• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1395-1409
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• 2006

A spatial data warehouse has spatial data cube composed of multi-dimensional data for efficient OLAP(On-Line Analytical Processing) operations. A spatial data cube supporting concept hierarchies holds huge amount of data so that many researches have studied a incremental update method for minimum modification of a spatial data cube. The Cube, however, compressed by eliminating prefix and suffix redundancy has coalescing paths that cause update inconsistencies for some updates can affect the aggregate value of coalesced cell that has no relationship with the update. In this paper, we propose incremental batch update method of a spatial data cube. The proposed method uses duplicated nodes and extended node structure to avoid update inconsistencies. If any collision is detected during update procedure, the shared node is duplicated and the duplicate is updated. As a result, compressed spatial data cube that includes concept hierarchies can be updated incrementally with no inconsistency. In performance evaluation, we show the proposed method is more efficient than other naive update methods.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.917-926
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• 2003

More views in data warehouse, can respond to the users more rapidly because the user's requests may be processed by utilizing only the materialized views with higher probabilities rather than accessing base relations. But, more views cause longer update duration of all the materialized views. During the update time, queries of users can not be processed because accesses to the datawarehouse are blocked. Thus, the update durations of the materialized views are necessary to be reduced. In this paper, we propose algorithm the update duration of the materialized views owing to managing by distinguishing insecton/deletion operations. Though there might overheads for distinguishing insertion/deletion, we concluded that the proposed algorithm could be excellent than others.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.297-312
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• 2005

Aggregation is an operation that plays a key role in multidimensional OLAP (MOLAP) systems of data warehouse. Existing aggregation operations in MOLAP have been proposed for file structures such as multidimensional arrays. These tile structures do not work well with skewed distributions. This paper presents a physical design methodology for storage structures ni MOLAP that use the multidimensional tile organizations adapting to a skewed distribution. In uniform data distribution, we first show that the performance of multidimensional analytical processing is highly affected by the similarity of the shapes between query regions and page regions in the domain space of the multidimensional file organizations. And than, in skewed distributions, we reflect the effect of data distributions on the design by using the shapes of the normalized query regions that are weighted with data density of those query regions. Finally, we demonstrate that the physical design methodology theoretically derived is indeed correct in real environments. In the two-dimensional file organizations, the results of experiments indicate that the performance of the proposed method is enhanced by more than seven times over the conventional method. We expect that the performance will be more enhanced when the dimensionality is more than two. The result confirms that the proposed physical design methodology is useful in a practical way.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.239-252
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• 2005

The major reason that spatial data warehousing has attracted a great deal of attention in business GIS in recent years is due to the wide availability of huge amount of spatial data and the imminent need for fuming such data into useful geographic information. Therefore, this research has been focused on designing and implementing the pilot tested system for spatial decision making. The purpose of the system is to predict targeted marketing area by discriminating the customers by using both transaction quantity and the number of customer using credit card in department store. Moreover, the pilot tested system of this research provides OLAP tools for interactive analysis of multidimensional data of geographically various granularities, which facilitate effective spatial data mining. focused on the analysis methodology, the case study is aiming to use GIS and clustering for knowledge discovery. Especially, the importance of this study is in the use of snowflake schema model capabilities for GIS framework.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1156-1166
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• 1999

의사결정 시스템은 전사적인 의사결정과 전략적 정보수집을 위해 거대한 량의 정보를 빠른 시간내에 제공할 것을 요구한다. 데이타 웨어하우스는 이러한 정보를 신속히 제공하기 위해 여러 지역 데이타베이스로부터 필요한 정보를 사전에 추출하고 가공 및 통합하여 별도의 저장공간에 저장한다. 일반적으로, 웨어하우스 내의 정보는 지역 데이타베이스에 저장된 정보에 대한 실체화된 뷰로서 간주하며 지역 데이타의 변경에 따라 일관성을 유지하도록 반영해야 한다. 본 논문에서는 일관성을 유지하기 위해 정보 공유가 가능한 데이타 웨어하우스 시스템의 구조와 비-보상 실체 뷰 관리 기법을 제안한다. 본 논문에서 제안한 데이타 웨어하우스 시스템의 구조는 지역 데이타베이스에서 추출된 정보를 관리하는 별도의 지역 정보 관리자를 두어 뷰 관리자들 간의 정보 공유가 가능하게 한다. 비-보상 실체 뷰 관리 기법은 지역 데이타 변경 사건에 따른 뷰 관리 시 다른 사건에 의해 영향을 받지 않도록 하기 때문에 기본의 사전 보상이나 나중 보상 기법과는 달리 추가적인 질의 처리를 요구하지 않는 기법이다.Abstract A decision support system(DSS) commonly requires fast access to tremendous volume of information. A data warehouse is a database storing the information that is extracted, filtered and integrated from several relevant local databases to reply upon aggregated queries. The information stored in the data warehouse can be regarded as materialized views. The materialized view has to be modified according to the change of the corresponding local databases to preserve the data consistency. In this paper, we propose a data warehousing system architecture allowing information sharing (DAWINS), and a non-compensating materialized view maintenance algorithm(NCA). DAWINS architecture allows relevant information to be shared by individual view managers with local data manager for each local database. Unlikely to the pre- or post-compensating algorithms, which are required to remove the effects of some events to other view in the process of view maintenance, NCA does not require any additional query processing, since a local data manager in DAWINS already maintains the effects of update events occurring in local systems.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.268-275
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• 2014

