• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6A
• /
• pp.870-884
• /
• 2000

Among OLAP(On-Line Analytical Processing) approaches, ROLAP(Relational OLAP) based on the star, snowflake schema which offer the multidimensional analytical method has performance problem and MOLAP (Multidimensional OLAP) based on Multidimensional Database System has scalability problem. In this paper, to solve the limitaions of previous approaches, design and implementation of multidimensional data model based on Object-Relation DBMS was proposed. With the extensibility of Object-Relation DBMS, it is possible to advent multidimensional data model which more expressively define multidimensional concept and analysis functions that are optimized for the defined multidimensional data model. In addition, through the hierarchy between data objects supported by Object-Relation DBMS, the aggregated data model which is inherited from the super-table, multidimensional data model, was designed. One these data models and functions are defined, they behave just like a built-in function, w th the full performance characteristics of Object-Relation DBMS engine.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.643-647
• /
• 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
• /
• v.1 no.2
• /
• pp.125-160
• /
• 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.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.1
• /
• pp.1-9
• /
• 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
• /
• v.13 no.1
• /
• pp.137-147
• /
• 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.

• Korean Journal of Computational Design and Engineering
• /
• v.19 no.2
• /
• pp.103-110
• /
• 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
• /
• v.27 no.3
• /
• pp.243-269
• /
• 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.

• Korean Journal of Organic Agriculture
• /
• v.29 no.4
• /
• pp.471-484
• /
• 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
• /
• v.26 no.4
• /
• pp.371-383
• /
• 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.

• Journal of KIISE:Databases
• /
• v.34 no.4
• /
• pp.315-327
• /
• 2007

The performance of database applications with large sets of multidimensional data depends on the performance of its access methods and the underlying disk system. In modeling the disk system, even though modem disks are manufactured with multiple physical zones, conventional access methods have been developed based on a traditional disk model with many simplifying assumptions. Thus, there is a marked lack of investigation on how to enhance the performance of access methods given a zoned disk model. The paper proposes novel zoning techniques that can be applied to any multidimensional access methods, both static and dynamic, enhancing the effective data transfer rate of underlying disk system by fully utilizing its zone characteristics. Our zoning techniques include data placement algorithms for multidimensional index structures and accompanying localized query processing algorithms for range queries. The experimental results show that our zoning techniques significantly improve the query performance.