Predefined Object Reduction

Reduction techniques is still an open area to be explored in knowledge management. This paper defines algorithm known as Predefined Hybrid Reduction which generate its conditions for object co occurrences of original data then execute Hybrid Reduction data for their data to perform extractions. Predefined Hybrid Reduction give a proper solution for expansion the data set , it select significant object with high quality of informations, it delete every object not satisfies their conditions. It show appropriate relevant result. It provide better reduction without inconsistency problem unlike data comparisons. It manage the inferior object which store only significant data based on predefined confidence and predefined support for maintain the inferior object then Hybrid reduction which are dual reduction. As part of this proposal, a comparison test with Hybrid reduction. The conclusion part which shows better alternative result through our mode

An Alternative Algorithm for Soft Set Parameter Selection using Special Order

The outcome of the reduction of soft data is dependent on the quality and discount evidence that increases with optimization analysis. There is a set of techniques that can be used to reduce the data, but the different techniques showed different results as each technique is focused on solving a particular problem. This paper proposed a parameter reduction algorithm, known as 3C algorithm, to circumvent the false frequent object in reduction. Results indicated that the proposed algorithm is easy to implement and perform better than the state-of-the-art parameter reduction algorithm. Also, the proposed algorithm can be used as an effective alternative method for reducing parameters in order to enhance the decision making process. Comparative analysis were performed between the proposed algorithm and the state-of-the-art parameter reduction algorithm using several soft set in terms of parameter reductio

Preserving data replication consistency through ROWA-MSTS

In modern distributed systems, replication receives particular aware�ness to provide high data availability, reliability and enhance the performance of the system. Replication becomes as significant mechanism since it enables organizations to provide users with admission to current data where and when they need it. Integrated VSFTPD with Read-One-Write-All Monitoring Syn�chronization Transaction System (ROWA-MSTS) has been developed to moni�tor data replication and transaction performs in distributed system environment. This paper presents the ROWA-MSTS framework and process flow in order to preserve the data replication consistency. The implementation shows that ROWA-MSTS able to monitor the replicated data distribution while maintain�ing the data consistency over multiple sites

The influence of appropriation of knowledge management system and intrinsic motivation on social capital in higher education institutions

As knowledge organization, Higher Education Institutions (HEI) are among the critical players in ensuring the knowledge being managed strategically in order to ensure the optimum benefits of knowledge creation, transfer and sharing happens among the internal and external community. These days, it was estimated that more than 80 percent of Knowledge Management (KM) programs end up with very low significant impact on the adopting organizations. Based on the existing studies, there are significant role of Appropriateness of KMS (A-KMS), Intrinsic Motivation (IM) and Social Capital (SC) in ensuring the KMS success. Thus, the purpose of this study was to empirically and systematically investigate the possible relationship between Appropriateness of KMS (A-KMS), Intrinsic Motivation (IM), Social Capital (SC) and demographic background in order to recommend the KMS Success Model. There were two phases of study using quantitative approach. The first phase was the survey approach where 1200 workers from Malaysian HEis were invited to participate in the survey and 398 (33%) was responded. Subsequently, the second phase was the semistructured interview where nine (9) senior managers from HEI were selected for detail interview. In quantitative study, a single mean t-test was conducted to identify whether the implementation level of A-KMS, IM and SC are significantly high. Furthermore, One-way ANOV A and independent sample t-tests were conducted to identify which demographic variables have influence on the SC. Subsequently a correlation and multiple regression analyses were conducted to identify the correlation and model that best represent the interrelation between A-KMS, IM, SC and demographic variables. A positive correlation was found between A-KMS and SC as well as IM and SC. As for multiple regression, the best model comprises of selected variables from A-KMS and IM was derived. The semi-structured interview was also conducted to complement and expand the findings from survey. Significant patterns and themes were identified and the findings suggest that the internal and external factors as well as barriers are the contextual factors that affect the implementation of A-KMS and IM to support the development of social capital. Finally, the Conceptual Framework of a A-KMS-IM-SC relationship was recommended accordingly

Categorizing Users in Requirement Engineering Process: A Case Study in e-University Project

Abstract-In order to fulfill user's requirement, it is very important to manage user effectively. Many studies have shown that most of the software project fails due to the inconsistent needs of software users with the view of software developers [1][6]. Understanding user needs is not a sequential process. It involves different disciplines such as psychology, languages and communications, organizational behavior and management. This study has explored a practical approach of managing users by establishing ownership-based user groups. Each group will have its own characteristic and responsibility which will be established through policy, education and awareness program. The commitment and cooperation of each user group will be managed through identifying and managing them through cooperation based user group. The case study has been carried out in University College of Engineering & Technology Malaysia. The study has derived 4 categories of users based on ownership with its own characteristic and responsibility. As for the cooperation based user groups, it has been categorized into 4 groups. The study found that managing users through ownership-based and cooperation based user group has improved the involvement and commitment level of users during the whole software process especially during requirement engineering process

Applying Variable Precision Rough Set for Clustering Diabetics Dataset

Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a rough-set based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute

RMF: Rough Set Membership Function-based for Clustering Web Transactions

One of the most important techniques to improve information management on the web in order to obtain better understanding of user's behaviour is clustering web data. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions to merge between two or more clusters. However, in reviewing the technique, it has a weakness in terms of processing time in obtaining web clusters. In this paper, an alternative technique for grouping web transactions using rough set theory, named RMF is proposed. It is based on the rough membership function of a transaction similarity class with respect to the other classes. The two UCI benchmarks datasets are opted in the experimental processes. The experimental results reveal that the proposed technique has an benefit of low time complexity as compared to the baseline technique up to 67

Applying Variable Precision Rough Set for Clustering Diabetics

Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a roughset based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute

Pre Soft Parameter Reduction From Soft Set

The complexity induced by machine infinite stae cost the decisions resource such as CPU time. In solving this problem serarch space should be divided or decomposed into several sub sets in order to look for the informations which contains original characterstics. Identical of original set and it’s reductions induced two sets which measured by Jaccard similirtity to select the reductions were totally dominated against original characterstics. Searching process decomposed implises into several sub combinations eliminations as pre reductions which checked by decision partition cluster using Hybrid complement reduction. The decomposisions performances enhanced searching complexity and provided faster result in the process of decisions making generation