• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.51-56
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• 2003

Traditional travel information systems provide us with comprehensive information and internet-based travel information systems (IBTIS) with useful information. However, they suffer from limited access to the system, and a lack of flexibility for user requirements. In order to overcome these problems, this research introduces a knowledge-based expert system approach. As a case study, a Knowledge-based Expert System for guiding a New Zealand tour (KEGUNZ) has been developed to suggest highly user-oriented travel plans efficiently in New Zealand. KEGUNZ has been implemented using EXSYS, an expert system shell.

• Asia pacific journal of information systems
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• v.12 no.1
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• pp.1-16
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• 2002

Most of searching systems used by shopping-mall provide too much information for user requirements or fail to provide appropriate items reflecting customer's preference. This paper aims to design and implement the product searching systems based on customer preference which will enable efficient product selection in the internet shopping-mall. The proposed system consists of user/provider interface, searching and model agent, data management system, and model management system. Especially, we construct the searching pattern database to support fast search using association mining method. And this system includes the customer-oriented decision model which shows the highly preferred products. Input weight value per attribute and preference level should be needed to compute priority grade of preference.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.274-280
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• 2005

Future-oriented train design should be able to provide highly refined traffic culture and public transportation service by achieving 'higher value addition, sensitization, and humanization' and then securing safety, convenience, and amenity. Therefore, design of FRT, as a means of environment-friendly public transportation, should have new concepts achieving harmony between design and technology. Consequently, when designing interior of trains for public transportation, it should aim for user-centered strategies. This approach is essentially applied to vehicle design and inducement to new concepts by design aesthetics and their verification become requirements for train Interior design, which should be equipped with sensitivity and functional images. Accordingly, a study on the correlation between constituents in order to implement design is essential to design planning and realization of strategies.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.255-262
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• 2014

Our previous research[1] suggested object extraction and modeling method based on Fillmore's case grammar. This approach had not considered of use case extraction and method. To solve this problem, we adopt Fillmore's semantic method as linguistic approach into requirement engineering, which refine fillmore's case grammar for extracting and modeling use cases from customer requirements. This Refined mechanism includes the definition of a structured procedure and the representation of visual notations for 'case' modeling. This paper also proposes the use case decision matrix to identify use case size from extracted use cases based on goal oriented requirement engineering(GoRE), which related with the complexity of use case, and also prioritizes the use cases with this matrix. It demonstrates our proposal with the bank ATM system.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.240-250
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• 2010

A URC, which is a Ubiquitous Robot Companion, provides services to users in ubiquitous computing environments and has advantage of simplifying robot's hardware and software by distributing the complicated functionality of robots to other system. In this paper, we propose SOWL, which is a software architecture for URC robots and a mixed word of SOMAR and CAWL. SOWL keeps the advantages of URC and it also has the loosely-coupled characteristics. Moreover it makes it easy to develop of URC robot software. The proposed architecture is composed of 4 layers: device software, robot software, robot application, and end user layer. Developers of the each layer is able to build software suitable for their requirements by combining software modules in the lower layer. SOWL consists of SOMAR and CAWL engine. SOMAR, which is a middleware for the execution of device software and robot software, is based on service-oriented architecture(SOA) for robot software. CAWL engine is a system to process CAWL which is a context-aware workflow language. SOWL is able to provide a layered architecture for the execution of a robot software. It also makes it possible for developers of the each layer to build module-based robot software.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.817-833
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• 1996

Most object-oriented analysis and design methodologies are based on structured analysis and information modeling and are using for intuitive analysis and design models based on object-oriented programming languages. Therefore there are many problems such as when a system is implemented incorrect semantics and inconsistency between models.This paper submits a decomposition and design method for object, dynamic and functional module of the methodology of a new system development life-cycle. Thus, we present a new system development life cycle, and suggestsa object-oriented design methodand standards of module decomposition for the decomposition of object, dynamic, functional models due to object-oriented design procedures and specifications. This proposed method enables developers to reflect user's software requirements conveniently. We prove the validity and practicality of this object-oriented design method through implementing a real-system.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.631-639
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• 2014

Since digital broadcasting service has been begun, user's requirements have been increased for personalized broadcasting system that users can watch the part of programs which they want to watch in anytime. In personalized broadcasting system, metadata is important for searching program information based on user's input data. In this paper, an object-oriented media service metadata management module is implemented. Metadata used in this system is described by extending TV-Anytime specifications which is the International standard for personalized media service. For this, extended segments information is provided including object information which do not existing in previous personalized broadcasting standard. This system can provide better satisfaction to users by using object segmentation information.

• Proceedings of Korean Society of Archives and Records Management
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• 2019.05a
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• pp.35-41
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• 2019

Providing efficient recording information services has become vital in a utilization-oriented records management environment and further increasing the importance of the quality of the services offered. This study is a preliminary study to propose improvement measures by applying Quality Function Development (QFD), a quality control method, to the National Archives of Korea (NAK) and Records Information Service. The quality dimensions of the recording information service were analyzed, and specific user requirements were derived. Through this process, the user requirements' questionnaire was developed, reflecting the characteristics of the NAK, and the application model of QFD was designed based on its prior study. Based on the QFD application model proposed, the results obtained by performing the QFD step by step can be expressed in House of Quality (HoQ), which can be used as a basis for service design.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.