• 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.

• 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.

• 한국멀티미디어학회논문지
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• 제14권12호
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• pp.1591-1600
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• 2011

The skyline of a multidimensional data set is the maximal subset whose elements are not dominated by other elements of the set. Skyline computation is considered to be very useful for a decision making system that deals with multidimensional data analyses. Recently, a great deal of interests has been shown to improve the performance of skyline computation algorithms. In order to speedup, the number of comparisons between data elements should be reduced. In this paper, we propose a filter lining scheme to accomplish such objectives. The scheme divides the multidimensional data space into angle-based partitions, and places a filter for each partition, and then connects them together in order to establish the final filter line. The filter line can be used to eliminate data, that are not part of the skyline, from the original data set in the preprocessing stage. The filter line is adaptively improved during the data scanning stage. In addition, skylines are computed for each remaining data partition, and are then merged to form the final skyline. Our scheme is an improvement of the previously reported simple preprocessing scheme using simple filters. The performance of the scheme is shown by experiments.

• Asia pacific journal of information systems
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• 제15권4호
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• pp.153-164
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• 2005

Nowadays, large amounts of XML documents are available in the Internet. Thus, we need to analyze them multidimensionally in the same way as relational data. In this paper, we propose a new framework for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where all fact and dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new OLAP language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate measure, axis and slicer. They incorporate text mining operations for aggregating text data. We apply XML-OLAP to the United States patent XML warehouse to demonstrate multidimensional analysis of XML documents.

• 정보처리학회논문지D
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• 제10D권2호
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• pp.283-292
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• 2003

연속된 일차원 실수로 이루어진 시계열 데이터는 데이터 마이닝이나 데이터 웨어하우징과 같은 다양한 데이터베이스 응용 분야에서 연구되어져 왔다. 그러나 최근의 복잡한 비즈니스 환경에서, 다차원 데이터 시퀀스(multidimensional data sequence : MDS)는 일차원 시계열 데이터와 더불어 그 중요성이 더해가고 있다. 다차원 데이터 시퀀스의 예로써, 비디오 스트림은 색상과 질감 등의 속성들로 이루어진 다차원 공간상에서 MDS로 나타낼 수 있다. 본 논문에서는 패턴 유사성 검색에서 사용되는 효과적인 유사성 척도를 제시한다. 하나의 MDS는 여러 개의 세그먼트(segment)로 나누어지며, 각 세그먼트는 다양한 의미적인 특징들로 표현된다. 유사성 척도는 이러한 세그먼트에 대해서 정의되는데 이 척도를 사용하여 어떤 주어진 질의 시퀀스에 대하여 무관한 세그먼트들은 검색 대상에서 일차적으로 제외된다. 데이터 시퀀스와 질의 시퀀스 모두 세그먼트 단위로 분할되며, 질의 처리는 전체 시퀀스의 모든 데이터를 검색하지 않고 데이터 세그먼트와 질의 세그먼트의 특징을 비교하는 것을 기초로 하여 수행된다.

• 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.

• 한국정보과학회논문지:데이타베이스
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• 제28권4호
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• pp.655-664
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• 2001

비디오 스트림이나 음성 아날로그 신호와 같은 연속된 데이타는 특징 공간(feature space)에서 다차원 데이타 시퀀스(multidimensional data sequence)로 모델링될 수 있다. 본 논문에서는 이러한 다차원 데 이타 시퀀스의 효과적인 클러스터링 기법에 대하여 연구한다. 각 시퀀스는 차후의 저장 및 유사성 검색 (similarity search)을 효율적으로 실행하기 위하여 소수 개의 하이퍼 사각형 (hyper-rectangle) 형태의 클러스터로 표현된다. 본 논문에서는 사전에 정의된 수준의 클러스터링 품질을 보장하는 선형 복잡도를 갖는 클러스터링 알고리즘을 제시하고, 다양한 비디오 데이타에 관한 실험을 통하여 알고리즘의 적합성을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.617-636
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• 2021

This paper presents a privacy-preserving data aggregation scheme deals with the multidimensional data. It is essential that the multidimensional data is rarely mentioned in all researches on smart grid. We use the Paillier Cryptosystem and blinding factor technique to encrypt the multidimensional data as a whole and take advantage of the homomorphic property of the Paillier Cryptosystem to achieve data aggregation. Signature and efficient batch verification have also been applied into our scheme for data integrity and quick verification. And the efficient batch verification only requires 2 pairing operations. Our scheme also supports fault tolerance which means that even some smart meters don't work, our scheme can still work well. In addition, we give two extensions of our scheme. One is that our scheme can be used to compute a fixed user's time-of-use electricity bill. The other is that our scheme is able to effectively and quickly deal with the dynamic user situation. In security analysis, we prove the detailed unforgeability and security of batch verification, and briefly introduce other security features. Performance analysis shows that our scheme has lower computational complexity and communication overhead than existing schemes.

• 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.