• 정보처리학회논문지D
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• 제14D권5호
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• pp.483-490
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• 2007

최근에 데이터 스트림을 분석하여 잠재되어 있는 지식을 발견하기 위한 마이닝 방법에 대한 연구가 진행되고 있다. 그러나 대부분의 지지도 기반의 마이닝 방법들은 일정 주기 동안에 미리 정의된 지지도 이상의 발생 빈도를 갖는 이벤트만을 고려함으로써 발생 빈도에 비하여 중요도가 높은 이벤트를 간과하는 문제점을 가지고 있다. 본 논문에서는 이상 이벤트에 대한 연관 규칙을 탐사할 수 있는 SM-AF 방법을 제안한다. SM-AF 방법은 이상 이벤트가 감지된 윈도우만 고려하여 연관 정보를 탐사함으로써 자주 발생하지 않더라도 중요도가 높은 이벤트에 대한 연관 정보를 탐사할 수 있다. 또한 SM-AF 방법은 이상 이벤트에 대한 의미 있는 희소 항목 집합과 주기적인 이벤트 집합도 탐사한다. 그리고 다양한 실험을 통하여 SM-AF 방법이 기존의 연관 규칙 방법들에 비하여 우수함을 확인하였다.

• 대한간호
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• 제19권5호통권108호
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• pp.55-68
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• 1980

The purpose of this study is to find out most wanted nursing contents (behavior) of the dying patient and his family and to discover the obstacles to helping the dying as analysis of 53 factual reports on the care of dying patients on American Journal of

• Journal of applied mathematics & informatics
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• 제36권1_2호
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• pp.115-120
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• 2018

In this paper, we discover symmetric properties for generalized Carlitz's q-tangent polynomials.

• 한국응용약물학회:학술대회논문집
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• 한국응용약물학회 1996년도 제4회 추계심포지움
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• pp.147-152
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• 1996

In order to discover new useful antiinflammatory agent, many new compounds are continually being synthesized and tested. A certain peptide which contains an anisolated glutamic acid has been reported to show considerable biological activities.

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2003년도 The 12th Korea Genome Conference
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• pp.38.2-38.2
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• 2003

• 대한간호학회지
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• 제25권2호
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• pp.295-315
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• 1995

This qualitative nursing research used a hermeneutic phenomenological approach to discover meaning in mid - aged women's experience of mastectomy. The ultimate aim of the inquiry was to discover the essence of mid-aged women's experience and promote understanding. The study was guided by Van Manen's method for doing research. The method of inquiry included. turning to phenomenon of interest ; inquiring and investigating experience as it was lived rather than as conceptualized ; reflecting and analyzing essential themes which characterize phenomenon : and describing phenomenon through art of writing and rewriting. Multiple strategies for data collecting were needed : in depth face-to-face interview ; analysis of women's writings ; artistic works : and analysis of examples of phenomenon in art, litera lure, and drama. Although the experience was different for all of the women interviewd, essential themes of experience emerged : Self-discovering of vicious disease, Divining punishment-unfortunate women, loss of self though the scar, a night-mare, change of life values and attitudes, rediscovering living alone, living through with fearing of death. Findings from artistic and creative inquiry further validated findings and meaning descovered. The study illuminated meaning and simultaneously validated the phenomenological research process. Essential themes for understanding women's experience, implications for education, research, and practice, direction and need for continuing inquiry were identified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.1260-1281
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• 2015

As the number of services available on the Web increases, it is vital to be able to identify which services can be trusted. Since there can be an extremely large number of potential services that offer similar functionality, it is challenging to select the right ones. Service requestors have to decide which services closelysatisfy their needs, and theymust worry about the reliability of the service provider. Although an individual service can be trusted, a composed service is not guaranteed to be trustworthy. In this paper, we present a trust model that supports service discovery and composition based on trustworthiness. We define a method to evaluate trust in order to discover trustworthy services. We also provide a method to perform trust estimation for dynamic service composition, and we present results of two experiments. The proposed model allows for service requestors to obtain the most trustworthy services possible. Our mechanism uses direct and indirect user experience to discover the trustworthiness of the services and service providers. Moreover, composing services based on quantitative trust measurements will allow for consumers to acquire a highly reliable service that meet their quality and functional requirements.

• 한국IT서비스학회지
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• 제15권3호
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• ETRI Journal
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• 제30권3호
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• pp.421-431
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• 2008

In this paper, we offer a new technique to discover frequent spatiotemporal patterns from a moving object database. Though the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and timing constraints on moving sequences makes the computation feasible. The proposed technique includes two algorithms, AllMOP and MaxMOP, to find all frequent patterns and maximal patterns, respectively. In addition, to support the service provider in sending information to a user in a push-driven manner, we propose a rule-based location prediction technique to predict the future location of the user. The idea is to employ the algorithm AllMOP to discover the frequent movement patterns in the user's historical movements, from which frequent movement rules are generated. These rules are then used to estimate the future location of the user. The performance is assessed with respect to precision and recall. The proposed techniques could be quite efficiently applied in a location-based service (LBS) system in which diverse types of data are integrated to support a variety of LBSs.

• Journal of Computing Science and Engineering
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• 제6권3호
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• pp.193-206
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• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.