<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

In data integration, schema matching plays an important role. Present schema matching tools combine various match algorithms, each employing a particular technique to improve matching accuracy. However there is still no fully automatic tool is available and also there is lack of accuracy. As a step in this direction, we have proposed a new and efficient Semantic-Relationship schema matching (SR-Match) approach which considers the semantic relationships as one of the parameters for matching. Here in SR-Match, the initial mappings performed by the basic schema mapping techniques, acts as input to the relationship matcher. Relationship matcher compares the remaining unmapped elements based on their semantic relationship with their parents. It is observed that, if both semantics and relationships are taken into account, the degree of accuracy in matching results is improved. © Springer-Verlag 2011.

Computer Science and Engineering

(SoE) School of Engineering

World University root node

Communications in Computer and Information Science

SR-match: A novel schema matcher based on semantic relationship

It is possible to obtain fine grained location information fairly easily using Global Positioning System (GPS) enabled devices. It becomes easy to track an individual's location and trace her trajectory using such devices. By aggregating this data and analyzing multiple users' trajectory a lot of useful information can be extracted. In this paper, we aim to analyze aggregate GPS information of multiple users to mine a list of interesting locations and rank them. By interesting locations we mean the geographical locations visited by several users. It can be an office, university, historical place, a good restaurant, a shopping complex, a stadium, etc. To achieve this various relational algebra operations and statistical operations are applied on the GPS trajectory data of multiple users. The end result is a ranked list of interesting locations. We show the results of applying our methods on a large real life GPS dataset of sixty two users collected over a period of two years. Copyright © 2011 ACM.