Earticle

Spatial data is data related to space. In various application fields like GIS, multimedia information systems, etc., there is a need to store and manage these data. Some datastructures used for the spatial access methods are R tree and its extensions where objects could be approximated by their minimum bounding rectangles and Quad tree based structures where space is subdivided according to certain rules. Also another structure KD Tree is used for organizing points in a k dimensional space. This paper makes review on some of these Hiearchical datastructures used for handling point data. It focuses on PR Quad Tree and KD Tree.The insertion procedure of these structures is reviewed and analyzed and also a comparison between them is drawn..

In the era of intense competition among organizations, retaining a customer is a collaborative process. Business organizations are adopting different strategies to facilitate their customers in verity of ways, so that these customers keep on buying from them. Association Rule Mining (ARM) is one of the strategies that find out correspondence/association among the items sold together by applying basket analysis. The clustering technique is also used for different advantages like; recognizing class of most sold products, classifying customers based on their buying behavior and their power of purchase. Different researchers have provided different algorithms for both ARM and Clustering, and are implemented in different data mining tools. This paper is extended version of [4], we have compared the results of Apriori and K-Mean algorithms against their implementation in Weka and XLMiner. For this comparison we have used the transaction data of Sales Day (a super store). The results are very encouraging and also produced valuable information for sales and business improvements. We have also analyzed the data for hidden knowledge and the results showed some very interesting patterns in user buying behavior and buying timings.

Research in the field of knowledge representation system is usually focused on methods for providing high-level descriptions of the world that can be effectively used to build intelligent applications. This paper shares the development process of an ontology which involves preparing questionnaires, design decisions, and interviewing key persons. This is an ongoing work resulting in the development of ontology for knowledge management in an enterprise. The final result is a knowledge ontology, coined as Know-Ont, is a collection of concepts and their related properties from maintenance and new product design domain that fit together to process and store knowledge thus making it available for later reuse.

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we focus our discussion on the rough set theory for categorical data clustering. We propose MADE (Maximal Attributes DEpendency), an alternative technique for categorical data clustering using rough set theory taking into account maximum attributes dependencies degree in categorical-valued information systems. Experimental results on two benchmark UCI datasets show that MADE technique is better with the baseline categorical data clustering technique with respect to computational complexity and clusters purity.

Transforming data from different information systems is important and challenging for integration purposes as data can be stored and represented in different data models in different information systems. In addition, when modeling data in the information systems, integrity constraints on the schemas are necessary for semantics and to maintain consistency purposes. Thus when schemas with conforming data are transformed from heterogeneous information systems, there is a need to transform and preserve semantics of data using constraints. Addressing this problem, we propose how data from different source information systems can be transformed to a global information system. We also review how constraints in data transformation are used in data integration for the purpose of integrating information systems. Our research is towards the handling of semantics using integrity constraints in data integration from heterogeneous information systems.