• Journal of Information Technology Services
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• v.21 no.2
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• pp.61-84
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• 2022

With the recent application of deep learning to Natural Language Processing (NLP), the performance of NLP has improved significantly and NLP is emerging as a core competency of organizations. However, when encountering NLP use cases that are sporadically reported through various online and offline channels, it is often difficult to come up with a big picture of how to understand and interpret them or how to connect them to business. This study presents a framework for systematically analyzing NLP use cases, considering the characteristics of NLP techniques applicable to almost all industries and business functions, environmental changes in the era of the Fourth Industrial Revolution, and the effectiveness of adopting NLP reflecting all business functional areas. Through solving research questions based on the framework, the usefulness of it is validated. First, by accumulating NLP use cases and pivoting them around the business function dimension, we derive how NLP techniques are used in each business functional area. Next, by synthesizing related surveys and reports to the accumulated use cases, we draw implications for each business function and major NLP techniques. This work promotes the creation of innovative business scenarios and provides multilateral implications for the adoption of NLP by systematically viewing NLP techniques, industries, and business functional areas. The use case analysis framework proposed in this study presents a new perspective for research on new technology use cases. It also helps explore strategies that can dramatically improve organizational performance through a holistic approach that encompasses all business functional areas.

• Journal of KIISE
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• v.44 no.3
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• pp.261-274
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• 2017

Variability management is the foremost criterion that defines the extent to which complexities can be handled in a system. Predominantly, the requirements' engineering (RE) study overlooks, or speculates a consistent behavior of, the environment in which a system functions. In real-time systems it is vital to observe and adjust to an intrinsically changing context. Therefore, in this work we identify the requirements of the system in various contexts by recommending a framework using i* goal model, problem frames, use case maps and live sequence charts. The framework is illustrated using a case study of the smart grid RTP system. In the case study, elaboration of scenarios using use case maps and live sequence charts proved beneficial as they assisted in early analysis and validation of contexts. In addition, the elaboration of requirements for obstacle and conflict analysis assists the requirements engineer to increase the robustness of the system. The proposed framework is evaluated theoretically and by empirical study.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.51-67
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• 2012

In recent years, research has begun to place greater emphasis on the strategic use of IT in seeking to build firm's capability, thus innovating firm's business value. Many leading firms have been put efforts to formulate and implement IT governance (ITG) structures, processes, and mechanisms for firm's IT activities. However, recent literatures reviews found that ITG framework have been discussed independently rather than in an integrated and on convergent manner failing to provide comprehensive conceptual framework. The objective of this paper is therefore, to develop a conceptual framework that enable to conduct a comparative ITG case studies. The proposed framework, developed by one of author, has extensively applied in real case study to validate its usefulness and also aims to contribute practitioners to understand how well IT principles are formed within the organization as well as corelate with ITG objectives and ITG execution.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.69-88
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• 2013

Agile methods are highly attractive for small projects, but no agile method works well as a standalone system. Therefore, some adaption or customization is always required. In this paper, the Agile Framework for Small Projects (AFSP) was applied to four industry cases. The AFSP provides a structured way for software organizations to adopt agile practices and evaluate the results. The framework includes an extended Scrum process and agile practices, which are based on agility and critical success factors in agile software projects that are selected from Scrum, XP, FDD, DSDM and Crystal Clear. AFSP also helps software managers and developers effectively use agile engineering techniques throughout the software development lifecycle. The case study projects were evaluated on the basis of risk-based agility factors, the agility of the adopted practices, agile adoption levels, and the degree of the agile project success. The analysis of the results showed that the framework used in the aforementioned cases was effective.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.45-52
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• 2015

To solve the problem of low inter-operability of systems and low reusability caused by not being standardized and common-shared development foundation between software developers, the government has proposed e-Government(eGov) standard framework. However, the development method based on eGov standard framework is not easy to learn and to use fluently, because it is so dissimilar the existing traditional development method. In this paper, we propose an approach for developing web applications by reusing common components of eGov standard framework, and describe about the case study of developing Can_Pro(Computer Aided Nutritional analysis program for Professionals) based on eGov standard framework. Consequently, we show improvement in productivity and reduction budget through reusability of common components and eGov standard framework.

