Informatics Publishing Limited

C. Malikarjuna Rao ¹, K. V. Chalapati Rao ², A. Aswani Kumar ¹

Affiliations
1 Medical College, Kurnool, IN
2 Medical College, Guntur, IN

Abstract

Abstract not Given.

Keywords

No Keywords given

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C. Ramesh ¹, K. V. Chalapati Rao ¹, A. Goverdhan ²

Affiliations
1 Department of Computer Science and Engineering, CVR College of Engineering, Ibrahimpatnam, R.R.District, Andhra Pradesh, IN
2 JNTUH College of Engineering, Jagityala, Karimnagar District, Andhra Pradesh, IN

Abstract

With the rapid growth of internet technologies, Web has become a huge repository of information and keeps growing exponentially under no editorial control. However the human capability to read, access and understand Web content remains constant. This motivated researchers to provide Web personalized online services such as Web recommendations to alleviate the information overload problem and provide tailored Web experiences to the Web users. Recent studies show that Web usage mining has emerged as a popular approach in providing Web personalization. However conventional Web usage based recommender systems are limited in their ability to use the domain knowledge of the Web application. The focus is only on Web usage data. As a consequence the quality of the discovered patterns is low. In this paper, we propose a novel framework integrating semantic information in the Web usage mining process. Sequential Pattern Mining technique is applied over the semantic space to discover the frequent sequential patterns. The frequent navigational patterns are extracted in the form of Ontology instances instead of Web page views and the resultant semantic patterns are used for generating Web page recommendations to the user. Experimental results shown are promising and proved that incorporating semantic information into Web usage mining process can provide us with more interesting patterns which consequently make the recommendation system more functional, smarter and comprehensive.

Keywords

Web Usage Mining, Semantic Web, Domain Ontology, Sequential Pattern Mining, Recommender Systems.

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K. Venkateswara Rao ¹, A. Govardhan ², K. V. Chalapati Rao ¹

Affiliations
1 Department of Computer Science and Engineering, CVR College of Engineering, Ibrahimpatnam RR District, Andhra Pradesh, IN
2 JNTUH College of Engineering, Jagityala, Karimnagar Dist, Andhra Pradesh, IN

Abstract

Spatiotemporal databases deal with changes to spatial objects with time. The Spatiotemporal knowledge discovery process involves the integration of such database systems with data warehousing, data mining and online-analytical processing technologies. The applications in this domain will process spatial, temporal and attribute data elements and use spatio-temporal relationships among these elements. These applications deal with the evolution of spatial objects and changes in their topological relationships, associations along with time. These advanced database applications require storing, management and processing of complex spatiotemporal data. In this paper we consider the classification and modeling requirements for spatiotemporal applications, System modeling, preprocessing spatiotemporal data, discovering spatiotemporal topological relationships and extension of apriori algorithm for mining spatiotemporal frequent predicates. The design of an appropriate system that can capture spatiotemporal features of datasets is discussed. Prototype implementation of the system is carried out on top of open source object relational spatial database management system called postgresql and postgis. The algorithms are experimented on historical cadastral datasets which are created using OpenJump. The results that are visualized using OpenJump software are presented.

Keywords

Spatiotemporal Database, Spatiotemporal Application, Spatiotemporal Data Modeling, Spatiotemporal Relationships, Spatiotemporal Data Mining, Spatiotemporal Data Analysis.

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