Towards Semantic Web Services Density Clustering Technique

Allali, Naoufal El; Fariss, Mourad; Asaidi, Hakima; Bellouki, Mohamed

doi:10.1007/978-3-030-73882-2_49

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…Furthermore, unlike existing methods [20] that create matchmaking ontologies for discovery, our approach does not require such intermediary steps. For validation purposes, our work utilized the relevance file provided for the OWLS dataset in Phase 3 instead of employing alternative methods [29][30][31][32][33] to find relevant services, thereby enhancing the reliability of the framework. Additionally, we considered the complete OWLS-TC dataset rather than using its subset to generate KGSWS and further for service discovery.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, our approach, which seamlessly integrates the advantages of in depth querying over Knowledge Graph, overcame this bottleneck, leading to a more streamlined and efficient service discovery process. An important observation in our results was that while some subsequent studies [29][30][31][32] explored machine learning-based classification techniques, none had harnessed the potential of the Knowledge Graph within this domain.…”

Section: ) Analysis Ofmentioning

confidence: 96%

“…Nevertheless, the challenge of semantic matching persisted, particularly for complex concepts and relations within domain ontologies. Working on the same OWL-S dataset, the work in [29] have used unsupervised learning methods like DBSCAN clustering to cluster the services semantically closed and thereby finding the semantically closed cluster to user requirement using Latent Semantic Analysis (LSA). An ensemble model based approach in [30] use decision tree and logistic regression for service classification and recommendation of top-10 services.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…However, with advanced machine learning methods, the overall accuracy of SWS discovery has improved. However, most of these works [29][30][31][32] focused on a subset of domains within the OWLS collection, and none of the works have implemented an automatic querying method for user queries. Instead of using the relevance file provided for OWLS-TC as per user queries, different methods have been employed to compute evaluation metrics.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

Thapar,

Sharma

2024

IJACSA

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In the era of big data, Knowledge Graphs (KGs) have become essential tools for managing interconnected datasets across various domains. This paper introduces a novel RDF (Resource Description Framework) based Knowledge Graph of Semantic Web Services (KGSWS), designed to enhance service discovery. Leveraging the versatile SPARQL query language, the framework facilitates precise querying operations on KGSWS, enabling customized service matching for user queries. Through comprehensive experimentation and analysis, notable improvements in accuracy (69.75% and 90.01%) and rapid response times (0.61s and 1.57s) across two semantic search levels are demonstrated, validating the efficacy of the approach. Furthermore, research questions regarding the interlinking of ontologies, methods for formulating automatic queries, and efficient retrieval of services are addressed, offering insights into future avenues for research. This work represents a significant advancement in the domain of semantic web services, with potential applications across various industries reliant on efficient service identification and integration. Future phases of research will focus on logical inference and the integration of machine learning-based graph embedding models, promising even greater strides in knowledge discovery within the KGSWS framework, thus reshaping the domain of semantic web services.

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Section: Discussionmentioning

confidence: 99%

Section: ) Analysis Ofmentioning

confidence: 96%

Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

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Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

Thapar,

Sharma

2024

IJACSA

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Boosting the visibility of services in microservice architecture

Tokmak,

Akbulut,

Catal

2023

Cluster Comput

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Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice’s Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

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Towards Semantic Web Services Density Clustering Technique

Cited by 2 publications

References 19 publications

Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

Q-KGSWS: Querying the Hybrid Framework of Knowledge Graph and Semantic Web Services for Service Discovery

Boosting the visibility of services in microservice architecture

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