Advanced Analytics and Artificial Intelligence Applications

extraordinary, complex, and fascinating natural phenomena. Of particular interest is the intelligent behavior of some animal species to find a solution to solve a problem and maintain the perfect balance of the environment surrounding.

4.Web service classification and clustering consists of selecting and extracting the characteristics of a collection of Web services and discovering the similarities between them to form groups or classes considering those characteristics. Therefore, this phase depends on the results of the previous phases. In

particular, in this chapter a hybrid clustering algorithm based on the Artificial

5. Inference and reasoning represents the supporting mechanisms to exploit and

Web service classification and clustering is a topic addressed form different perspectives such as statistical, stochastic, and novel approaches based on bio-inspired algorithms. Additionally, semantic approaches to describe, discover, and invoke Web services have been studied to propose novel clustering algorithms. In this section a revision of related work is presented considering two trends: reported work that address clustering and classification of Web services and clustering approaches based on bio-inspired algorithms. Table 1 presents the main characteristics of related work. In 2009, Liang et al. [1] proposed a method for Web service categorization considering keywords and semantic relations between elements of the description. Their proposed methodology involves preprocessing WSDL documents, rough

In 2009, Platzer et al. [2] described a scalable approach for clustering very large service repositories. They use a statistical clustering algorithm enhancing a vector

In 2012, Pop et al. [3] presented two approaches for service clustering, one inspired by the behavior of the birds and other inspired by the behavior of ants. They implemented methods to evaluate the semantic similarity between services. In 2013, Du et al. [4] presented an approach for clustering Web services based on functional similarity and refinement of clusters using a concept position vector. In 2014, Wu et al. [5] presented an approach which consists of three modules: data preprocessing, Web service tag recommendation, and Web services clustering. The first module consists of building a content vector formed with nouns, verbs, or adjectives. Authors use different features and different approaches for similarity computation. For content use the normalized Google distance, for data types and messages they use a basic match similarity, and for tag similarity they apply the

In 2014, Prakash and Singh [6] compared the performance of evolutionary algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Artificial Bee Colony for clustering three real and one synthetic data sets. In 2017, Sahoo [7] presented a two-step ABC algorithm for data clustering problems. Authors addressed the three problems of the ABC algorithm such as initial positions of food sources, solution search equation, and abandoned food location. In 2018, Kotekar and Kamath [8] described a Web services clustering approach based on Cat Swarm Optimization (CSO), which emulates the social behavior of cats

Automated Web services clustering is useful to facilitate service search, service discovery, service composition, and service substitution. Of particular interest is the representation of Web services through ontologies because the purpose of this work is the automatic organization of any collection (public or private) of Web service in

ontologies and their semantic enrichment by classification and clustering.

Bee Colony algorithm, the K-means, and Consensus is described.

clustering by labeling Web services with class tag, and fine clustering.

space to support the search of services related to a given query.

utilize the enriched Web service ontologies.

Bio-Inspired Hybrid Algorithm for Web Services Clustering

DOI: http://dx.doi.org/10.5772/intechopen.85200

2. Related work

Jaccard coefficient.

in nature.

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This is the main idea of computation inspired by nature, that is, to imitate the steps that nature has developed and adapt them to find a solution of a problem, thus converting it into a bio-inspired algorithm. This is the main reason for the implementation of an algorithm that exploits the collective intelligence of a Bee Colony as an alternative for clustering. This chapter presents an innovative approach for Web services clustering using a hybrid algorithm based on the Artificial Bee Colony, Kmeans, and Consensus.

The work described in this chapter is part of a research project whose objective is to design and implement a semantic directory of Web services using efficient, fully automated methods to allow the organization, composition, and classification of Web services with a semantic approach.

Figure 1 shows the general architecture of the semantic directory and a methodology to construct and manage the directory. The methodology consists of the following phases:


Figure 1. Semantic directory of Web services.

Bio-Inspired Hybrid Algorithm for Web Services Clustering DOI: http://dx.doi.org/10.5772/intechopen.85200