In future, hospitals are expected to automatically issue remote transcriptions. Many general hospitals are planning to encrypt their medical database to secure personal information as mandated by law. The electronic medical record system, picture archiving communication system, and the clinical data warehouse, amongst others, are the preferred targets for which stronger security is planned. In the near future, medical systems can be assumed to be automated and connected to remote locations, such as rural areas, and islands. Connecting patients who are in remote locations to medical complexes that are usually based in larger cities requires not only automatic processing, but also a certain amount of security in terms of medical data that is of a sensitive and critical nature. Unauthorized access to patients' transcription data could result in the data being modified, with possible lethal results. Hence, personal and sensitive data on telemedicine and medical information systems should be encrypted to protect patients from these risks. Login passwords, personal identification information, and biological information should similarly be protected in a systematic way. This paper proposes the use of electronic acupuncture with a built-in multi-pad, which has the advantage of being able to establish a patient's physical condition, while simultaneously treating the patient with acupuncture. This system implements a sensing pad, amplifier, a small signal drive circuit, and a digital signal processing system, while the use of a built-in fuzzy technique and a control algorithm have been proposed for performing analyses.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.2
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• pp.59-67
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• 2010

Rice varietal information exchange is vital for agricultural experiments and trials. With the growing size of rice data gathered around the world, and numerous research and development achievements, the effective collection and convenient ways of data dissemination is an important aspect to be dealt with. The collection of this data is continuously worked out through various international cooperation and network programs. The problem in acquiring this information anytime anywhere is the new challenge faced by rice breeders, scientist and crop information specialists, in order to perform rapid analysis and obtain significant results in rice research, thus alleviating rice production. To address these constraints, we propose an Online Analytical Query System, a web query application to provide breeders and rice scientist around the world a fast web search engine for rice varieties, giving the users the freedom to choose from which trial it has been used, trait observation parameters as well as geographical or weather conditions, and location specifications. The application uses data warehouse techniques and OLAP for summarization of agricultural trials conducted, and statistical analysis in deriving outstanding varieties used in these trials, consolidated in an Model-View-Controller Web framework.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1386-1396
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• 2009

Most decision support functions of spatial data warehouse rely on the OLAP operations upon a spatial cube. Meanwhile, higher performance is always guaranteed by indexing the cube, which stores huge amount of pre-aggregated information. Hierarchical Dwarf was proposed as a solution, which can be taken as an extension of the Dwarf, a compressed index for cube structures. However, it does not consider the spatial dimension and even aggregates incorrectly if there are redundant values at the lower levels. OLAP-favored Searching was proposed as a spatial hierarchy based OLAP operation, which employs the advantages of R-tree. Although it supports aggregating functions well against specified areas, it ignores the operations on the spatial dimensions. In this paper, an indexing approach, which aims at utilizing the concept hierarchy of the spatial cube for decision support, is proposed. The index consists of concept hierarchy trees of all dimensions, which are linked according to the tuples stored in the fact table. It saves storage cost by preventing identical trees from being created redundantly. Also, it reduces the OLAP operation cost by integrating the spatial and aspatial dimensions in the virtual concept hierarchy.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.29-38
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• 2011

Unlike in traditional row-oriented database systems, a column-oriented database system stores data in column-oriented and not row-oriented order. Recently, research results revealed the effectiveness of column-oriented databases for applications such as data warehouse and decision support systems that access large volumes of data in a read only manner. In this paper, we investigate the join strategies for column-oriented databases and prove the effectiveness of column-oriented databases in data warehouse systems. For unbiased comparison, the two database systems are analyzed using the star schema benchmark and the performance analysis of a star schema join query is carried out. We experimented with well-known join algorithms and considered early materialization and late materialization join strategies for column-oriented databases. The performance results confirm that star schema join queries perform better in terms of disk I/O cost in column-oriented databases than in row-oriented databases. In addition, the late materialization strategy showed more performance gain than the early materialization strategy in column-oriented databases.