The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nos últimos anos, um mesa horizontal com um grande número de atributos é amplamente utilizado em aplicações OLAP ou de e-business para analisar dados multidimensionais de forma eficiente. Para armazenamento e consulta eficientes de tabelas horizontais, trabalhos recentes tentaram transformar uma tabela horizontal em uma tabela tradicional. mesa vertical. Os trabalhos existentes, no entanto, têm a desvantagem de não considerarem uma otimização PIVÔ operação fornecida (ou a ser fornecida) em RDBMSs comerciais recentes. Neste artigo propomos uma abordagem formal que explora a operação PIVOT otimizada de RDBMSs comerciais para armazenamento e consulta de tabelas horizontais. Para atingir esse objetivo, primeiro fornecemos uma estrutura geral que armazena e consulta uma tabela horizontal usando uma tabela vertical equivalente. Na estrutura proposta, definimos formalmente 1) um método que armazena uma tabela horizontal em uma tabela vertical equivalente e 2) uma operação PIVOT que converte uma tabela vertical armazenada em uma visualização horizontal equivalente. A seguir, propomos um novo método que transforma uma consulta especificada pelo usuário em tabelas horizontais em uma consulta equivalente. PIVOT incluído consulta em tabelas verticais. Em particular, ao fornecer regras de transformação para todas as cinco operações elementares da álgebra relacional como teoremas, provamos que nosso método é teoricamente aplicável a RDBMSs comerciais. Os resultados experimentais mostram que, em comparação com o trabalho anterior, o nosso método reduz significativamente o espaço de armazenamento e também melhora o desempenho médio em várias ordens de grandeza. Estes resultados indicam que nosso método fornece uma excelente estrutura para maximizar o desempenho no tratamento de tabelas horizontais, explorando a operação PIVOT otimizada em RDBMSs comerciais.
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Sung-Hyun SHIN, Yang-Sae MOON, Jinho KIM, Sang-Wook KIM, "Efficient Storage and Querying of Horizontal Tables Using a PIVOT Operation in Commercial Relational DBMSs" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 6, pp. 1719-1729, June 2008, doi: 10.1093/ietisy/e91-d.6.1719.
Abstract: In recent years, a horizontal table with a large number of attributes is widely used in OLAP or e-business applications to analyze multidimensional data efficiently. For efficient storing and querying of horizontal tables, recent works have tried to transform a horizontal table to a traditional vertical table. Existing works, however, have the drawback of not considering an optimized PIVOT operation provided (or to be provided) in recent commercial RDBMSs. In this paper we propose a formal approach that exploits the optimized PIVOT operation of commercial RDBMSs for storing and querying of horizontal tables. To achieve this goal, we first provide an overall framework that stores and queries a horizontal table using an equivalent vertical table. Under the proposed framework, we then formally define 1) a method that stores a horizontal table in an equivalent vertical table and 2) a PIVOT operation that converts a stored vertical table to an equivalent horizontal view. Next, we propose a novel method that transforms a user-specified query on horizontal tables to an equivalent PIVOT-included query on vertical tables. In particular, by providing transformation rules for all five elementary operations in relational algebra as theorems, we prove our method is theoretically applicable to commercial RDBMSs. Experimental results show that, compared with the earlier work, our method reduces storage space significantly and also improves average performance by several orders of magnitude. These results indicate that our method provides an excellent framework to maximize performance in handling horizontal tables by exploiting the optimized PIVOT operation in commercial RDBMSs.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.6.1719/_p
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@ARTICLE{e91-d_6_1719,
author={Sung-Hyun SHIN, Yang-Sae MOON, Jinho KIM, Sang-Wook KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Storage and Querying of Horizontal Tables Using a PIVOT Operation in Commercial Relational DBMSs},
year={2008},
volume={E91-D},
number={6},
pages={1719-1729},
abstract={In recent years, a horizontal table with a large number of attributes is widely used in OLAP or e-business applications to analyze multidimensional data efficiently. For efficient storing and querying of horizontal tables, recent works have tried to transform a horizontal table to a traditional vertical table. Existing works, however, have the drawback of not considering an optimized PIVOT operation provided (or to be provided) in recent commercial RDBMSs. In this paper we propose a formal approach that exploits the optimized PIVOT operation of commercial RDBMSs for storing and querying of horizontal tables. To achieve this goal, we first provide an overall framework that stores and queries a horizontal table using an equivalent vertical table. Under the proposed framework, we then formally define 1) a method that stores a horizontal table in an equivalent vertical table and 2) a PIVOT operation that converts a stored vertical table to an equivalent horizontal view. Next, we propose a novel method that transforms a user-specified query on horizontal tables to an equivalent PIVOT-included query on vertical tables. In particular, by providing transformation rules for all five elementary operations in relational algebra as theorems, we prove our method is theoretically applicable to commercial RDBMSs. Experimental results show that, compared with the earlier work, our method reduces storage space significantly and also improves average performance by several orders of magnitude. These results indicate that our method provides an excellent framework to maximize performance in handling horizontal tables by exploiting the optimized PIVOT operation in commercial RDBMSs.},
keywords={},
doi={10.1093/ietisy/e91-d.6.1719},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Efficient Storage and Querying of Horizontal Tables Using a PIVOT Operation in Commercial Relational DBMSs
T2 - IEICE TRANSACTIONS on Information
SP - 1719
EP - 1729
AU - Sung-Hyun SHIN
AU - Yang-Sae MOON
AU - Jinho KIM
AU - Sang-Wook KIM
PY - 2008
DO - 10.1093/ietisy/e91-d.6.1719
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2008
AB - In recent years, a horizontal table with a large number of attributes is widely used in OLAP or e-business applications to analyze multidimensional data efficiently. For efficient storing and querying of horizontal tables, recent works have tried to transform a horizontal table to a traditional vertical table. Existing works, however, have the drawback of not considering an optimized PIVOT operation provided (or to be provided) in recent commercial RDBMSs. In this paper we propose a formal approach that exploits the optimized PIVOT operation of commercial RDBMSs for storing and querying of horizontal tables. To achieve this goal, we first provide an overall framework that stores and queries a horizontal table using an equivalent vertical table. Under the proposed framework, we then formally define 1) a method that stores a horizontal table in an equivalent vertical table and 2) a PIVOT operation that converts a stored vertical table to an equivalent horizontal view. Next, we propose a novel method that transforms a user-specified query on horizontal tables to an equivalent PIVOT-included query on vertical tables. In particular, by providing transformation rules for all five elementary operations in relational algebra as theorems, we prove our method is theoretically applicable to commercial RDBMSs. Experimental results show that, compared with the earlier work, our method reduces storage space significantly and also improves average performance by several orders of magnitude. These results indicate that our method provides an excellent framework to maximize performance in handling horizontal tables by exploiting the optimized PIVOT operation in commercial RDBMSs.
ER -