• Journal of the HCI Society of Korea
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• v.1 no.2
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• pp.51-58
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• 2006

Information visualization, generally speaking, consists of three steps: transform from raw data to data model, visual mapping from data model to visual structure, and transform from visual structure to information model. In this paper, we propose a visual mapping method from spatiotemporal table information, which is related to events in large-scale building, to 3D map metaphor. The process has also three steps as follows. First, after analyzing the table attributes, we carefully define a context to fully represent the table-information. Second, we choose meaningful attribute sets from the context. Third, each meaningful attribute set is mapped to one well defined visual structure. Our method has several advantages. First, users can intuitively achieve non-spatial information through the 3D map which is a powerful spatial metaphor. Second, this system shows various visual mapping method applicable to other data models in the form of table, especially GIS. After describing the whole concept of our visual mapping, we will show the results of implementation for several requests.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.103-105
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• 2006

최근 기업들은 분산된 조직과 각 조직의 목적에 따라 데이터베이스도 분산되어 있기 때문에 이들 간의 공유 및 협업을 통한 상호 운용성을 지원하기 어려우므로 일관적인 형태로 연동하기 위해서 메타데이터 수준의 표준이 필요하다. 또한 협업적인 거래환경에서의 EAI시스템은 다양한 정보 시스템에서 관리되는 지식들을 유기적으로 통합하고 공유함으로서 효율적인 검색 및 비용절감 등 많은 효과를 기대할 수 있다. 그러나 기존의 시스템은 특정 목적에 따라 관리되고 공유되므로 사실상 통합 외 공유에는 상당한 어려움이 있다. 따라서 본 논문에서 제시하는 XMDR은 온톨로지와 메타데이터 결합된 형태로 각종 표준들을 일관적인 형태로 온톨로지와 시소러스 개념을 도입함으로서 데이터수준의 정보를 통합 하기위한 메타데이터 공유 및 정보 시스템 통합의 일관성을 유지 할 수 있다. 본 논문에서 제시되는 브리지 XMDR 검색시스템은 원시데이터 계층, XMDR 계층. 브리지 XMDR 계층으로 3계층으로 구성된다. XMDR 계층은 분산된 데이터베이스의 속성표현의 표준과 관계성을 정의한 표준 온톨로지, 카테고리 분류 온틀로지, 사이트의 정보를 제공하는 로케이션 온톨로지로 구성되는 XMDR을 정의한다. 브리지 XMDR 계층은 XMDR간의 정보를 공유하기 위한 공유 도메인 속성을 추출한 하이브리드 통합방식으로 업무간의 의미적 통합이 가능하다.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.301-314
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• 2008

The existing event recognition is accomplished with the limited systematic foundation, and thus much longer learning time is needed for emergency scenario interpretation due to large scale of probability data. In this paper, we propose a method for nile-based event recognition of an independent object(human) which extract a feature vectors from the object and analyze the behavior pattern of each object and interpretation of emergency scenarios using a probability and object's events. The event rule of an independent object is composed of the Primary-event, Move-event, Interaction-event, and 'FALL DOWN' event and is defined through feature vectors of the object and the segmented motion orientated vector (SMOV) in which the dynamic Bayesian network is applied. The emergency scenario is analyzed using current state of an event and its post probability. In this paper, we define diversified events compared to that of pre-existing method and thus make it easy to expand by increasing independence of each events. Accordingly, semantics information, which is impossible to be gained through an.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.55-68
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• 2024

Plant engineering companies create or refer to requirements documents for each related field, such as plant process/equipment/piping/instrumentation, in different engineering departments. The process-related requirements document includes not only a description of the process but also the requirements of the equipment or related facilities that will operate it. Since the authors and reviewers of the requirements documents are different, there is a possibility that inconsistencies may occur between equipment or parts design specifications described in different requirement documents. Ensuring consistency in these matters can increase the reliability of the overall plant design information. However, the amount of documents and the scattered nature of requirements for a same equipment and parts across different documents make it challenging for engineers to trace and manage requirements. This paper proposes a method to analyze requirement sentences and calculate the similarity of requirement sentences in order to identify semantically identical sentences. To calculate the similarity of requirement sentences, we propose a named entity recognition method to identify compound words for the parts and properties that are semantically central to the requirements. A method to calculate the similarity of the identified compound words for parts and properties is also proposed. The proposed method is explained using sentences in practical documents, and experimental results are described.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.27-35
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• 2018

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.57-78
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• 2011

Paraphrasing is the act of writing a text using other words without altering the meaning. Paraphrases can be used in many fields of natural language processing. In particular, paraphrases can be incorporated in machine translation in order to improve the coverage and the quality of translation. Recently, the approaches on paraphrase extraction utilize bilingual parallel corpora, which consist of aligned sentence pairs. In these approaches, paraphrases are identified, from the word alignment result, by pivot phrases which are the phrases in one language to which two or more phrases are connected in the other language. However, the word alignment is itself a very difficult task, so there can be many alignment errors. Moreover, the alignment errors can lead to the problem of selecting incorrect pivot phrases. In this study, we propose a method in paraphrase extraction that discriminates good pivot phrases from bad pivot phrases. Each pivot phrase is weighted according to its reliability, which is scored by considering the lexical and part-of-speech information. The experimental result shows that the proposed method achieves higher precision and recall of the paraphrase extraction than the baseline. Also, we show that the extracted paraphrases can increase the coverage of the Korean-English machine translation.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.647-656
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• 2003

In Webspace, mining traversal patterns is to understand user's path traversal patterns. On this mining, it has a unique characteristic which objects (for example, URLs) may be visited due to their positions rather than contents, because users move to other objects according to providing information services. As a consequence, it becomes very complex to extract meaningful information from these data. Recently discovering traversal patterns has been an important problem in data mining because there has been an increasing amount of research activity on various aspects of improving the quality of information services. This paper presents a Dynamic Link Recommendation (DLR) algorithm that recommends link sets on a Web site through mining frequent traversal patterns. It can be employed to any Web site with massive amounts of data. Our experimentation with two real Weblog data clearly validate that our method outperforms traditional method.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.123-133
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• 2010

XML has been become the new standard for publishing and exchanging data on the Web. However, most business data is still stored and maintained in relational database management systems. As such, there is an increasing need to efficiently publish relational data as XML data for Internet-based applications. The most important issue in the transformation is to reflect structural and semantic relations of RDB to XML schema exactly. Most transformation approaches have been done to resolve the issue, but those methods have several problems. In this paper, we discuss algorithm in transforming a relational database schema into corresponding XML schema in XML Schema. We aim to achieve not only explicit/implicit referential integrity relation information but also high level of nested structure while introducing no data redundancy for the transformed XML schema. To achieve these goals, we propose a transformation model which is redundancy free and then we improve the XML Schema structure by exploring more nested structure.