literature survey of the recent advances in service-oriented composition approaches and solutions for disabled people, their domains of application, and the major challenges, covering studies published between January 2010 and October 2022. To this end, we applied the systematic literature review (SLR) methodology to retrieve and collate only the articles presenting and discussing service composition solutions tailored to produce digitally accessible services for consumption by people who suffer from an impairment or loss of some physical or mental functions. We searched six renowned bibliographic databases, particularly IEEE Xplore, Web of Science, Springer Link, ACM Library, ScienceDirect, and Google Scholar, to synthesize a final pool of 38 related articles. Our survey contributes a comprehensive taxonomy of service composition solutions, techniques, and practices that are utilized to create assistive technologies and services. The seven-facet taxonomy helps researchers and practitioners to quickly understand and analyze the fundamental conceptualizations and characteristics of accessible service composition for people with disabilities. Key findings showed that services are fused to assist disabled persons to carry out their daily activities, mainly in smart homes and ambient intelligent environments. Despite the emergence of immersive technologies (e.g., wearable computing), user-service interactions are enabled primarily through tactile and speech modalities. Service descriptions mainly incorporate functional features (e.g., performance, latency, and cost) of service quality, largely ignoring accessibility features. Moreover, the outstanding research problems revolve around (1) the unavailability of assistive services datasets, (2) the underspecification of accessibility aspects of disabilities, (3) the weak adoption of accessible and universal design practices, (4) the abstraction of service composition approaches, and (5) the rare experimental testing of composition approaches with disabled users. We conclude our survey with a set of guidelines to realize effective assistive service composition in IoT and cloud environments. Researchers and practitioners are advised to create assistive services that support the social relationships of disabled users and model their accessibility needs as part of the quality of service (QoS). Moreover, they should exploit AI/ML models to address the evolving requirements of disabled users in their unique environments. Furthermore, weaknesses of service composition solutions and research challenges are exposed as notable opportunities for future research.

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“…Service selection during the composition process is mainly driven by functional quality of service (QoS) criteria, such as efficiency and reliability [ 29 , 30 , 31 ], scalability [ 23 , 32 ], availability [ 33 , 34 ], and response time [ 35 , 36 ]. In [ 29 , 30 ], the authors did not focus only on the efficient delivery of the services or information but also on the trustworthiness and reliability of data from different sources.…”

Section: Introductionmentioning

confidence: 99%

“…Services are grouped using community ontology with semantically similar functionality, whereby the behavior of those services is described in terms of a finite transition system. In another study [ 34 ], the authors presented a framework that supports the availability of heterogeneous services developed in various service-oriented architectures (SOAs). A case study in a smart home environment was conducted to evaluate the feasibility and performance of automated service composition compared with the manual process, and it was limited to OSGi- and SOA-based services.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Namoun

Sen

Tufail

et al. 2022

Sensors

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The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world’s population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

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Automatic Service Composition with Heterogeneous Service-Oriented Architectures

Cited by 9 publications

References 7 publications

Complex Event Processing Using IOT Devices Based on Arduino

Complex Event Processing Using IOT Devices Based on Arduino

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

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