• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.6
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• pp.689-698
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• 2009

SOLAP system integrates and complements the functions of both OLAP and GIS systems. This enables users not only to easily access geospatial data but also to analyze and extract information for decision making. In this study a SOLAP system was designed and implemented to provide urban planners with GIS information when making urban planning decisions. Rapid urbanization in Korea has brought about ill-balanced urban structure as the result of development without detailed analysis of urban plans. Systematic urban planning procedures and automated systems are crucial for detail analysis of future development plans. Data regarding the development regulations and current status of land use need to be assessed precisely and instantly. Multi-dimensional aspects of a suggested plan must be formulated instantly and examined thoroughly using 'what if' scenarios to come up with a best possible plan. The SOLAP system presented in this study designed the dimension tables and the fact tables for supplying timely geospatial information to the planners when making decisions regarding urban planning. The database was implemented using open source DBMS and was populated with necessary attribute data which was freely available from the Statistics Korea bureau homepage. It is anticipated the SOLAP system presented in this study will contribute to better urban planning decisions in Korea through more timely and accurate provision of geospatial information.

• Journal of Computing Science and Engineering
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• v.1 no.2
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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.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.135-145
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• 2011

These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through the some experimental scenarios. For the this, we use a sensor data generator implemented by ourselves.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.681-690
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• 2008

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.243-250
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• 2006

A recursive relationship is a relationship among entities of the same class. N : M recursive relationships can be used to represent bills of materials. A bill of materials is a special data structure that occurs frequently in manufacturing applications. This data structure is a hierarchy. Most business dimensions have a hierarchical structure. In this study, a design of a hierarchical dimension of the bill of materials type is carried out. As with other N : M relationships, an intersection table that shows pairs of related rows is created, and this table is transformed into a dimension in the OLAP(OnLine Analytical Processing) model. This transformation consists of two tasks: (1)replacing the first column of the intersection table with the lowest level of the dimension: and (2)replacing the second column of the table with the only upper level of the dimension. A case multidimensional information system using the hierarchical dimension is also developed.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.565-573
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• 2017

The ex-post evaluation of governmental crisis management is an important issues since it is necessary to prepare for the future disasters and becomes the cornerstone of our success as well. In this paper, we propose a data cube model with data mining techniques for the analysis of governmental crisis management strategies and ripple effects of foot-and-mouth(FMD) disease using the online news articles. Based on the construction of the data cube model, a multidimensional FMD analysis is performed using on line analytical processing operations (OLAP) to assess the temporal perspectives of the spread of the disease with varying levels of abstraction. Furthermore, the proposed analysis model provides useful information that generates the causal relationship between crisis response actions and its social ripple effects of FMD outbreaks by applying association rule mining. We confirmed the feasibility and applicability of the proposed FMD analysis model by implementing and applying an analysis system to FMD outbreaks from July 2010 to December 2011 in South Korea.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.18-34
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• 2007

In order to provide various business analysis methods, OLAP(On-Line Analytical Processing) systems represent their data with multidimensional structures. These multidimensional data are often delivered to users in the horizontal format of tables whose columns are corresponding to values of dimension attributes. Since the horizontal tables nay have a large number of columns, they cannot be stored directly in relational database systems. Furthermore, the tables are likely to have many null values (i.e., sparse tables). In order to manage the horizontal tables efficiently, we can store them as the vertical format of tables which has dimension attribute names as their columns thus transforms the columns of horizontal tables into rows. In this way, every queries for horizontal tables have to be transformed into those for vertical tables. This paper proposed a technique for transforming horizontal table queries into vertical table ones by utilizing not only traditional relational algebraic operators but also the PIVOT operator which recent DBMS versions are providing. For achieving this goal, we designed a relational algebraic expression equivalent to the PIVOT operator and we formally proved their equivalence. Then, we developed a transformation technique for horizontal table queries using the PIVOT operator. We also performed experiments to analyze the performance of the proposed method. From the experimental results, we revealed that the proposed method has better performance than existing methods.

• KSCI Review
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• v.14 no.2
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• pp.185-197
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• 2006

A star schema consists of a central fact table, which is surrounded by one or more dimension tables. Each row int the fact table contains a multi-part primary key(or a composite foreign key) along with one or more columns containing various facts about the data stored in the row Each of the composit foreign key components is related to a dimensional table. The combination of keys in the fact table creates a composite foreign key that is unique to the fact table record. The composite foreign key, however, is rarely unique to the fact table record in real-world applications, particularly in financial applications. In order to make the composite foreign key be the determinant in real-world application, some precalculation might be performed in the SQL relational database, and cached in the OLAP database. However, there are many drawbacks to this approach. In some cases, this approach might give users the wrong results. In this paper, an approach to fact table modeling and related MDX query composing, which can be used in real-world applications without performing any precalculation and gives users the correct results, is proposed.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.177-188
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• 2007

A star schema consists of a central fact table, which is surrounded by one or more dimension tables. Each row in the fact table contains a multi-part primary key(or a composite foreign key) along with one or more columns containing various facts about the data stored in the row. Each of the composit foreign key components is related to a dimensional table. The combination of keys in the fact table creates a composite foreign key that is unique to the fact table record. The composite foreign key, however, is rarely unique to the fact table retold in real-world applications, particularly in financial applications. In order to make the composite foreign key be the determinant in real-world application, some precalculation might be performed in the SQL relational database, and cached in the OLAP database. However, there are many drawbacks to this approach. In some cases, this approach might give users the wrong results. In this paper, an approach to fact table modeling and related MDX query composing, which can be used in real-world applications without performing any precalculation and gives users the correct results, is proposed.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.100-107
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• 2011

As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.