Piecemeal Development of Intelligent Applications for Smart Spaces

Ovaska, Eila; Kuusijärvi, Jarkko

doi:10.1109/access.2014.2309396

Cited by 7 publications

(9 citation statements)

References 30 publications

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“…& Enabling infrastructure: the ecosystem infrastructure supports the collaboration and cooperation of ecosystem members, providing the required services and tools (PantsarSyväniemi et al 2012) (Khriyenko 2012;Ruokolainen et al 2011;Ruokolainen and Kutvonen 2009). & Utilization of the ecosystem's assets: the existing ecosystem assets, such as the ecosystem's rules, methods, and practices for service engineering, enable co-innovation and cocreation of the services (Pantsar-Syväniemi et al 2012;Ovaska et al 2012;Ovaska and Kuusijärvi 2014).…”

Section: )mentioning

confidence: 99%

Towards certified open data in digital service ecosystems

2017

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The opportunities of open data have been recently recognized among companies in different domains. Digital service providers have increasingly been interested in the possibilities of innovating new ideas and services around open data. Digital service ecosystems provide several advantages for service developers, enabling the service co-innovation and co-creation among ecosystem members utilizing and sharing common assets and knowledge.The utilization of open data in digital services requires new innovation practices, service development models, and a collaboration environment. These can be provided by the ecosystem. However, since open data can be almost anything and originate from different kinds of data sources, the quality of data becomes the key issue. The new challenge for service providers is how to guarantee the quality of open data. In the ecosystems, uncertain data quality poses major challenges. The main contribution of this paper is the concept of the Evolvable Open Data based digital service Ecosystem (EODE), which defines the kinds of knowledge and services that are required for validating open data in digital service ecosystems. Thus, the EODE provides business potential for open data and digital service providers, as well as other actors around open data. The ecosystem capability model, knowledge management models, and the taxonomy of services to support the open data quality certification are described. Data quality certification confirms that the open data is trustworthy and its quality is good enough to be accepted for the usage of the ecosystem's services. The five-phase open data quality certification process, according to which open data is brought to the ecosystem and certified for the usage of the digital service ecosystem members using the knowledge models and support services of the ecosystem, is also described. The initial experiences of the Software Qual J (2018)

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Section: )mentioning

confidence: 99%

Towards certified open data in digital service ecosystems

2017

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“…• The knowledge repositories are required for storage of the collaboration models, service descriptions and ontologies of service types to support interoperability validation [25][26][27][28].…”

Section: Challenges Of Ecosystem-based Service Requirements Engineeringmentioning

confidence: 99%

“…Different kind of approaches have been suggested, such as for generating a requirements model based on the concepts in the service requirements modeling ontology [52] or establishing a mapping between a requirements specification and ontological elements [53]. A knowledgebased development of intelligent smart spaces is introduced in [27], which support the service innovation and co-creation, exploiting the usage scenario description, the set of use cases that define the specific viewpoint of the usage scenario, and the reusable artefacts, such as ontologies, models, patterns and rules, provided in the knowledge base. In [25,54], an approach for developing intelligent applications/services for smart spaces is introduced that exploits the ontology models, interoperability models and context models for describing self-adaptable services.…”

Section: Utilisation Of Ecosystem's Assetsmentioning

confidence: 99%

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A service requirements engineering method for a digital service ecosystem

Immonen

Ovaska

Kalaoja

et al. 2015

SOCA

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A digital service ecosystem enables value creation and the co-development of services in a value network under a common ecosystem regulation. The ecosystem members are able to focus on their core competences and can strengthen their forces by co-operating; yet remaining able to act independently. However, due to regulated environment, the ecosystem elements-i.e. ecosystem members, capabilities, infrastructure and the existing ecosystem assets-have an influence on digital service engineering, especially in the service requirements engineering phase. The main contribution of this paper is to describe how to specify the requirements of digital services in a digital service ecosystem. To this aim, this paper introduces the basic definitions and elements of the digital service ecosystem, and a scenario-based service requirement engineering (RE) method for the digital service ecosystem. A practical example is given to illustrate the use of the RE method. The collected feedback from the RE method users highlights the user experiences on the advantages and limitations of the proposed method.

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“…Also, the definition of quality requirements for DSEs needs further exploration, and special skills are required in the innovation and requirements analysis, negotiation and specification phases. However, quality ontologies, qualitydriven methods and tool support for attaching quality properties for architectural elements as discussed in [51][52][53][54][55] can aid the quality requirements engineering process.…”

Section: Quality Attributesmentioning

confidence: 99%

A survey of autonomic computing methods in digital service ecosystems

Abeywickrama

Ovaska

2016

SOCA

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Service engineering of digital service ecosystems can be associated with several challenges, such as change and evolution of requirements; gathering of quality requirements and assessment; and uncertainty caused by dynamic nature and unknown deployment environment, composition and users. Therefore, the complexity and dynamics in which these digital services are deployed call for solutions to make them autonomic. Until now there has been no upto-date review of the scientific literature on the application of the autonomic computing initiative in the digital service ecosystems domain. This article presents a review and comparison of autonomic computing methods in digital service ecosystems from the perspective of service engineering, i.e., requirements engineering and architecting of services. The review is based on systematic queries in four leading scientific databases and Google Scholar, and it is organized in four thematic research areas. A comparison framework has been defined which can be used as a guide for comparing the different methods selected. The goal is to discover which methods are suitable for the service engineering of digital service ecosystems with autonomic computing capabilities, highlight what the shortcomings of the methods are, and identify which research activities need to be conducted in order to overcome these shortcomings. The comparison reveals that none of the existing methods entirely fulfills the requirements that are defined in the comparison framework.B Dhaminda B. Abeywickrama

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Piecemeal Development of Intelligent Applications for Smart Spaces

Cited by 7 publications

References 30 publications

Towards certified open data in digital service ecosystems

Towards certified open data in digital service ecosystems

A service requirements engineering method for a digital service ecosystem

A survey of autonomic computing methods in digital service ecosystems

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