• The Journal of Information Systems
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• v.19 no.3
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• pp.181-208
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• 2010

As knowledge-based society comes to the fore, sharing knowledge becomes a hot issue. Among various types of knowledge, a significance of software(applications) and contents is a huge in a current society. Moreover, along with appearance of smartphone, for instance iPhone, Application Store(also known as AppStore) utilized as a common gateway for sharing software and contents, brings a big interest for many users and developers. However, prior research to understand users' behavior on AppStore has been a scant. Therefore, the main purpose of this study is to investigate the impact of key smart service environmental factors on AppStore in order to empirically explain users' psychological feelings of intention to use AppStore. Based on a well-known technology adoption model, TAM, the study incorporates three main characteristics(user, society, and service) with six constructs(Innovation, Enjoyment, Subjective Norm, Information Level, Content variety, and Cost), influencing perceived usefulness, which then affects users' intention to use AppStore. Results provide evidence that support the tested hypotheses. The implications of the findings suggest a new theoretical work for future AppStore research and offers suggestions that the researchers and practitioners of AppStore should consider regarding the development of application and contents.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.27-36
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• 1998

This paper suggests an integrated method based on rough sets for discovering useful knowledge from a large databse. Our approach applies attribute-oriented concept hierarchy ascension technique to extract generalized data from actual data in database, induction of decision trees to measure the information gain, and knowledge reduction method of rough set theory to remove superfluous attributes and attribute values. The integrated algorithm first reduces the size of database through the concept generalization, reduces the number of attributes by means of eliminating condition attributes which have little influence on decision attribute, and finally induces simplified decision rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1972-1986
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• 2017

With the advent of the knowledge economy, science and technology resources have played an important role in economic competition, and their optimal allocation has been regarded as very important across the world. Thus, allocation optimization research for regional science and technology resources is significant for accelerating the reform of regional science and technology systems. Regional science and technology resource allocation optimization is modeled as a double-layer optimization model: the entire system is characterized by top-layer optimization, whereas the subsystems are characterized by bottom-layer optimization. To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm and sensitivity analysis. This novel method adopts the integrated modeling concept with a combination of the knowledge model and heuristic search model, on the basis of the heuristic search model, and simultaneously highlights the effect of the knowledge model. To compare the performance of different methods, five methods and two channels were used to address an application example. Both the optimized results and simulation time of the proposed method outperformed those of the other methods. The application of the proposed method to solve the problem of entire system optimization is feasible, correct, and effective.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.760-765
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• 2009

Since the features in object-images such as color and shape cannot clearly express the characteristic of objects, those features lead to vagueness of object-image recognition. Recently there have been studied on object-image recognition based on knowledge base in order to reduce the vagueness. However, because images are represented by numerical information but knowledge bases are represented by conceptual information, combining two kinds of information is difficult. In this paper, we compose knowledge base by using ontology to reduce the gap between the two kinds of information, and propose a method for object-image recognition to reduce the vagueness by using information on inner-object. Moreover, we confirm the usefulness of the proposed method through the experiments on object-image recognition in fruit domain.

• IE interfaces
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• v.12 no.2
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• pp.319-328
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• 1999

Today, offshore design field is concerned with system integration such as CIM(Computer Integrated Manufacturing), PDMS(Product Data Management System) and EDMS(Engineering Data Management System) in order to cope with the change of engineering specification as owner's requirements during construction stage of the project. This paper deals with the case study that describes about the efficient interface between material control system and 3D CAD system to support the design process in offshore industry using design rules involved the designer's knowledge. In this paper, we constructed an information system, called knowledge-based CAD interface systems, which is composed material code management system and 3D specification generator which automatically creates 3D catalogue anti specification by linking the material code, called short code, and the specification components of the 3D CAD system. As a result of the construction, it is possible to maintain consistency of the design process, and through reduction of the design processing time and improvement of the design process, competitiveness is improved.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.11-26
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• 2014

The analyses carried out within the Seismic Probabilistic Risk Assessments (SPRAs) of Nuclear Power Plants (NPPs) are affected by significant aleatory and epistemic uncertainties. These uncertainties have to be represented and quantified coherently with the data, information and knowledge available, to provide reasonable assurance that related decisions can be taken robustly and with confidence. The amount of data, information and knowledge available for seismic risk assessment is typically limited, so that the analysis must strongly rely on expert judgments. In this paper, a Dempster-Shafer Theory (DST) framework for handling uncertainties in NPP SPRAs is proposed and applied to an example case study. The main contributions of this paper are two: (i) applying the complete DST framework to SPRA models, showing how to build the Dempster-Shafer structures of the uncertainty parameters based on industry generic data, and (ii) embedding Bayesian updating based on plant specific data into the framework. The results of the application to a case study show that the approach is feasible and effective in (i) describing and jointly propagating aleatory and epistemic uncertainties in SPRA models and (ii) providing 'conservative' bounds on the safety quantities of interest (i.e. Core Damage Frequency, CDF) that reflect the (limited) state of knowledge of the experts about the system of interest.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.261-279
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• 2023

In this article, the uplink achievable rate is investigated for massive multiple-input multiple-output (MIMO) under correlated Ricean fading channel, where each base station (BS) and user are both deployed multiple antennas. Considering the availability of prior knowledge at BS, two different channel estimation approaches are adopted with and without prior knowledge. Based on these channel estimations, a two-layer decoding scheme is adopted with maximum ratio precoding as the first layer decoder and optimal second layer precoding in the second layer. Based on two aforementioned channel estimations and two-layer decoding scheme, the exact closed form expressions for uplink achievable rates are computed with and without prior knowledge, respectively. These derived expressions enable us to analyze the impacts of line-of-sight (LoS) component, two-layer decoding, data transmit power, pilot contamination, and spatially correlated Ricean fading. Then, numerical results illustrate that the system with spatially correlated Ricean fading channel is superior in terms of uplink achievable rate. Besides, it reveals that compared with the single-layer decoding, the two-layer decoding scheme can significantly improve the uplink achievable rate performance.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.295-315
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• 2006

Sharing knowledge is an important factor in the discourses on the knowledge management on the campus. This article analyzed the impact of organizational context on faculty's perceptions of knowledge-sharing capabilities in the university. As a result, perceptions of knowledge-sharing capabilities and performance-based reward systems were found to significantly affect faculty knowledge-sharing capabilities in the university studied. Also, results from multivariate analysis showed that the faculty's perception of knowledge-sharing more significantly affected knowledge-sharing than reward system.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.1079-1089
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• 2021

The current study aims to examine the effect of Hofstede's dimensions of culture on the intention of sharing knowledge in higher education institutions (HEIs) in Jordan. In the literature, researchers have given limited attention to such an effect. Therefore, by adopting Hofstede's framework, the current study attempts to investigate how Jordan's cultural context impacts on the intentions to share knowledge in HEIs. This study applied quantitative research methods to investigate the effect of Hofstede's cultural dimensions on knowledge-sharing intentions. In total, 307 questionnaires were collected from employees in Jordanian universities and, then, tested using descriptive and regression analytical methods. The study results show that culture dimensions influence knowledge-sharing intention and that each dimension plays a different role in enhancing this knowledge-sharing intention. More specifically, it was found that long-term orientation, collectivism and high uncertainty avoidance had a positive effect on knowledge-sharing intention, while cultural masculinity and power distance had no negative effect. Based on these results, the study makes several recommendations, the most important of which is the promotion of cultural values that encourage intention to share knowledge. Also, more qualitative research is needed to explore in depth the effective means that encourage intentions to share different types of knowledge.