• 한국통신학회논문지
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• 제25권6A호
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• pp.870-884
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• 2000

OLAT(On-Line Analytical Processing) 기법에서 스타 또는 눈송이(snowflake) 스키마에 기반한 ROLAP(Relational OLAP)은 성능 저하라는 문제가 있고, 다차원 데이터베이스에 기반한 MOLAP(Multidinmensional OLAP)은 데이터 크기 증가에 따른 공간 문제가 있다. 본 논문에서는 기존의 OLAP 시스템이 이러한 문제점을 해결하기 위해서 객체-관계 DBMS에 기반한 다차원 데이터 모델을 제안하였다. 객체-관계 DBMS가 가지는 확장성 특징을 사용하여 다차원 데이터 모델에 최적화된 다차원 개념과 함수를 정의할 수 있었다. 또한 객체-관계 DBMS의 객체간 계승 기능을 통하여 상위 테이블을 계승받는 요약 다차원 데이터 큐브의 다차원 데이터 모델을 설계하였다. 이와 같은 OLAP을 위한 데이터 타입과 함수가 정의되면, 새로운 객체-관계 DBMS 엔진과 같이 내장된 기능처럼 동작되어 성능향상이 가능하다. 또한 객체 관계 DBMS의 하나인 Informix Universal Server와 클라이언트 개발 도구를 이용하여 제안된 다차원 데이터 모델을 구현하였다.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.643-647
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• 2021

The use of data analysis on large volumes of data constitutes a challenge for deducting knowledge and new information. Data can be heterogeneous and complex: Semi-structured data (Example: XML), Data from social networks (Example: Tweets) and Factual data (Example: Spreading of Covid-19). In this paper, we propose a generic multidimensional model in order to analyze complex data, according to several dimensions.

• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.125-160
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• 2007

Data Warehousing and OLAP (On-Line Analytical Processing) have turned into the key technology for comprehensive data analysis. Originally developed for the needs of decision support in business, data warehouses have proven to be an adequate solution for a variety of non-business applications and domains, such as government, research, and medicine. Analytical power of the OLAP technology comes from its underlying multidimensional data model, which allows users to see data from different perspectives. However, this model displays a number of deficiencies when applied to non-conventional scenarios and analysis tasks. This paper presents an attempt to systematically summarize various extensions of the original multidimensional data model that have been proposed by researchers and practitioners in the recent years. Presented concepts are arranged into a formal classification consisting of fact types, factual and fact-dimensional relationships, and dimension types, supplied with explanatory examples from real-world usage scenarios. Both the static elements of the model, such as types of fact and dimension hierarchy schemes, and dynamic features, such as support for advanced operators and derived elements. We also propose a semantically rich graphical notation called X-DFM that extends the popular Dimensional Fact Model by refining and modifying the set of constructs as to make it coherent with the formal model. An evaluation of our framework against a set of common modeling requirements summarizes the contribution.

• 대한산업공학회지
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• 제39권1호
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• pp.1-9
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• 2013

The current manufacturers have generated tremendous amount of digitized product data to efficiently share and exchange it with other stakeholders or various software systems for product development. The digitized product data is a valuable asset for manufacturers, and has a potential to support high level strategic decision makings needed at many stages in product development. However, the lack of studies on extraction of key performance indicators(KPIs) from product data management(PDM) databases has prohibited manufacturers to use the product data to support the decision makings. Therefore this paper examines a possibility of an architecture that supports KPIs for evaluation of product development performances, by applying multidimensional product data model and on-line analytic processing(OLAP) to operational databases of product data management. To validate the architecture, the paper provides a prototype product data management system and OLAP applications that implement the multidimensional product data model and analytic processing.

• Journal of Information Technology Applications and Management
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• 제13권1호
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• pp.137-147
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• 2006

Spatiotemporal data are records of the spatial changes of moving objects over time. Most data in corporate databases have a spatiotemporal nature, but they are typically treated as merely descriptive semantic data without considering their potential visual (or cartographic) representation. Businesses such as geographical CRM, location-based services, and technologies like GPS and RFID depend on the storage and analysis of spatiotemporal data. Effectively handling the data analysis process may be accomplished through spatiotemporal data warehouse and spatial OLAP. This paper proposes a multidimensional model for spatiotemporal data analysis, and cartographically represents the results of the analysis.

