• Asia pacific journal of information systems
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• v.15 no.4
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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.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.155-164
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• 2002

This paper focuses on how modem data mining can be integrated with real-time relational databases and commercial data warehouses to improve reliability in real-time. An important Issue for many manufacturers is the development of relational databases that link key product attributes with real-time process parameters. Helpful data for key product attributes in manufacturing may be derived from destructive reliability testing. Destructive samples are taken at periodic time intervals during manufacturing, which might create a long time-gap between key product attributes and real-time process data. A case study is briefly summarized for the medium density fiberboard (MDF) industry. MDF is a wood composite that is used extensively by the home building and furniture manufacturing industries around the world. The cost of unacceptable MDF was as large as 5% to 10% of total manufacturing costs. Prevention can result In millions of US dollars saved by using better Information systems.

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.05a
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• pp.65-78
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• 2005

Nowadays, large amounts of XML documents are available on the Internet. Thus, we need to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new frame-work for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents.

• ETRI Journal
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• v.22 no.3
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• pp.41-51
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• 2000

An important use of data warehousing is to provide temporal views over the history of source data. It is significant that nearly all data warehouses are dependent on relational database technology, yet relational databases provide little or no real support for temporal data. Therefore, in is difficult to obtain accurate information for time-varying data. In this paper, we are going to design a temporal data warehouse to support time-varying data efficiently. For this purpose, we present a method to support temporal query by combining a temporal query process layer with the relational database which is used as a source database in an existing data warehouse. We introduce the Temporal Aggregate Tree Strategy (TATS), and suggest its algorithm for the way to aggregate the time-varying data that is changed by the time when the temporal view is created. In addition, The TATS and the materialized view creation method of the existing data warehouse have been evaluated. As a result, the TATS reduces the size of the fact table and it shows a good performance for the comparison factor in case of processing the query for time-varying data.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.585-595
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• 2004

A query processing is a critical issue in data warehouse environment since queries on data warehouses often involve hundreds of complex operations over large volumes of data. Data warehouses therefore build a large number of materialized views to increase the system performance. Which views to materialized is an important factor on the view maintenance cost as well as the query performance. The goal of materialized view selection problem is to select an optimal set of views that minimizes total query response time in addition to the view maintenance cost. In this paper, we present an efficient solution for the materialized view selection problem. Although the optimal selection of materialized views is NP-hard problem, we developed a feasible solution by utilizing the characteristics of relational operators such as join, selection, and grouping.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.1-14
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• 2012

As the size of the data warehouse is large, the selection of indices on the data warehouse affects the efficiency of the query processing of the data warehouse. Indices induce the lower query processing cost, but they occupy the large storage areas and induce the index maintenance cost which are accompanied by database updates. The bitmap join indices are well applied when we optimize the star join queries which join a fact table and many dimension tables and the selection on dimension tables in data warehouses. Though the bitmap join indices with the binary representations induce the lower storage cost, the task to select the indexing attributes among the huge candidate attributes which are generated is difficult. The processes of index selection are to reduce the number of candidate attributes to be indexed and then select the indexing attributes. In this paper on bitmap join index selection problem we reduce the number of candidate attributes by the data mining techniques. Compared to the existing techniques which reduce the number of candidate attributes by the frequencies of attributes we consider the frequencies of attributes and the size of dimension tables and the size of the tuples of the dimension tables and the page size of disk. We use the mining of the frequent itemsets as mining techniques and reduce the great number of candidate attributes. We make the bitmap join indices which have the least costs and the least storage area adapted to storage constraints by using the cost functions applied to the bitmap join indices of the candidate attributes. We compare the existing techniques and ours and analyze them in order to evaluate the efficiencies of ours.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.137-139
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• 2000

본 논문에서는 차세대 DBMS로 각광을 받고 있는 객체-관계형 DBMS(Object-Relational DBMS : ORDBMS)기반의 데이터 웨어하우스(data warehouse)에서 질의 처리의 성능을 향상시키는 AH(Attribute Hierarchy) 인덱스와 이를 이용한 질의 처리 기법을 제안한다. 지금까지 관계 DBMS를 이용한 데이터 웨어하우스의 성능 향상에 관한 연구는 거의 이루어지지 않고 있다. 데이터 웨어하우스는 기존의 데이터베이스와는 비교할 수 없을 만큼의 대용량 데이터를 가정하므로 ORDBMS를 이용하여 데이터 웨어하우스를 구축하는 경우에서도 적절한 성능의 보장이 필수적으로 요구된다. 이 논문에서 제안된 AH 인덱스를 사용함으로써 데이터 웨어하우스 분석용 질의에서 자주 사용되는 조인과 그루핑 연산은 비용이 저렴한 인덱스 액세스 연산으로 대치되며, 데이터의 량과 무관하게 질의 처리비용이 거의 고정되는 효과를 얻을 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.214-216
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• 2005

데이터 웨어하우스에서는 데이터를 다양한 관점으로 분석하기 위해 데이터를 다차원 형태로 유지한다. 이 다차원 데이터를 간단하고 편리한 형태로 사용자에게 표현하기 위해 피벗 테이블이 이용된다. 피벗 테이블은 데이터에 대한 요약된 정보를 제공하는데 널리 사용되는 편리한 표현 방법이지만, 실제 값이 열의 제목으로 나오기 때문에 많은 개수의 열을 가질 수 있다. 이러한 피벗 테이블을 그대로 저장할 경우 관계 DBMS의 테이블 컬럼 수에 제약을 받게 되며, 데이터 저장 및 질의 처리에 성능이 떨어질 수 있다. 이 논문은 관계 데이터베이스의 테이블을 이용하여 피벗 테이블을 효율적으로 저장하는 방법을 제안한다. 이때, 피벗 테이블에 대한 질의물 저장된 형태의 테이블에 적용 가능하도록 질의를 변환시켜야 한다. 따라서 이 연구에서는 피벗 테이블에 대한 관계 연산자들(실렉션, 프로젝션, 합집합, 차집합 카디션 곱)을 효율적으로 변환하는 질의 변환 방범을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.281-283
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• 1999

본 논문에서는 객체-관계형 데이터베이스 관리 시스템(Object-Relational DBMS: ORDBMS) 기반의 데이터 웨어하우스(Data Warehouse)에서 성능 향상을 위한 인덱싱 기법과 이를 이용한 질의 처리 기법을 제안한다. 지금까지 관계형 DBMS를 기반으로 한 데이터 웨어하우스의 성능향상에 관한 연구는 활발히 이루어져 왔으나, ORDBMS에 기반한 데이터 웨어하우스의 구축 및 질의 처리 성능에 관한 연구는 거의 이루어지지 않고 있다. 데이터 웨어하우스는 기존의 데이터베이스와는 비교할 수 없을 만큼의 대용량 데이터를 가정하므로 ORDBMS를 이용하여 데이터 웨어하우스를 구축하는 경우에도 적절한 성능의 보장이 필수적으로 요구된다. 제안된 인덱싱 기법을 사용함으로써 데이터 웨어하우스 분석용 질의에 포함된 비용이 큰 조인과 그루핑 연산은 비용이 저렴한 인덱스 액세스 연산으로 대치되며, 데이터의 량과 거의 무관하게 질의 처리 비용이 고정되는 효과를 얻을 수 있다.