• Knowledge Management Research
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• v.17 no.1
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• pp.49-72
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• 2016

Extant research on firm innovation productivity is limited in measuring the innovation productivity, in which they measured firm innovation productivity by using either inputs or outputs of innovation. The present study complemented the extant research by employing Data Envelopment Analysis (DEA) approach to measure firm innovation productivity. Furthermore, this paper examined the effects of firms' external knowledge search, as one of open innovation practices, on firm innovation productivity, for open innovation activities are regarded as an influencing factor on firm innovation productivity in the previous literatures. Using the data of the Korean Innovation Survey (KIS) of manufacturing industries conducted in 2008, this study developed hypotheses in which we considered not only two dimensions of external knowledge search (breadth and depth) but also two subtypes of external knowledge search (market-driven and science-driven). The results found that searching deeply and market-driven search are positively related to firm innovation productivity, but science-driven search is somewhat negatively related to firm innovation productivity. Furthermore, market-driven search can mitigate the negative effect of science-driven search on innovation productivity.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.70-77
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• 1998

Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven inference engine named CAKES and then experimented with some illustrative examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.1
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• pp.40-50
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• 2009

Knowledge-based services are largely dependent upon human-driven works. Therefore, considering human characteristics is required when modeling processes for knowledge-based services. As an emerging technology for Business Process Management, Human Interaction Management and its supportive process management can be an alternative to deal with human-driven processes. However, current HIM does not suggest concrete method for modeling conditions that are essential to realize supportive process management. And the condition modeling of HumanEdj, the only HIM software implemented, reveals the problem of complexity. As a solution, this paper suggests a fact-oriented ontological approach to process modeling. The approach uses human-friendly form of facts for condition modeling.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.83-90
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• 2008

Development of human-system interfaces (HSI) supporting the interaction between human and automation-based systems, particularly safety-critical sociotechnial systems, entails a wide range of design and evaluation problems. To help HSI designers deal with these problems, many methodologies from traditional human-computer interaction, software engineering, and systems engineering have been applied; however, they have been proved inadequate to develop cognitively well engineered HSI. This paper takes a viewpoint that HSI development is itself a cognitive process consisting of various decision making and problem solving activities and then proposes a design requirements-driven process for developing HSI. High-level design problems and their corresponding design requirements for visual information display are explained to clarify the concept of design requirements. Lastly, conceptual design of software system to support the requirements-driven process and designers' knowledge management is described.

• Asia pacific journal of information systems
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• v.9 no.1
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• pp.115-142
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• 1999

Legacy applications are valuable assets that should be integrated into next generation business systems. To gain this advantage, progressive companies can reverse engineer the legacy business operations. This paper presents a form-driven object-oriented reverse engineering(FORE) methodology by the use of business forms to recover semantics of legacy applications. They retain the user-oriented contents of business and thus are easily understandable. Our form driven object-oriented reverse engineering methodology consists of five phases: form and usage analysis, form object slicing, object structure modeling, scenario design, and model integration. Knowledge about form structure and user interaction with legacy applications is used to capture the design semantics. An object model, which consists of an object structure model and scenario results from such form knowledge. The resulting object model is more likely to help reverse engineers understand and reuse legacy systems.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.477-495
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• 2022

This paper proposes how to design and implement data-driven strategies by investigating how a firm can increase its value using data science. Drawing on prior studies on architectural innovation, a behavioral theory of the firm, and the knowledge-based view of the firm as well as the analysis of field observations, the paper shows how data science is abused in dealing with meso-level data while it is underused in using macro-level and alternative data to accomplish machine-human teaming and risk management. The implications help us understand why some firms are better at drawing value from intangibles such as data, data-science capabilities, and routines and how to evaluate such capabilities.

• Korean Journal of English Language and Linguistics
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• v.2 no.3
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• pp.305-327
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• 2002

This paper is in line with the attempts to examine two assumptions implied about the role of Universal Grammar (UC) in nonnative language acquisition: Are the EFL learners at disadvantage in acquiring UC-driven knowledge? Are there critical period effects in EFL learning? Based on the research with the seven studies of ESL and EFL adult learners performance on the Subjacency violation sentences, the paper investigates the extent to which the EFL adult learners can attain UG-driven knowledge represented by the Subjacency Principle. It also makes comparison of the EFL learners level of access to UG with that of their counterparts, the ESL learners. The research findings suggests that the EFL environment doesn't prevent the learners from acquiring target grammar in UG domain. That is, the current paper strongly suggests that the EFL adult-learners be able to acquire UG-driven knowledge to a considerable extent, at least as high as the ESL adult learners can attain. For the interpretation of the research results of the seven studies, Constructionist Hypothesis (CH) supported by a Minimalist Program (MP) assumption is employed. CH seems more plausible to account not only for incomplete acquisition observed among the beginning and intermediate level learners but also for the native-like competence acquired by advanced level L2 learners.