• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.133-142
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• 2004

The next generation of web applications will need to be larger, more complex, and flexible Agent-oriented systems have great potential for these e-commerce applications. Agents can dynamically discover and compose e-services and mediate interactions. Development of software agents with CBD (Component Based Development) has proved to be successful in increasing speed to market of development Projects, lowering the development cost and providing better qualify. In this thesis, we propose a systemic development process for software agents using component and UML (Unified Modeling Language). We suggest a etA (e-business Agent) CBD reference architecture for layer the related components through identification and classification of general agent and e-business agent. We also propose the ebA-CBD process that is a guideline to consider the best features of existing agent oriented software engineering methodologies, while grounding agent-oriented concepts in the same underlying semantic framework used by UML. We first developed the agent components specification and modeled it with Goal, Role, Interaction, and Architecture Model. Based on this, we developed e-CPIMAS (e-Commerce Product Information Mailing Agent System) as a case study that provides the product information's mailing service according to proposed process formality. We finally describe how these concepts may assist in increasing the efficiency reusability, productivity and quality to develop the business application and e-business agent.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.937-950
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• 2004

With growing population of internet and web applications, distributed environment is considered to be the standard architecture of application. A network management systems(NMS) is necessary to control and monitor the complex network resources for providing and sharing the heft quality service. We recognize the NMS as a standard infrastructure for supporting efficient networking and a separate commercial applications. We believe every resource including software, hardware and environment for the network management should be separated from special protocols, vendors and applications. Therefore, We need a standard network management system that is efficient and consistent because of the heterogeous network features. In regards to software development, software reuse through assembling and extending the reusable elements such as patterns and components assures to realize the best productivity and quality The component based development(CBD) methodology that can assemble black box though well defined interfaces makes it possible to develop easer and quicker applications and is proved as the best software development solution involved in construction, selection and assembly of components. In this thesis, we describe the architecture for the network management and identify, define and design the components through analysis and design in the network management domain and Identified components mapped to the component architecture. We also specify the component development and design and implement the component for developing the network management. Implemented components apply to the component repository system that register, retrieve and understand the components. We analyze, design and implement the entire network management system based on configuration, connection, performance and fault management through the pre-developed components.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.383-390
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• 2006

Product-line engineering is a dreaming goal in software engineering research. Unfortunately, the current underlying technologies do not seem to be still not much matured enough to make it viable in the industry. Based on our experiences in working on mobile telephony systems over 3 years, now we are in the course of developing an approach to product-line engineering for mobile telephony system software. In this paper, the experiences are shared together with our research motivation and idea. Consequently, we propose an approach to building and maintaining telephony application logics from the perspective of scenes. As a Domain-Specific Language(DSL), Menu Navigation Viewpoint(MNV) DSL is designed to deal with the problem domain of telephony applications. The functional requirements on how a set of telephony application logics are configured can be so various depending on manufacturer, product concept, service carrier, and so on. However, there is a commonality that all of the currently used telephony application logics can be generally described from the point of user's view, with a set of functional features that can be combinatorially synthesized from typical telephony services(i.e. voice/video telephony, CBS/SMS/MMS, address book, data connection, camera/multimedia, web browsing, etc.), and their possible connectivity. MNV DSL description acts as a backbone software architecture based on which the other types of telephony application logics are placed and aligned to work together globally.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.71-81
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• 2002

The lack of specialties of the existing commercial web search systems stems from the fact that they have no capabilities to extract and gather the meaningful information from each information domain they cover. We are sure, however, that the necessity for the information integration system, not just search system, will be likely to become larger in the future. In this paper, we propose a design and implementation of an information integration system called TIC(target information collector). TIC is able to extract meaningful information from a specific information area in the internet and integrate them for the commercial service. We also show the evaluation results of our implementation. For the experiments we applied our TIC to the international procurement information area. The international procurement information is publicly and freely announced by each government to the world. To automatically extract common properties from the related source sites, we adopt information pointing technique using inter-HTML tag pattern parsing. And through the information integration framework design, we can easily implement a site-specific information integration engine. By running our TIC for about 8 months, we find out it can remove considerable amount of the duplicated information, and as a result, we can obtain high quality international procurement information. The main contribution of this paper is to present a framework design and it's implementation for extracting the information of a specific area and then integrating them into a meaningful one.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.3
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• pp.202-213
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• 2024

Introduction: Acknowledging the global issue of diseases potentially caused by overwork, this study aims to develop an AI model to help workers understand the connection between cerebrocardiovascular diseases and their work environment. Materials and methods: The model was trained using medical and legal expertise along with data from the 2021 occupational disease adjudication certificate by the Industrial Accident Compensation Insurance and Prevention Service. The Polyglot-ko-5.8B model, which is effective for processing Korean, was utilized. Model performance was evaluated through accuracy, precision, sensitivity, and F1-score metrics. Results: The model trained on a comprehensive dataset, including expert knowledge and actual case data, outperformed the others with respective accuracy, precision, sensitivity, and F1-scores of 0.91, 0.89, 0.84, and 0.87. However, it still had limitations in responding to certain scenarios. Discussion: The comprehensive model proved most effective in diagnosing work-related cerebrocardiovascular diseases, highlighting the significance of integrating actual case data in AI model development. Despite its efficacy, the model showed limitations in handling diverse cases and offering health management solutions. Conclusion: The study succeeded in creating an AI model to discern the link between work factors and cerebrocardiovascular diseases, showcasing the highest efficacy with the comprehensively trained model. Future enhancements towards a template-based approach and the development of a user-friendly chatbot webUI for workers are recommended to address the model's current limitations.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.154-166
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• 2009

Database query and reporting tools, OLAP tools and data mining tools are typical front-end tools in Business Intelligence environment which is able to support gathering, consolidating and analyzing data produced from business operation activities and provide access to the result to enterprise's users. Traditional reporting tools have an advantage of creating sophisticated dynamic reports including SQL query result sets, which look like documents produced by word processors, and publishing the reports to the Web environment, but data source for the tools is limited to RDBMS. On the other hand, OLAP tools and data mining tools have an advantage of providing powerful information analysis functions on each own way, but built-in visualization components for analysis results are limited to tables or some charts. Thus, this paper presents a system that integrates three typical front-end tools to complement one another for BI environment. Traditional reporting tools only have a query editor for generating SQL statements to bring data from RDBMS. However, the reporting tool presented by this paper can extract data also from OLAP and data mining servers, because editors for OLAP and data mining query requests are added into this tool. Traditional systems produce all documents in the server side. This structure enables reporting tools to avoid repetitive process to generate documents, when many clients intend to access the same dynamic document. But, because this system targets that a few users generate documents for data analysis, this tool generates documents at the client side. Therefore, the tool has a processing mechanism to deal with a number of data despite the limited memory capacity of the report viewer in the client side. Also, this reporting tool has data structure for integrating data from three kinds of data sources into one document. Finally, most of traditional front-end tools for BI are dependent on data source architecture from specific vendor. To overcome the problem, this system uses XMLA that is a protocol based on web service to access to data sources for OLAP and data mining services from various vendors.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.