• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.797-806
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• 2006

In the data warehouse environment, materialized views are typically used to support efficient query processing. Materialized views need to be updated when source data change. Since the update of the views need impose a significant overhead, it is essential to update the views efficiently. Though various view maintenance strategies have been discussed in the past, the efficient maintenance of SPJ materialized views has not been sufficiently investigated. In this paper, we propose an efficient incremental view maintenance method for SPJ materialized views that minimizes the total accesses to data sources. The proposed method finds an optimal view maintenance strategy using a dynamic programming algorithm. We also present various experimental results that shows the efficiency of our proposed method.

• Journal of Information Processing Systems
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• v.2 no.2
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• pp.67-75
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• 2006

In order to acquire a precise and quick response to an analytical query, proper selection of the views to materialize in the data warehouse is crucial. In traditional view selection algorithms, all relations are considered for selection as materialized views. However, materializing all relations rather than a part results in much worse performance in terms of time and space costs. Therefore, we present an improved algorithm for selection of views to materialize using the clustering method to overcome the problem resulting from conventional view selection algorithms. In the presented algorithm, ASVMRT (Algorithm for Selection of Views to Materialize using Reduced Table), we first generate reduced tables in the data warehouse using clustering based on attribute-values density, and then we consider the combination of reduced tables as materialized views instead of a combination of the original base relations. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs are approximately 1.8 times better than conventional algorithms.

• Journal of KIISE:Databases
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• v.27 no.1
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• pp.113-128
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• 2000

The adaptation of spatial view is to update materialized view objects when apatial view is redefined. There are tow kinds of adaptation : incremental updates and recomputation. The incremental update changes related view objects and it is more efficient than the recomputation which evaluates redefined view defining query because spatial view is defined by spatial query including high cost spatial operator. This paper proposes the several incremental update methods according to the types of changing the definition of a spatial view. There are two kinds of incremental view adaptation : the method of using only the existing view objects and the view derivation relationship between view objects and their sources. This incremental update is achieved by updating the current materialized view objects or by inserting new materialized view objects. Spatial view adapter is implemented and tested on top of the object oriented geographic information system. This paper evaluates performance between the recomputation and incremental update method through real data.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.318-320
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• 1998

CORBA/JAVA 기반의 멀티데이터 소스에 대한 통합 질의 환경에서 많은 사용자가 질의를 동시에 수행함으로써, 질의 처리 성능 문제가 나타난다. 이러한 문제점을 해결하기 위해서 본 논문에서는 메타데이터를 이용하여 분할된 질의에 대한 실체 뷰(Materialized view)를 관리함으로써, 질의 처리를 향상시키는 기법을 제안한다. 제안된 기법은 자주 사용되는 실체 뷰를 캐쉬에 유지시킴으로써, 재사용율을 높이고, 질의 절차를 감소시켜, 중복되는 질의의 반복 수행을 줄인다.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.561-570
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• 2002

In order to evaluate the performance of incremental update algorithms, we perform experimental tests on the time of updating view objects. In this paper, the incremental update algorithms are evaluated on two kinds of materialized methods : materialization by value-copy and materialization by preserving object identifiers (OIDs). The result of performance evaluation shows that there is little difference in the updating time of view objects between two materialization methods. The evaluation of query processing on spatial views shows that materialization by value-copy is much better than materialization by preserving OIDs. As the results of overall performance evaluation, it is more desirable to use the incremental update method based on materialization by value-copy than the incremental update method based on materialization by preserving OIDs.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.377-386
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• 2001

In spatial database system, spatial view is supported for efficient access method to spatial database and is managed by materialization and non-materialization technique. In non-materialization technique, repeated execution on the same query makes problems such as the bottle-neck effect of server-side and overloads on a network. In materialization technique, view maintenance technique is very difficult and maintenance cost is too high when the base table has been changed. In this paper, the SVMT (Spatial View Materialization Technique) is proposed by using R-tree re-construction. The SVMT is a technique which constructs a spatial index according to the distribution ratio of objects in spatial view. This ratio is computed by using a SVHR (Spatial View Height in R-tree) and SVOC (Spatial View Object Count). If the ratio is higher than the average, a spatial view is materialized and the R-tree index is re-used. In this case, the root node of this index is exchanged a node which has a MBR (Minimum Boundary Rectangle) value that can contains the whole region of spatial view at a minimum size. Otherwise, a spatial view is materialized and the R-tree is re-constructed. In this technique, the information of spatial view is managed by using a SVIT (Spatial View Information Table) and is stored on the record of this table. The proposed technique increases the speed of response time through fast query processing on a materialized view and eliminates additional costs occurred from repeatable query modification on the same query. With these advantages, it can greatly minimize the network overloads and the bottle-neck effect on the server.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.175-184
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• 2000

A data warehouse is an integrated and summarized collection of data that can efficiently support decision making process. The summarized data at the data warehouse is often stored in materialized views. These materialized views need to be updated when source data change. Since the propagation of updates to the views may impose a significant overhead, it is very important to update the warehouse views efficiently. Though various strategies have been proposed to maintain views in the past, they typically require too much accesses to the data sources when the changes of multiple data sources have to be reflected in the view. In this paper we propose an efficient view update strategy that uses relatively small number of accesses to the data sources. We also show the performance advantage of our method over other existing methods through experiments using TPC-D data and queries.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2273-2286
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• 2000

In order to acquire the precise and fast response for an analytical query, proper selection of the views to materialize in data warehouse is very crucial. In traditional view selection algorithms, the whole relations are considered to be selected as materialized views. However, materializing the whole relations rather than a part of relations results in much worse performance in terms of time and space cost. Therefore, we present an improved algorithm for selection of views to materialize using clustering method to overcome the problem resulted from conventional view selection algorithms. In the presented algorithm, ASVMRT(Algorithm for Selection of Views to daterialize using Iteduced Table). we first generate reduced tables in clata warehouse using automatic clustering based on attrihute-values density, then we consider the combination of reduced tables as materialized views instead of the combination of the original hase relations. For the justification of the proposecl algorithm. we show the experimental results in which both time and space cost are approximately 1.8 times better than the conventional algorithms.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.1-9
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• 1998

Temporal Databases store all of informations by time varying, so the temporal query processor has to process very large information. Therefore, we propose an efficient method of query processing by using the relevance checking algorithm of input query and view definition. The relevance checking algorithm of query investigates relevance between the input query of user about base relation and the execution tree of view definition stored in system catalog. And related input query with view definition have a process of the query translation to the execution tree of view. So temporal query processor is able to increase performance of query processor by reducing the number of tuple.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.97-100
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• 2015

Analysis of construction tender documents in overseas projects is a very important issue from a risk management point of view. Unfortunately, majority of construction firms are biased by winning contracts without in-depth analysis of tender documents. As a result, many contractors have incurred loss in overseas projects. Although a lot of risk analysis techniques have been introduced, most of them focus project's external unexpected risks such as country conditions and owner's financial standing. However, because those external risks are difficult to control and take preemptive action, we need to concentrate on project inherent risks. Based on this premise, this paper proposes a project-oriented risk mining approach which could detect and extract project risk factors automatically before they are materialized and assess them. This study presents a methodology regarding how to extract potential risks which exist in owner's project requirements and project tender documents using state of the art data analysis method such as text mining, data mining, and information visualization. The project-oriented risk mining approach is expected to effectively reflect project characteristics to the project risk management and could provide construction firms with valuable business intelligence.