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identifier ( Ĭ LtD \ ( pt0X (|x FAnalysis of High Dimensional Data using Low Dimensional Summary Tables2003 Y 0YtTŐYP Y0 YMaster@Master's Thesis
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( pt0 ̬ \ l ŰX pt0X ɷt Xଐ XՔ pt0X 0 pt0X D ɹ0 t )t. t| t Ǩ @\̹ pt0| UŕX Ȁ DD tp LtD | \ ȥh<\ Ĭ ŰD `t ̬XՔ l . X̹ t\ )@ ( pt0X (|x XՔ pt0X D <\ tհXɔ \.
l ( pt0 L ( pt0| XT|ĳ \ ( Ĭ Ltt \ 䲔 )HX pt0X D t pt0| XՔ )HD \. t@ @ )X (1D | t X| ̬XՔ t P 1X D t X \ 1 LବD HX. HXՔ Lବ@ Ĭ ŰD ` L l0 )\ ( Ĭ LtX 1D X, 3~6( tX (X Ĭ Lt̹D <\ ٳ 1XՔ )t. t Lବ@ T| (<\ \֩X ( pt0X ǴX MOLAPX \ĬD X ( pt0X \ t \XՌ t踴ĳ] X. lX (1D t`x ( Ĭ Lt D !0| h<\ H \ LବX DD !` ǌ X.;Business data with many dimensions(or attribute) is used for planning company strategy or analyzing customer patterns in various views. The online analysis of high dimensional data incurs serious problems differently from processing of low dimensional data. For providing users with results of data analysis quickly, OLAP systems pre-compute such results called summary tables. In case of high dimensional data, it is impossible to pre-compute whole summary tables because of the vast quantity and size of summary tables.
Previous approach of dealing with high dimensional data is to reduce data explosion that the cube size of pre-computed aggregation is larger than the size of analysis data. In order to reduce data explosion, large data is compressed into valid cells for reducing storage cost, it improve scanning time to storing fact table as column unit. These methods are not fundamental solutions for data explosion.
In this paper, we propose a new analysis method for reducing data explosion in high dimensional data. It is focus a fact that analysts tends to be interested in querying low dimensional summary results such as 3~6. We analyze effectiveness of using low dimensional data in query processing with real examples and in reducing cost of cube generation. Also, we propose a generation algorithm. The proposed algorithm generates low dimensional summary tables instead of whole summary tables from a fact table simultaneously. This algorithm improves coverage of MOLAP with high dimensionality by reusing memory efficiently and analyzes high dimensional data quickly. We show the efficiency of the new algorithm through theoretical analysis and implement an application for estimating the cost of cube generation.~http://dspace.ewha.ac.kr/handle/2015.oak/202793;
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