an affinity-driven clustering approach for service discovery and composition for pervasive computing

2006 ◽  
Author(s):  
J. Gaber ◽  
M. Bakhouya
Keyword(s):  
Pervasive Computing ◽  
Service Discovery ◽  
Clustering Approach

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

Secure and Private Service Discovery in Pervasive Computing Environments

2010 ◽  
Vol 2 (3) ◽  
pp. 46-59
Author(s):  
Feng Zhu ◽  
Wei Zhu
Keyword(s):  
Pervasive Computing ◽  
Service Discovery ◽  
Service Providers ◽  
Code Word ◽  
Sensitive Information ◽  
Word Form ◽  
Essential Step ◽  
Pervasive Service ◽  
Computing Environments ◽  
User Centric

With the convergence of embedded computers and wireless communication, pervasive computing has become the inevitable future of computing. Every year, billions of computing devices are built. They are ubiquitously deployed and are gracefully integrated with people and their environments. Service discovery is an essential step for the devices to properly discover, configure, and communicate with each other. Authentication for pervasive service discovery is difficult. In this chapter, we introduce a user-centric service discovery model, called PrudentExposure, which automates authentication processes. It encodes hundreds of authentication messages in a novel code word form. Perhaps the most serious challenge for pervasive service discovery is the integration of computing devices with people. A critical privacy challenge can be expressed as a “chicken-andegg problem”: both users and service providers want the other parties to expose sensitive information first. We discuss how a progressive and probabilistic model can protect both users’ and service providers’ privacy.

scholarly journals A trust-based secure service discovery (TSSD) model for pervasive computing

2008 ◽  
Vol 31 (18) ◽  
pp. 4281-4293 ◽  
Author(s):  
Sheikh I. Ahamed ◽  
Moushumi Sharmin
Keyword(s):  
Pervasive Computing ◽  
Service Discovery

scholarly journals Service Discovery in Agent-Based Pervasive Computing Environments

2004 ◽  
Vol 9 (6) ◽  
pp. 679-692 ◽  
Author(s):  
Olga Ratsimor ◽  
Dipanjan Chakraborty ◽  
Anupam Joshi ◽  
Timothy Finin ◽  
Yelena Yesha
Keyword(s):  
Pervasive Computing ◽  
Service Discovery ◽  
Agent Based ◽  
Computing Environments

Context-Aware Web Service Clustering and Visualization

2020 ◽  
Vol 17 (4) ◽  
pp. 32-54
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Yuichi Yaguchi
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Search Space ◽  
Context Aware ◽  
Specific Context ◽  
Web Service Discovery ◽  
Domain Specific ◽  
Clustering Approach ◽  
Service Clustering

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.

Using Event B to Specify Context Awareness for Service Discovery in Pervasive Environments

2017 ◽  
Vol 8 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Karima Belgharbi ◽  
Mahmoud Boufaida
Keyword(s):  
Pervasive Computing ◽  
Service Provider ◽  
Context Awareness ◽  
Service Discovery ◽  
Service Use ◽  
Dynamic Environment ◽  
Design Phase ◽  
Pervasive Environments ◽  
Mathematical Proofs ◽  
Complex Features

The environments of pervasive computing are open and dynamic. In order to ensure the dynamic discovery of services evolving in a heterogeneous and dynamic environment, specific extensions to WSDL, known as A-WSDL are suggested. These extensions permit to a service provider to define the context of service use and the behavior associated to each change of context. To verify and prove the expected behavior of the suggested discovery protocol in the design phase, the Event-B formalism is adopted. One of the advantages of the Event B formalism is the application of the refining techniques which permit to express complex features by means of mathematical proofs and moves from an abstract specification to a concrete specification by using the Rodin tool which offers a support for the refining and the proofs.

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