• 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 the Korea Safety Management & Science
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• v.6 no.1
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• pp.71-79
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• 2004

According to the changing environment of management, the concept of the one-to-one marketing is appeared in the environment of management by high Quality that customers needs. In this paradigm, CRM(Customer Relationship Management) offering integrated multiple view points is able to be executed by the advance of Information Technology (IT). In this study, the environment and the management status of the steel company that CRM implemented is analyzed. This study presents the system architecture which applies OLAP(on-line analytical processing), web technologies, and logistics service. Then this research presents a variety of effects and development direction in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.35-38
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• 2006

데이터 웨어하우스에서는 OLAP(On-Line Analytical Processing) 연산을 제공하기 위해 다차원 데이터를 큐브의 형태로 관리한다. 특히, 공간 차원과 같이 데이터 큐브의 차원에 개념 계층이 존재하는 경우 사용자는 특정 계층에 대한 집계 결과를 요구한다. 기조의 데이터 큐브의 구조들은 차원의 개념 계층을 지원하지 못하거나 지원하더라도 시간이나 공간적 비용에 대해 비효율적이다. 본 논문에서는 공간 데이터 웨어하우스에서 공간 개념 계층을 이용하여 효율적인 계층별 영역 집계연산을 지원하는 공간 데이터 큐브를 제안한다. 이는 개념 계층을 DAG(Directed Acyclic Graph) 형태로 표현하여 구성된 여러 개의 차원들을 공간차원의 지역성을 기준으로 연결한 구조이다. 이러한 구조를 갖는 큐브를 이용하면, 데이터 검색 시 상위 계층부터 아래 방향으로 탐색하기 때문에 각 차원에 대한 효율적인 검색이 가능하다. 특히, 공간 개념 계층에 대한 DAG를 이용하면, 공간적 지역성에 따른 영역 검색을 지원할 수 있다. 성능평가에서 개념 계층이 적용된 질의에 대한 실험을 통해 제안 기법이 기존 기법들에 비해 저장 공간 효율성 및 질의 응답 성능이 우수함을 증명한다.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.463-474
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• 2009

To analyze multidimensional data conveniently and efficiently, OLAP (On-Line Analytical Processing) systems or e-business are widely using views in a horizontal form to represent measurement values over multiple dimensions. These views can be stored as materialized views derived from several sources in order to support accesses to the integrated data. The horizontal views can provide effective accesses to complex queries of OLAP or e-business. However, we have a problem of occurring maintenance of the horizontal views since data sources are distributed over remote sites. We need a method that propagates the changes of source tables to the corresponding horizontal views. In this paper, we address incremental maintenance of horizontal views that makes it possible to reflect the changes of source tables efficiently. We first propose an overall framework that processes queries over horizontal views transformed from source tables in a vertical form. Under the proposed framework, we propagate the change of vertical tables to the corresponding horizontal views. In order to execute this view maintenance process efficiently, we keep every change of vertical tables in a differential file and then modify the horizontal views with the differential file. Because the differential file is represented as a vertical form, its tuples should be converted to those in a horizontal form to apply them to the out-of-date horizontal view. With this mechanism, horizontal views can be efficiently refreshed with the changes in a differential file without accessing source tables. Experimental results show that the proposed method improves average performance by 1.2$\sim$5.0 times over the existing methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1910-1915
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• 2008

The load analysis for the distribution system and facilities has relied on measurement equipment. Moreover, load monitoring incurs huge costs in terms of installation and maintenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 minutes using meter reading data that can be obtained from a power consumer every 15 minute or a month even without setting up any measuring equipment. After the data warehouse is constructed by interfacing the legacy system required for the load calculation, the relationship between the distribution system and the power consumer is established. Once the load pattern is forecasted by applying clustering and classification algorithm of temporal data mining techniques for the power customer who is not involved in Automatic Meter Reading(AMR), a single-line diagram per feeder is created, and power flow calculation is executed. The calculation result is analyzed using various temporal and spatial analysis methods such as Internet Geographic Information System(GIS), single-line diagram, and Online Analytical Processing (OLAP).