• 한국CDE학회논문집
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• 제19권2호
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• pp.103-110
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• 2014

Engineering changes are indispensable engineering and management activities for manufactures to develop competitive products and to maintain consistency of its product data. Analysis of engineering changes provides a core functionality to support decision makings for engineering change management. This study aims to develop a method for analysis of engineering changes based on On-Line Analytical Processing (OLAP), a proven database analysis technology that has been applied to various business areas. This approach automates data processing for engineering change analysis from product databases that follow an international standard for product data management (PDM), and enables analysts to analyze various aspects of engineering changes with its OLAP operations. The study consists of modeling a standard PDM database and a multidimensional data model for engineering change analysis, implementing the standard and multidimensional models with PDM and data cube systems and applying the implemented data cube to core functions of engineering change management, the evaluation and propagation of engineering changes.

• East Asian Economic Review
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• 제27권3호
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• pp.243-269
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• 2023

The multidimensional poverty index is an indicator system established for defining and evaluating poverty, to understand poverty in dimensions beyond just monetary scarcity. Based on income, education, health, living standards, and social dimensions, this article measures and analyzes the level of multidimensional poverty in Xinjiang using the AlkireFoster method, with cross-sectional data obtained from a 2022 survey. Probit model is constructed for regression analysis, further considering the impact of education on enhancing feasible capabilities and alleviating multidimensional poverty at the post-poverty alleviation era. The data shows that many people still face significant challenges from the perspective of multidimensional poverty; the decomposition results of each dimension show that education contributes more to the multidimensional poverty; the regression analysis results show that the higher the education level, the lower the multidimensional poverty; heterogeneity analysis revealed that the inhibitory effect of education on multidimensional poverty is greater for females than males, and the poverty reduction effect of education mainly concentrates on middle-aged and older individuals. This article is meaningful for exploring strategies to alleviate multidimensional poverty in ethnic minority regions in frontier areas in the new era, accelerating regional economic development, and achieving shared prosperity.

• 한국유기농업학회지
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• 제29권4호
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• pp.471-484
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• 2021

The characteristics of poverty can be comprehensively revealed from the two angles of income and multidimensional. This paper compares China's rural income poverty measure with multidimensional poverty index using data from China Family Panel Studies (CFPS) by focusing on the static and dynamic disparities, and analyzes the factors influencing poverty through the Logit model. The results show that there exists a substantial mismatch in who is deemed poor, 60 percent of multidimensional poverty households are not considered poor in terms of income poverty, and 70 percent of income poverty households are not considered poor in terms of multidimensional poverty; There is a high level of disparity between the dynamics of the two measures of poverty. Among those who rose in the income dimension, only about 7 percent also rose in the multidimensional measure from 2016 to 2018.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• 한국정보과학회논문지:데이타베이스
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• 제34권4호
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• pp.315-327
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• 2007

대용량의 다차원 데이타를 다루는 데이타베이스 응용분야에서는 접근 방법 및 기반 디스크 시스템이 전반적인 성능에 중요한 영향을 미친다. 현재 생산되고 있는 많은 디스크들은 다중의 물리적 존을 갖도록 설계되고 있다. 그러나 기존의 접근 방법에 대한 연구는 단순한 가정의 전통적인 디스크 모델에 기반을 두고 진행되어 왔고, 다중 존 디스크를 고려한 접근 방법에 대한 연구는 현재까지 거의 이루어지지 않고 있다. 본 논문에서는 다중 존 디스크 환경에서 실질적인 데이타 전송률을 향상시키기 위해, 정적 및 동적 환경 모두를 고려한 다차원 인덱스 구조의 디스크 저장 기법을 제안한다. 이를 위해 다차원 인덱스 구조를 다중 존 디스크에 효과적으로 배치하는 알고리즘을 제시하고, 범위 질의에 대해 지역화된 질의 처리 기법을 제안한다. 또한 실험을 통하여 본 논문이 제안한 기술이 질의 성능을 획기적으로 향상시킨다는 것을 증명한다.