• 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 Korean society of Dental Hygiene
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• v.18 no.4
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• pp.477-488
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• 2018

Objectives: The purpose of this study was to investigate career competency, tasks, and job satisfaction of public servants, public institutions, and researchers. Methods: The survey was conducted about career competency, job satisfaction, and satisfaction on work life. Next, they interviewed on the characteristics of each job by two or three dimensions. The following conclusions were obtained from July to August 2017. Results: Career competencies were GPA with 3.87, 818 points of TOEIC score, and ITQ certification. Public servants required the information on literacy skills for employment and job performance, while civil servants need more than one year of clinical experience in the dental hospital. The non-commissioned officer needed a written test and fitness training. The health insurance review and assessment center required more than one year of experience from general hospital or medical institutions. Researchers required a research career, language skill, and professors required research and teaching experiences with clinical experience more than three years. The main job tasks were as follows; for public servants, they were official document processing and community projects. For the civilian workers and military/noncommissioned officers, they were medical assistant and administrative works. The employees of the health insurance review and assessment service are examining the medical expenses and the medical examination, the researchers are experimenting, researching and writing articles, and the teaching staff are lecturing and conducting individual research. Conclusions: The results of job satisfaction survey showed that occupational satisfaction was the highest in civil servants, researchers, and teaching professions. Job security was the highest in health workers and health inspectors' evaluation centers, and time vacancy was the highest in civilian workers and military/noncommissioned officers. If you want to work in such an institution, you should prepare elements that match your basic literacy and job specific characteristics. And we should try to increase the satisfaction of work even after work.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.12-31
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• 2006

The n-gram inverted index has two major advantages: language-neutral and error-tolerant. Due to these advantages, it has been widely used in information retrieval or in similar sequence matching for DNA and Protein databases. Nevertheless, the n-gram inverted index also has drawbacks: the size tends to be very large, and the performance of queries tends to be bad. In this paper, we propose the two-level n-gram inverted index (simply, the n-gram/2L index) that significantly reduces the size and improves the query performance while preserving the advantages of the n-gram inverted index. The proposed index eliminates the redundancy of the position information that exists in the n-gram inverted index. The proposed index is constructed in two steps: 1) extracting subsequences of length m from documents and 2) extracting n-grams from those subsequences. We formally prove that this two-step construction is identical to the relational normalization process that removes the redundancy caused by a non-trivial multivalued dependency. The n-gram/2L index has excellent properties: 1) it significantly reduces the size and improves the Performance compared with the n-gram inverted index with these improvements becoming more marked as the database size gets larger; 2) the query processing time increases only very slightly as the query length gets longer. Experimental results using databases of 1 GBytes show that the size of the n-gram/2L index is reduced by up to 1.9${\~}$2.7 times and, at the same time, the query performance is improved by up to 13.1 times compared with those of the n-gram inverted index.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.187-196
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• 2008

The system development approach method of the new paradigm, as being standardizing MDA(Model Driven Architecture) in OMG(Object Management Group), the core technique of MDA definite technique structure for system materialization focusing on Model, is to build the design and the statement for system in PIM(Platform Independent Model), Materialization technique, and to build PSM(Platform Specific Model) adapt to materialization environment, and then to be the technique transforming into language Platform suitable to materialization environment. It includes MOF(Meta-Object Facility), UML, XMI(XML Metadata Interchange), CWM(Common Warehouse Metamodel), the core of MDA. Though these operations MDA support automatic tools product the practicable system after carrying out transform operation with the above information. In this thesis, it will be approached how to design and materialize the Bachelor management system based on MDA and the importance of modeling should be emphasized by applying to the approach method based on MDA. It should be suggested how to develop software efficiently, written it out in PIM, and suggested the process transforming PSM into EJB by J2EE Platform. The system is designed and implemented using automatic tool, edit machine(Together Architect 2006 for Eclipse, Edit plus2) supporting MDA and Database Modeling tool(ER/WIN 4.1). In conclusion, it should be suggested the efficiency analysis result for development process of traditional and based on MDA.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.691-704
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• 2021

The development of AI technology has brought about a big change in our lives. The importance of AI and data education is also growing as AI's influence from life to society to the economy grows. In response, the OECD Education Research Report and various domestic information and curriculum studies deal with data literacy and present it as an essential competency. However, the definition of data literacy and the content and scope of the components vary among researchers. Thus, we analyze the semantic similarity of words through Word2Vec deep learning natural language processing methods along with the definitions of key data literacy studies and analysis of word frequency utilized in components, to present objective and comprehensive definition and components. It was revised and supplemented by expert review, and we defined data literacy as the 'basic ability of knowledge construction and communication to collect, analyze, and use data and process it as information for problem solving'. Furthermore we propose the components of each category of knowledge, skills, values and attitudes. We hope that the definition and components of data literacy derived from this study will serve as a good foundation for the systematization and education research of AI education related to students' future competency.

• Journal of vocational education research
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• v.29 no.3
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• pp.61-83
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• 2010

The purpose of this study was to analyze high schools' general and special subject required to successfully complete same stream curriculum which is identical to their major from agricultural high school, and to offer basic data on strategies of organizing agricultural high schools' curriculum for improving universities' major basic competencies. Using purposeful sampling technique, the professors of 116 universities professors in 8 agricultural university were analyzed through the survey research. The result was as follows. first, it appeared that for successful completion of major subjects of the same stream university, the basic science subject such as biology and chemistry has high relation with major basic ability, however math and physics are related highly in agricultural machine and agricultural civil engineering department, economics and math are in agricultural produce distribution department. Second, the basic ability such as linguistic competence and foreign language ability are essential to complete major subject. Third, if we look into relation of agriculture and life science industry stream specialized subject with major basic competencies, we can find considerable similarity between major field of university and subject name of specialized high school. Fourth, the main opinion is that basic concept and principle, laws of nature are should be main contents which is able to be practical, however experiment and practice is in food processing department, and academic theory is in biotechnology department.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.140-153
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• 2019

Cloase-range image information from drones and ground-based camera has been frequently used in the field of disaster mitigation with 3D modeling and mapping. In addition, the utilization of virtual reality(VR) is being increased by implementing realistic 3D models with the VR technology simulating disaster circumstances in large scale. In this paper, we created a VR training program by extracting realistic 3D models from close-range images from unmanned aircraft and digital camera on hand and observed several issues occurring during the implementation and the effectiveness in the case of a VR application in training for disaster mitigation. First of all, we built up a scenario of disaster and created 3D models after image processing with the close-range imagery. The 3D models were imported into Unity, a software for creation of augmented/virtual reality, as a background for android-based mobile phones and VR environment was created with C#-based script language. The generated virtual reality includes a scenario in which the trainer moves to a safe place along the evacuation route in the event of a disaster, and it was considered that the successful training can be obtained with virtual reality. In addition, the training through the virtual reality has advantages relative to actual evacuation training in terms of cost, space and time efficiencies.