Earticle

The theory of soft set proposed by Molodtsov in 1999 is a new method for handling uncertain data and can be redefined as a Boolean-valued information system. The soft set theory has been applied to data analysis and decision support systems based on large data sets. Using retrieved datasets, we will be comparing two techniques in solving incomplete datasets : parity bits of supported set; and the aggregate and calculated support values. It is demonstrated in this paper that the technique using aggregate values and calculated support values performs better in the process of identifying missing values in incomplete datasets.

In recent years, decision support systems otherwise called business Intelligence[BI] have become an integral part of organization's decision making strategy. Organizations nowadays are competing in the global market. In order for a company to gain competitive advantage over the others and also to help make better decisions, Data warehousing cum Data Mining are now playing a significant role in strategic decision making. It helps companies make better decisions, streamline work-flows, provide better customer services, and target market their products and services. The use of data warehousing and BI technology span sectors such as retail, airline, banking, health, government, investment, insurance, manufacturing, telecommunication, transportation, hospitality, pharmaceutical, and entertainment. This paper gives the report about developing data warehouse for business management using the Federal University of Technology Student-Course management system as a case study. It describes the process of data warehouse design and development using Microsoft SQL Server Analysis Services. It also outlines the development of a data cube as well as application of Online Analytical processing (OLAP) tools and Data Mining tools in data analysis. The purpose of the paper is to present the benefits of data warehouse and to sensitize companies in Nigeria to start building these facilities into their Enterprise resource management systems with the aim of making effective business decisions that will promote the rapid growth of the companies.

Detecting races is important for debugging shared-memory parallel programs, because the races result in unintended nondeterministic execution of the programs. Previous on-the-fly techniques to detect races in parallel programs with general inter-thread coordination show serious space overhead which is dependant on the maximum parallelism of the program. This paper presents a tool for space-efficient on-the-fly race detection. This employs a two-pass algorithm which splits a parallel loop with just one event variable into a series of two serializable loops, while preserving the semantics of the original program. The first serializable loop contains all the original dynamic blocks which are executed before the first wait operation in every thread. And, the next serializable loop contains all the original dynamic blocks which are executed after the first wait operation in every thread.

Software change management is an essential discipline for enterprise IT organizations. In modern enterprises, software automates a wide variety of business processes. Changes made to software are, in effect, changes made to the business processes themselves. While the majority of enterprises have implemented basic software change management processes, these processes fall down in the face of the increased complexity of today’s business and IT environments. Truly effective and efficient software change management will require better coordination of change across geographic, organizational, functional, and technological arena of IT implemented organization. This paper presents a classification of the change management problems derived from the literature study done. This study explores software change management — the definition and implementation of defined processes to control changes made to software. It also addresses the specific problems in the change management of application software development.

The ability to adapt automated guided vehicles for employment to a range of practical situations can significantly enhance their usability in hazardous situations where security is a major concern and it is inadvisable for humans to enter. Robot guidance is still a very challenging issue computationally in both the academic and industrial worlds. Whilst considerable progress has been made in robotics in the last few decades, many still experience difficulties in the recognition of dynamically changing situations such as our daily environments. With so many different scenarios it is difficult to find one system that can effectively deal with both the expected and unexpected issues that may arise. This paper examines the possibility of manipulating the potential inputs and outputs to a system to tailor a better solution to the current problem. Weightless neural networks will be used as a classification tool to determine the direction of a robot in an open loop simulation.

Feature selection has become very vital aspect in musical instruments sounds for handling the problem of ‘curse of dimensionality’. Various feature selection techniques have been applied in this domain focusing on Western musical instruments sounds. However, study on feature selection using rough sets of non-Western musical instruments sounds including Malay Traditional musical instruments is inadequate and still needs an intensive research. Thus, in this paper, an alternative feature selection technique using rough set theory based on Maximum Degree of dependency of Attributes (MDA) technique for Traditional Malay musical instruments sounds is proposed. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, feature selection using proposed technique and feature validation via classification. The results show that the performance of the best 17 selected features is increase up to 99.82% and 98.03% with 1-NN and PART classifiers respectively.

Distributed data mining (DDM) techniques have become necessary for large and multi-scenario datasets requiring resources, which are heterogeneous and distributed in nature. In this paper, we focus our attention on distributed data mining approach via grid. We have discussed and analyzed a new framework based on grid environments to execute new distributed data mining approaches that best suits a distributed and heterogeneous datasets that are commercially available. The architecture and motivation for the design have also been presented in this paper. A detailed survey on distributed data mining technology was also carried out which could offer a better solution since they are designed to work in a grid environment by paying careful attention to the computing and communication resources.