• Asia pacific journal of information systems
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• v.32 no.1
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• pp.168-190
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• 2022

This review aims to synthesize social network theories by drawing on the importance of social network perspectives in understanding knowledge management with technology in organizations. I provide an overview of prior social network research with the following core ideas: the primacy of relations between organizational actors, the utility of actors' embeddedness in social fields, the social utility of network connections, and the structural patterning of social life. On top of that, I summarize critical social perspectives (the social capital theory, the structural hole theory, the embeddedness perspective, the social exchange theory, the organizational learning theory, and the innovation diffusion theory) to suggest potential research questions for future studies in social network research in the knowledge management discipline.

• Knowledge Management Research
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• v.16 no.2
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• pp.1-27
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• 2015

This paper presents a knowledge-based view of platform-centered collaborations among multiple organizations. Studies of technological innovation and knowledge creation have broadened beyond their initial emphasis on internal development within an organization or simple exchange of ideas between two parties toward complex collaboration among many organizations at the level of platform-based knowledge ecosystems. Platforms serve as an interface between different groups of producers and consumers in a variety of multi-sided knowledge markets such as smartphone operating systems and video games industries. This study is an exploratory examination to offer theoretical understanding of how the organizational capabilities of platform leaders help expand a network of platform participants. The growth of platform participants is particularly important in the early stage of any platforms as the concept of network effects suggests that the platform with the largest number of participants will capture entire markets. Building upon organization studies and network economics theory on multisided markets, this paper focuses on the role of platform leaders in expanding platform-based collaboration. In our view, platform leaders develop varying levels of three organizational capabilities to discern quality of potential participants, to attract them to actually participate in collaboration, and to maintain long-term exchange relations in the ecosystem. We suggest that the capabilities of platform leaders will have a positive effect on the expansion of platform participants to secure network effects, and also examine several contextual factors that moderate the relationship between a platform leader's capacity and platform expansion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.410-415
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• 1997

Recently, The manufacturing companies are introducing the CAD/CAM systems to solve problems for the lack of experts, the higher cost of manufacturing and the difficulties of process. Knowledge engineering approach makes it possible to change a know-how of experts to computerized information effectivly. The proposal of this paper is the development of an interactive knowledge-based CAM system to disign and manufacture the mold with non-expert engineers used easily. This system is composed of two functional parts. One is the geometric modeler that used the technique of a feature modeling. The other is the expert system module that composed inference engine and databas which contains characteristics of materials and cutting tools setc.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.32
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• pp.227-232
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• 1994

In the performance domain, physical performance is a measure that represents some degree of system, subsystem, component or device success in a continuous sense, as opposed to a classical binomial sense (success or failure). If applicable sensing and monitoring means exist, physical performance can be observed over time, along with explanatory variables or covariables. Performance-based reliability represents the probability that performance will remain satisfactory over a finite period of time or usage cycles in the future when a performance critical limit (which represents an appropriate definition of failure in terms of performance) is set at a fixed level, based on application requirements. In the case of inadequate knowledge of the failure mechanics, this physical based empirical modeling concept along with performance degradation knowledge can serve as an important analysis tool in reliability work in product and process improvement.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.99-107
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• 1992

Both simulation and expert systems are popular ways to solve complex and hard problems. However, the results of the simulation, which include a large amount of valuable information as a good knowledge source, are not used efficiently. Furthermore, the development of the expert systems can fail because there is no expert or an expert is not available. A new Simulation-Based Expert System(SIMBES) paradigm has been constructed to overcome these problems. It consists of simulator, feature extractor, machine learning system, performance evaluator and Knowledge-Based Expert System(KBES). A SIMBES was implemented for an existing schedule-based MRP system in Smalltalk/V to show how this paradigm works and experimented for a large number of jobs. The KBES and the existing system produced better schedules for 72 percent and 28 percent of the jobs, respectively.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.17-27
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• 1995

We propose a framework for representing and processing uncertain knowledge on the basis of constraint satisfaction. A system of equations and/or inequalities can be considered as a set of constraints that should be solved, and each constraint in the set is transformed into a corresponding logical formula which can be solved through a constraint solving program. Most of rule-based systems, for instance, use a simple probabilistic theory in order to maintain uncertain knowledge, therefore uncertain knowledge can be represented and processed in the constraint satisfaction program quite efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.319-326
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• 2000

Expert systems using uncertain and ambiguous knowledge are not of the recent interests about uncertainty problem for performing inference similar to the decision making of a human expert. Human factors on rule-based systems often involve uncertain information. Expert systems had been used the methods of conflict resolution in a rule conflict situation, but this methods not properly solved the rule conflict. If a human expert appends a new rule to an original rule base, the rule base rightly causes a rule conflict. In this paper, the problem of rule conflict is regarded as one in which uncertainty of information is fundamentally involved. In the reduction of problem with uncertainty, we propose an enhanced rule ordering method, which improve the rule ordering method using Dempster-Shafer theory. We also propose a combinative index, which involve human factors of experts decision making.