• The Mathematical Education
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• v.57 no.4
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• pp.433-452
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• 2018

The purpose of this study is to provide implications for improvement of mathematics textbook based on discursive approach to textbook analysis that complementarily combines a communicational approach to cognition and social semiotics. For this purpose, we reconstructed an analytic framework for discursive approach to written discourses of Korean textbook and CMP, and applied it to our analysis. Results show that several characteristics in meanings were developed by the use of words and visual mediators. First, in the case of ideational meaning, there were qualitative and quantitative differences between vocabularies used and between information addressed by visual mediators. Second, in the case of structural meaning, an offer and application of procedure was emphasized in a Korean textbook, whereas expectation and selection experiences of diverse possibilities for problem solving was underlined in CMP. In the case interpersonal meaning of student-author, imperative instructions were paid attentions in a Korean textbook. In contrast, students' interdependence and active participation were stressed in CMP. Therefore, this study addressed ideas about how to analyze mathematics textbooks based on integrated meanings developed by the use of words and visual mediators. In addition, it distributes implications for improvements of Korean mathematics textbooks through the analytic framework of both mathematical meanings and interpersonal meanings of student-author.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.89-113
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• 1990

The purpose of this study is to develop means to apply GIS and remote sensing technology to the analysis of Korean urban open spaces. To achieve this objective, a framework of analysis of urban open spaces was developed, and then the framework was applied for the evaluation of the potential and suitability of open spaces of Ansan City, which is a new town developed to accomodate industries relocation from Seoul, Korea, mainly due to their pollution problems. The software used in this study are IDRISI, a grid-based GIS, and KMIPS, a remote sensing analysis system. Both packages are based on IBM PC/AT computers with Microsoft DOS. Landsat MSS and TM data were used for the land use classification, land use change detection, and analysis of transformed vegetation indices. The size of the geographic data base is 110 rows and 150 columns with the spatial resolution of 100m$\times$100m. The framework of analysis includes both quanititative and qualitative analysis of open spaces. The quantitative analysis includes size and distribution of open spaces, urban develpment of open spaces, and the degree of vegree of vegetation removal of the study area. The qualitative analysis includes evaluative criteria for primary productivity of land, park use potential, major visual resources, and urban environmental control. The findings of this study can be summarized as follows. First, the size of builtup areas increased 18.73km$^2$, while the size of forest land decreased 10.86km$^2$ during last ten years. Agricultural lands maintained its size, but shifted toward outside of the city into forest. Second, the potential of open spaces for park use is limited mainly due to their lack of accessibility and connectivity among open spaces, in spite of ample acreage and good site conditions. Third, major landscape elements and historic sites should be connected to the open space system of the city by new accesses and buffers.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.900-908
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• 2010

Recently, OOM(Object-Oriented Method) access method which begins to apply to software development is emphasizing development cost and time reduction through independent software reuse. But because of planning to the exclusion of the lack of knowledge and expansion or performance for development technology many problems are coming out. Therefore, this thesis suggests Effective Object-Oriented Modeling methods considering the plan for higher productivity enhancement of software in the software development life cycle. Effective Object-Oriented Modeling method includes, methods, Use Case modeling methods, Analysis Modeling methods, Design Modeling methods and others.

• Journal of Korean Society of Rural Planning
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• v.2 no.1
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• pp.31-38
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• 1996

As a rational decision-making process of county-level area development, this study designed 3-step framework : function-giving(areal analysis) on unit planning area by decision matrix of land suitability, check of typical characteristics of each function area and formulation of its future development strategies. Two alternatives were suggested as the areal analysis method, of which one is equal ordering / valuing technique of checking indices for land suitability classfication and the other preferential weighting technique. And then, under the algorithm specially defined in this study, land suitability maps(Fig.2,3) of the case study area (Seungju-county area, Chonnam-province, Korea) were drawn from the areal analysis results. By use of land suitability classification results, unique characteristics of typical function areas were defined (on 7 types of alternative 1 , 8 types of II ) and their future development strategies were formulated in the case study area, According to the categorization criteria in this study, all the villages of the case area were classfied as a suitable type of function areas illustrated in this study.