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.939-947
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• 2016

As the number of legacy database systems and the size of data to manipulate have been vastly increased, it has become more difficult and complex to analyze characteristics of data. To improve the efficiency of data analysis and help administrators to make decisions in business life, BI(Business Intelligence) system is used. To construct data warehouse and cube from legacy database systems makes it easy and fast to transform raw data into integrated and categorized meaningful information. In this paper, we built a BI system for an University administration. Several source system databases were integrated to data warehouse to build data cubes. The implemented BI system shows much faster data analysis and reporting ability than the manipulation in legacy systems. It is especially efficient in multi dimensional data analysis, nonetheless in single dimensional analysis.

• IE interfaces
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• v.16 no.2
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• pp.229-239
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• 2003

The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. View maintenance, one of them, is to maintain view in response to updates in source data. Keeping the view consistent with updates to the base relations, however, can be expensive, since it may involve querying external sources where the base relations reside. In order to reduce maintenance costs, it is possible to maintain the views using information that is strictly local to the data warehouse. This process is usually referred to as "self-maintenance of views". A number of algorithm have been proposed for self maintenance of views where they keep some additional information in data warehouse in the form of auxiliary views. But those algorithms did not consider a consistency of materialized views using view self-maintenance. The purpose of this paper is to research consistency problem when self-maintenance of views is implemented. The proposed "conveyor algorithm" will resolved a complete consistency of materialized view using self-maintenance with considering network delay. The rationale for conveyor algorithm and performance characteristics are described in detail.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1421-1434
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• 2006

Spatial data warehouses provide analytical information for decision supports using SOLAP (Spatial On-Line Analytical Processing) operations. Many researches have been studied to reduce analysis cost of SOLAP operations using pre-aggregation methods. These methods use the index composed of fixed size nodes for supporting the concept hierarchy. Therefore, these methods have many unused entries in sparse data area. Also, it is impossible to support the concept hierarchy in dense data area. In this paper, we propose a dynamic pre-aggregation index method based on the spatial hierarchy. The proposed method uses the level of the index for supporting the concept hierarchy. In sparse data area, if sibling nodes have a few used entries, those entries are integrated in a node and the parent entries share the node. In dense data area, if a node has many objects, the node is connected with linked list of several nodes and data is stored in linked nodes. Therefore, the proposed method saves the space of unused entries by integrating nodes. Moreover it can support the concept hierarchy because a node is not divided by linked nodes. Experimental result shows that the proposed method saves both space and aggregation search cost with the similar building cost of other methods.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.157-168
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• 2007

For effective analyses in various business applications, OLAP(On-Line Analytical Processing) systems represent the multidimensional data as the horizontal format of tables whose columns are corresponding to values of dimension attributes. Because the traditional RDBMSs have the limitation on the maximum number of attributes in table columns(MS SQLServer and Oracle permit each table to have up to 1,024 columns), horizontal tables cannot be directly stored into relational database systems. In this paper, we propose various efficient optimization strategies in transforming horizontal queries to equivalent vertical queries. To achieve this goral, we first store a horizontal table using an equivalent vertical table, and then develop various query transformation rules for horizontal table queries using the PIVOT operator. In particular, we propose various alternative query transformation rules for the basic relational operators, selection, projection, and join. Here, we note that the transformed queries can be executed in several ways, and their execution times will differ from each other. Thus, we propose various optimization strategies that transform the horizontal queries to the equivalent vertical queries when using the PIVOT operator. Finally, we evaluate these methods through extensive experiments and identify the optimal transformation strategy when using the PIVOT operator.

• Interdisciplinary Bio Central
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• v.3 no.2
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• pp.7.1-7.6
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• 2011

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.