Dynamic adaptation of service compositions with variability models

Alférez, Germán H.; Pelechano, Vicente; Mazo, Raúl; Salinesi, Camille; Díaz, Daniel

doi:10.1016/j.jss.2013.06.034

Cited by 109 publications

(90 citation statements)

References 72 publications

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“…Alferez, Pelechano, Mazo, Salinesi and Diaz [35] propose a framework that uses semantically rich variability models at runtime to support the dynamic adaptation of service compositions. They propose that service compositions be abstracted as a set of features in a variability model.…”

Section: B Seco Architecture and Conceptmentioning

confidence: 99%

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Brisebois¹,

Abran²,

Nadembéga³

2017

IJITCS

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Abstract-Information systems need to be more flexible and to allow users to find content related to their context and interests. Metadata harvesting and metadata enrichments could represent a way to help users to find content and events according to their interests. However, metadata are underused and represents an interoperability challenge. This paper presents a new framework, called SMESE, and the implementation of its prototypes that consists of its semantic metadata model, a mapping ontology model and a user interest affinity model. This proposed framework makes these models interoperable with existing metadata models.SMESE also proposes a decision support process supporting the activation and deactivation of software features related to metadata. To consider context variability into account in modeling context-aware properties, SMESE makes use of an autonomous process that exploits context information to adapt software behavior using an enhanced metadata framework. When the user chooses preferences in terms of system behavior, the semantic weight of each feature is computed. This weight quantifies the importance of the feature for the user according to their interests. This paper also proposed a semantic metadata analysis ecosystem to support data harvesting according to a metadata model and a mapping ontology model. Data harvesting is coupled with internal and external enrichments. The initial SMESE prototype represents more than 400 millions of relationships (triplets). To conclude, this paper also presents the design and implementation of different prototypes of SMESE applied to digital ecosystems.Index Terms-Metadata, metadata enrichment, metadata model, ontology, semantic metadata enrichment, software ecosystem.

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Section: B Seco Architecture and Conceptmentioning

confidence: 99%

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Brisebois¹,

Abran²,

Nadembéga³

2017

IJITCS

View full text Add to dashboard Cite

show abstract

“…therefore, we make the difference between the internal behavior of the system, the system interactions with its environment, and the organizational context of the system. The authors in [20] propose a framework (internal system) that provides models, tools and objects to support the dynamic adaptation of service compositions. They introduce mechanisms to express where and how service compositions can be adapted.…”

Section: The Structure Facetmentioning

confidence: 99%

“…The decisions making that are taken should lead to a real choice or product. For example, Alférez et al in [20] present a variability model that expresses decisions leading to different variations at runtime.…”

Section: Knowledge Facetmentioning

confidence: 99%

“…Reusability of individual services and composition of services are determined by taking into consideration the different contexts [17] [18]. In [20] and [31] authors provided a formalism to define the context and introduced mechanisms to adapt the service composition at runtime. Also, they have provided an infrastructure that detects changes in context.…”

Section: The Descriptive Variabilitymentioning

confidence: 99%

“…So, in the approach [20], few intentions can be captured. The non-functional attribute addressed in this approach, indicates the error situations, performance and quality of service.…”

Section: Alférez Et Almentioning

confidence: 99%

See 2 more Smart Citations

A Proposal for a Comparison Framework for the Management of Variability in Web Services Composition

Jamouss¹,

Bahri²

2017

JSW

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Service Oriented Architecture (SOA) is currently considered as one of the most advanced means to realize quickly modular and flexible applications. A key concern for flexibility in SOA is the variability. Several works on variability were conducted. However, few studies have proposed classifications on the variability in the composition of web services. In this article, we propose an original approach to compare the management of variability in web services composition. This approach is based on a framework that considers the variability according to four dimensions namely the form, content, purpose, and life cycle. Each dimension is defined by a set of attributes that characterizes a particular facet of the variability.

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An automatic self‐adaptation framework for service‐based process based on exception handling

Wang

Feng

Huang

et al. 2016

Concurrency and Computation

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Summary For service‐based process, there is a frequent failure when it runs under a loose‐coupled and uncertain environment, such as mobile Internet. The runtime self‐adaptation mechanism is urgently needed to automatically achieve the goal of a process. Therefore, this article presents a self‐adaptation framework to automatically monitor and self‐adapt to various failures. On the basis of the presented self‐adaptation exceptions framework, the service‐based process is automatically extended with these exception definitions. In particular, the local self‐adaptation exceptions monitor the failures over each web service instance, whereas the global self‐adaptation exceptions are designed to explore the process‐level failures. For each exception, the self‐adaptation strategy is dynamically generated according to the running environment. Finally, we implement the prototype system running on the Apache ODE. The experimental results show that our framework can automatically recover a process instance from various failures, and the proposed approach can identify more failure types than previous works and apparently improve the running success rate of process instances.

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Dynamic adaptation of service compositions with variability models

Cited by 109 publications

References 72 publications

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

A Proposal for a Comparison Framework for the Management of Variability in Web Services Composition

An automatic self‐adaptation framework for service‐based process based on exception handling

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