Richi Nayak

Web Service Discovery with additional Semantics and Clustering

Due to the lack of semantic descriptions of the Web services, the search results returned by the service registries are effectively inadequate. This paper presents the Semantic Web services Clustering (SWSC) method that extends the semantic representation of services and groups the similar Web services in order to improve the service discovery. The empirical analysis shows the improvement in service discovery with the use of SWSC.

Ontology Mining for Personalized Web Information Gathering

Due to the lack of semantic descriptions of the Web services, the search results returned by the service registries are effectively inadequate. This paper presents the Semantic Web services Clustering (SWSC) method that extends the semantic representation of services and groups the similar Web services in order to improve the service discovery. The empirical analysis shows the improvement in service discovery with the use of SWSC.

Fast and effective clustering of XML data using structural information

This paper presents the incremental clustering algorithm, XML documents Clustering with Level Similarity (XCLS), that groups the XML documents according to structural similarity. A level structure format is introduced to represent the structure of XML documents for efficient processing. A global criterion function that measures the similarity between the new document and existing clusters is developed. It avoids the need to compute the pair-wise similarity between two individual documents and hence saves a huge amount of computing effort. XCLS is further modified to incorporate the semantic meanings of XML tags for investigating the trade-offs between accuracy and efficiency. The empirical analysis shows that the structural similarity overplays the semantic similarity in the clustering process of the structured data such as XML. The experimental analysis shows that the XCLS method is fast and accurate in clustering the heterogeneous documents by structures.

XML schema clustering with semantic and hierarchical similarity measures

With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis.

Element similarity measures in XML schema matching

Schema matching plays a central role in a myriad of XML-based applications. There has been a growing need for developing high-performance matching systems in order to identify and discover semantic correspondences across XML data. XML schema matching methods face several challenges in the form of definition, adoption, utilization, and combination of element similarity measures. In this paper, we classify, review, and experimentally compare major methods of element similarity measures and their combinations. We aim at presenting a unified view which is useful when developing a new element similarity measure, when implementing an XML schema matching component, when using an XML schema matching system, and when comparing XML schema matching systems.

Collaborative Filtering Recommender Systems Using Tag Information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviors such as purchase behavior, click streams, and browsing history etc., the tagging information implies userpsilas important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

XML data clustering: An overview

In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced.

A hybrid neural network and simulated annealing approach to the unit commitment problem

Abstract In this paper, the authors present an approach combining the feedforward neural network and the simulated annealing method to solve unit commitment, a mixed integer combinatorial optimisation problem in power system. The artificial neural network is used to determine the discrete variables corresponding to the state of each unit at each time interval. The simulated annealing method is used to generate the continuous variables corresponding to the power output of each unit and the production cost. The type of neural network used in this method is a multi-layer perceptron trained by the back-propagation algorithm. A set of load profiles as inputs and the corresponding unit commitment schedules as outputs (satisfying the minimum up–down, spinning reserve and crew constraints) are utilized to train the network. A method to generate the training patterns is also presented. The experimental result demonstrates that the proposed approach can solve unit commitment in a reduced computational time with an optimum generation schedule.

A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate.

Artificial neural networks in biomedical engineering: a review

This paper presents a review of applications of artificial neural networks in biomedical engineering area. Artificial neural networks in general are explained; some limitations and some proven benefits of neural networks are discussed. Use of artificial neural network techniques in various biomedical engineering applications is summarised. A case study is used to demonstrate the efficacy of artificial neural networks in this area. The paper concludes with a discussion of future usage of artificial neural networks in the area of biomedical engineering.