Banage T. G. S. Kumara

Web-Service Clustering with a Hybrid of Ontology Learning and Information-Retrieval-Based Term Similarity

Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.

Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. In this paper, the authors present a method that first adopts ontology learning to generate ontologies via the hidden semantic patterns existing within complex terms. If calculating similarity using the generated ontology fails, it then applies an information-retrieval-based method. Another important issue is identifying the most suitable cluster representative. This paper proposes an approach to identifying the cluster center by combining service similarity with term frequency—inverse document frequency values of service names. Experimental results show that our term-similarity approach outperforms comparable existing approaches. They also demonstrate the positive effects of our cluster-center identification approach.

Ontology-Based Workflow Generation for Intelligent Big Data Analytics

Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an industry-proven way to build predictive analytics models across the enterprise. However, the manual process in CRISP-DM hinders faster decision making on real-time application for efficient data analysis. In this paper, we present an approach to automate the process using Automatic Service Composition (ASC). Focusing on the planning stage of ASC, we propose an ontology-based workflow generation method to automate the CRISP-DM process. Ontology and rules are designed to infer workflow for data analytics process according to the properties of the datasets as well as user needs. Empirical study of our prototyping system has proved the efficiency of our workflow generation method.

Intelligent Big Data Analysis Architecture Based on Automatic Service Composition

Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An automated real-time and online analytical process is the most cognitive solution. Therefore it needs state of the art approach to overcome barriers and concerns currently facing by the Big Data industry. In this paper we proposed a novel architecture to automate data analytics process using Nested Automatic Service Composition (NASC) and CRoss Industry Standard Platform for Data Mining (CRISP-DM) as main based technologies of the solution. NASC is well defined scalable technology to automate multi-disciplined problems domains. Since CRISP-DM also a well-known data science process which can be used as innovative accumulator of multi-dimensional data sets. CRISP-DM will be mapped with Big Data analytical process and NASC will automate the CRISP-DM process in an intelligent and innovative way.

Clustering and Spherical Visualization of Web Services

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.

Big Data Analytic Service Discovery Using Social Service Network with Domain Ontology and Workflow Awareness.

In the era of Big Data, data analysis gives strong competition power to enterprises. As services for Big Data Analysis (BDA) become prevalent, analysis services with intelligence and autonomy using automatic service composition show very bright prospects in the BDA market. Service composition consists of four stages: workflow generation, discovery, selection, and execution. In this paper, we propose a novel service discovery approach that considers two key concerns in the discovery domain towards better quality as well as effective service composition. BDA services are fine grained according to the domain and functional behaviors. The services need a domain context-aware and precision-guided discovery approach. Therefore, we propose domain ontology-based service discovery. It is mainly focused on the BDA domain for precise service discovery considering all behavioral signatures between queries and services. As for the second concern, components in composed services depend greatly on each other in situations such as workflow for data analysis. We show that linking services together considering sociability or user preference gives better discovery performance. We propose a Linked Social Service Network (LSSN) with multiple feature attribute-based service discovery for BDA. Our approach combines two advantages, the precision and sociability of Web services. The experimental results show that both of these methods perform well based on their perspectives, better than previous approaches.

Cluster-Based Web Service Recommendation.

The concept of Web services has become a widely applied paradigm in research and industry, with the number of services published on the Internet increasing rapidly over the last few years. Thus, service recommendation is becoming a challenging and time-consuming task due to large search space. Organizing the Web services into clusters is a one of very efficient approach for search space pruning process. In this paper, we proposed cluster-based service recommendation approach. User may want to interact with services that have similar functionalities, which they used to interact. Thus, we consider semantic similarity between services as one factor in the clustering process. Further, user may be interesting to identify the appropriate services to generate value-added service. Thus, we consider the association between services as the second factor. Our approach recommend services for currently invoked service using the generated clusters and services with better QoS values selected by a filtering process. Experimental results show that our approach works effectively.

Calculating web service similarity using ontology learning with machine learning

The Web is a popular, easy and common way to propagate information today and according to the growth of the Web, Web service discovery has become a challenging task. Clustering Web services into similar clusters through calculating the semantic similarity of Web services is one way for overcome this issue. Several methods are used for current similarity calculation process such as knowledge based, information-retrieval based, text mining, ontology based and context-aware based methods. Through this paper, present a method for calculating Web service similarity using both ontology learning and machine learning that uses a support vector machine for similarity calculation in generated ontology instead of edge count base method. Experimental results show that our hybrid approach of combining ontology learning and machine learning works efficiently and give accurate results than previous two approaches.

Context-Aware Filtering and Visualization of Web Service Clusters

Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.

Linked Data-Based Service Publication for Service Clustering

In this paper, we propose an approach to publish services based on Linked data principles and discover services by service cluster with visualization for reducing the using thresholds. First, we propose Linked social service which is published on the open web by following Linked data principles with social link, then, a spatial clustering algorithm is proposed to enable visualization for reducing the using thresholds. Finally, experiment is conducted to show the effectiveness of our proposed approach.