Service-Oriented Computing – ICSOC 2008

Bouguettaya, Athman; Krueger, Ingolf; Margaria, Tiziana

doi:10.1007/978-3-540-89652-4

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…With the rapid development of the Internet, the number of Web APIs has increased rapidly in recent years, which has brought great trouble to developers [2]. In this case, mashup technology becomes popular [3]. Mashups can combine Web APIs effectively according to existing web resources [4].…”

Section: Introductionmentioning

confidence: 99%

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

Yang

Qin

et al. 2021

IEEE Access

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With the rapid development of the Internet, the number of Web APIs is increasing. How to recommend accurate and appropriate Web APIs to mashups has become a focus and difficulty in the field of service computing. The existing methods are mainly based on collaborative filtering technology, but these methods have problems such as the data sparsity and cold start, which leads to poor recommendation effects. This paper proposes a service recommendation model based on knowledge graph and collaborative filtering. In this model, the knowledge graph connects the APIs and mashups related information to mine the potential relations between mashups and APIs, hence reducing the impact of data sparsity. All the API entities in the service knowledge graph are embedded into the low-dimensional space through the representation learning algorithm, then the distances between the API vectors are calculated to recommend the related APIs. In addition, in order to solve the cold-start problem of recommending APIs to target mashups that have no APIs usage, the similarities of functional sets extracted from mashups are calculated to recommend APIs for target mashups. At the same time, the model obtains the Mashup-API usage record, using the technology of collaborative filtering to recommend appropriate APIs to target mashups. Finally, the similarities of the above recommended APIs are normalized and sorted to form the final recommendation result. The experimental results show that our proposed model significantly improves the accuracy of service recommendation.

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Section: Introductionmentioning

confidence: 99%

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

Yang

Qin

et al. 2021

IEEE Access

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The Pervasive Nature of Variability in SOC

Khan

Kästner

Köppen

et al. 2011

2011 Frontiers of Information Technology

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Abstract-Service-oriented computing has gained attention from researchers and industry due to various benefits. Enterprises are supported by SOC-based solutions. Enterprise requirements change with the customer needs, market conditions, and variability is often needed in SOC. Research usually focuses at variability on the business process level and ignores variability at other layers. In this paper, we show what kind of variability can occur and how it spans across all layers of SOC. We present some example scenarios to discuss different kinds of variability and their reasons in SOC environments. Finally, we classify variability to show that variability is not an isolated problem in a single layer.

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Integrating Fault-Tolerance Method into Service-Oriented Computing

Alhosban

Hashmi

Malik

et al. 2015

2015 IEEE Symposium on Service-Oriented System Engineering

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Service Oriented Architectures (SOAs) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown (in terms of dependability), how to deliver reliable Web services compositions is a significant and challenging problem. In this paper, we propose a framework for infusing dependability in SOAs through self-healing. We identify a set of high-level exception handling strategies based on the composition components' QoS performances and consumer requirements. Multiple recovery plans are produced and evaluated according to the performance of the included services, to select and execute the best recovery plan. We assess the overall system dependability using the generated plan and the available invocation information of the included services. Experiment results indicate the applicability of our method, and show the improved performance in comparison with similar existing approaches.

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Service-Oriented Computing – ICSOC 2008

Cited by 4 publications

References 9 publications

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

The Pervasive Nature of Variability in SOC

Integrating Fault-Tolerance Method into Service-Oriented Computing

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