Architecture for Web Services Filtering and Clustering

Abramowicz, Witold; Haniewicz, Konstanty; Kaczmarek, Monika; Zyskowski, Dominik

doi:10.1109/iciw.2007.19

Cited by 29 publications

(26 citation statements)

References 9 publications

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“…In particular, with the help of the co-occurrence of the terms appearing in the inputs and outputs, in the names of the operations and in the descriptions of web services, the similarity search approach employs the agglomerative clustering algorithm for clustering these terms to the meaningful concepts. Arbramowicz et al [9] proposes architecture for web services filtering and clustering. The service filtering is based on the profiles representing users and application information, which are further described through Web Ontology Language for Services (OWL-S).…”

Section: Related Workmentioning

confidence: 99%

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Analysis of Dynamic Web Services: Towards Efficient Discovery in Cloud

Ahmad¹,

Sarkar

2016

MJCS

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Web services are very dynamic as they can be added, modified and deleted from the repository on the fly. Discovering web services for service composition have special requirements. In the service composition, service requirement is not only to satisfy its functional specification but it also needs to ensure that the selected composing services are compatible with respect to their input/output structure and its associated data types. Web Service community [1] organized the functionally similar services together in a group. But one important drawback of using web service community in the service composition is that it requires further analysis of the discovered service communities for selecting appropriate service which can be composed based on its input/output structure and data types. This paper proposes data structures, which can be used to create web service family [2] by filtering not only on the basis of functionality but also on the basis of its input/output structure, which are required for composing services with one another. In order to speed up the web service family creation process, the creation of web service family is divided in two phases. The first phase of the proposed approach introduces a novel concept of business family and dynamic business web service (DBWS) tree, which are used to organize all web services in the tree structure on the basis of their business context and input/output structure. In the second phase, the concept of DBWS tree is used further to create the web service families. The DBWS resembles the classification and organization of businesses in NAICS. The DBWS tree can be used very efficiently for creating web service families, which in turn can be used for selecting service efficiently in service composition.

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Section: Related Workmentioning

confidence: 99%

“…One of the wide spread approaches used service discovery is clustering [8,9,10]. The clustering methodology is a technology that transforms a complex problem into a series of simpler ones, which can be handled more easily.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Dynamic Web Services: Towards Efficient Discovery in Cloud

Ahmad¹,

Sarkar

2016

MJCS

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“…In [1], the authors proposed an architecture for Web services filtering and clustering. The service filtering mechanism is based on user and application profiles that are described using OWL-S (Web Ontology Language for Services).…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays, we are moving from web of data to web of services as the number of UDDI Business Registries (URBs) is increasing. Moreover, the number of hosts 1 http://www.w3.org/standards/webofservices that offer available web services is also increasing significantly. Consequently, discovering services which can match with the user query is becoming a challenging and an important task.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the fact that the functional requirements (keywords) are often described by natural language. To enrich web service description, several Semantic Web methods and tools are developed, for instance, the authors of [10], [23], [1] use ontology to annotate the elements in web services. Nevertheless, the creation and maintenance of ontologies may be difficult and involve a huge amount of human effort [3], [14].…”

Section: Introductionmentioning

confidence: 99%

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Correlated Topic Model for Web Services Ranking

Aznag¹,

Quafafou²,

Jarir³

2013

IJACSA

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Abstract-With the increasing number of published Web services providing similar functionalities, it's very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn).

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Semantic link‐based constructing of multi‐dimensional service resource space for collaboration

Zhao

2012

Concurrency and Computation

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Automatic service collaboration calls for the development of semantically structured service resource space to maximize the utility of Web services. Semantic links contain rich semantic information that may indicate important relationships among services. We provided an effective method for constructing multi‐dimensional service resource space based on semantic links for service collaboration, in which similar and related semantic relationship between services are considered. We first clustered services with similar and related relations on the basis of a hierarchical structure respectively and then took advantage of the resource space model to construct multi‐dimensional service resource space. Finally, experimental results show the effectiveness of the method. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.

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Architecture for Web Services Filtering and Clustering

Cited by 29 publications

References 9 publications

Analysis of Dynamic Web Services: Towards Efficient Discovery in Cloud

Analysis of Dynamic Web Services: Towards Efficient Discovery in Cloud

Correlated Topic Model for Web Services Ranking

Semantic link‐based constructing of multi‐dimensional service resource space for collaboration

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