Ontology-based Data Access (OBDA) is gaining importance both scientifically and practically. However, little attention has been paid so far to the problem of updating OBDA systems. This is an essential issue if we want to be able to cope with modifications of data both at the ontology and at the source level, while maintaining the independence of the data sources. In this paper, we propose mech...

Multi layer perceptron with back propagation algorithm is popular and more used than other neural network types in various fields of investigation as a non-linear predictor. Though MLP can solve complex and non-linear problems, it cannot use missing data for training directly. We propose a training algorithm with incomplete pattern data using conventional MLP network. Focusing on the fact that ...

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...

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 cer...

Temporal databases are an active and fast growing research area. Although many temporal extensions of the relational data model have been proposed, there is no comparable amount of work in the context of object-oriented data models. Moreover, few of the proposed models have been implemented. This report discusses how the temporal data model developed for T Chimera [4] has been implemented on to...

Data streams are sequence of data examples that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques (e.g., sensor readings, call records, web page visits). Tachiniques for classification fail to successfully process data streams because of two factors: their overwhelmin...

A data-based methodology for improving product quality is proposed. Referred to as Data-Driven Quality Improvement (DDQI), the proposed method can cope with qualitative as well as quantitative variables, determine the operating conditions that can achieve the desired product quality, optimize operating condition under constraints, and also evaluate the validity of the results. The desired yield...

Missing Values and its problems are very common in the data cleaning process. Several methods have been proposed so as to process missing data in datasets and avoid problems caused by it. This paper discusses various problems caused by missing values and different ways in which one can deal with them. Missing data is a familiar and unavoidable problem in large datasets and is widely discussed i...

The SuperJournal research project evaluated the usage within UK academia of a set of electronic journals made available through a bespoke electronic journal application via the World Wide Web. This paper describes the data handling processes which comprised the production system required to import publisher supplied journal data into the SuperJournal application. In particular, it focuses on th...

Classification is one of the most efficient and widely used data mining technique. In classification, Decision trees can handle high dimensional data, and their representation is intuitive and generally easy to assimilate by humans. Decision trees handle the data whose values are certain. We extend such classifiers i.e, decision trees to handle uncertain information. Value uncertainty arises in...