• 한국콘텐츠학회논문지
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• 제12권4호
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• pp.76-85
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• 2012

오피니언마이닝 기법은 대량의 고개리뷰들에 나타나는 핵심개체 또는 속성들에 대하여 고객들이 느끼는 긍정 또는 부정의 정도를 계산할 수 있지만, 그 분석능력이 단순하다는 한계가 있다. 본 논문에서는 온라인 고객리뷰들에 대하여 다차원적으로 분석할 수 있는 기법을 제안하였다. 기존의 OLAP기법을 텍스트 데이터형에 적용할 수 있도록 수정하였다. 다차원 분석모델은 명사축과 형용사축, 문서축으로 구성되는 3차원 공간 개념을 4개의 관계형 테이블로 실체화 한 것이다. 다차원 분석모델은 기존의 오피니언마이닝, 정보요약, 클러스터링 알고리즘들을 융합할 수 있는 새로운 틀이라는 점에서 그 가치가 있다. 본 논문에서 제안한 다차원 분석모델과 알고리즘들을 실제로 구현하여 온라인 고객리뷰에 대한 복잡한 분석을 수행할 수 있음을 확인하였다.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.183-196
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• 2011

There has recently been a surge of interest in relational database mining that aims to discover useful patterns across multiple interlinked database relations. It is crucial for a learning algorithm to explore the multiple inter-connected relations so that important attributes are not excluded when mining such relational repositories. However, from a data privacy perspective, it becomes difficult to identify all possible relationships between attributes from the different relations, considering a complex database schema. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. Thus, we are at risk of disclosing unwanted knowledge when publishing the results of a data mining exercise. For instance, consider a financial database classification task to determine whether a loan is considered high risk. Suppose that we are aware that the database contains another confidential attribute, such as income level, that should not be divulged. One may thus choose to eliminate, or distort, the income level from the database to prevent potential privacy leakage. However, even after distortion, a learning model against the modified database may accurately determine the income level values. It follows that the database is still unsafe and may be compromised. This paper demonstrates this potential for privacy leakage in multi-relational classification and illustrates how such potential leaks may be detected. We propose a method to generate a ranked list of subschemas that maintains the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We illustrate and demonstrate the effectiveness of our method against a financial database and an insurance database.

• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2005년도 춘계학술대회 발표 논문집
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• pp.65-78
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• 2005

Nowadays, large amounts of XML documents are available on the Internet. Thus, we need to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new frame-work for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents.

• 한국멀티미디어학회논문지
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• 제7권9호
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• pp.1329-1338
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• 2004

데이터마이닝은 방대한 데이터로부터 다차원적인 정보를 추출하는 것이다. 방대하게 구축되어 있는 데이터베이스에서 임의의 테이블의 컬럼에 대해 참조 할 수 있는 정보는 단순하게 컬럼명과 자료형 혹은 간단한 주석 정도이다. 그러한 비구조적이고 빈약한 내용만으로는 데이터마이닝을 위한 자료수집 및 자료탐색 단계에서 컬럼의 용도와 특성 및 스키마를 파악하여 데이터를 정제하고 수집하는 것이 난해 할 뿐만 아니라 너무 많은 시간이 소요된다. 이러한 문제를 해결하기 위해 본 논문에서는 관계형 데이터베이스 환경에서 데이터준비 단계 에 대부분의 시간을 소요하는 문제를 해결하기 위한 방안을 제안한다. 즉, 데이터 준비 단계에서 유용한 요소들을 메타데이터의 표준인 ISO/IEC : 11179 MDR (MetaData Registry) 규격에 맞는 표준 메타데이터를 제안하고, 이기종 및 이질 DBMS간에 호환 가능한 XML 기반의 분산 MDR 검색 시스템 구조를 제안한다.