Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document : 2 / 3
ÇѱÛÁ¦¸ñ(Korean Title) |
ºÐ»ê ÀÎ ¸Þ¸ð¸® DBMS ±â¹Ý º´·Ä K-MeansÀÇ In-database ºÐ¼® ÇÔ¼ö·ÎÀÇ ¼³°è¿Í ±¸Çö |
¿µ¹®Á¦¸ñ(English Title) |
Design and Implementation of Distributed In-Memory DBMS-based Parallel K-Means as In-database Analytics Function |
ÀúÀÚ(Author) |
±¸Çظð
³²Ã¢¹Î
ÀÌ¿ìÇö
ÀÌ¿ëÀç
±èÇüÁÖ
Heymo Kou
Changmin Nam
Woohyun Lee
Yongjae Lee
HyoungJoo Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 24 NO. 03 PP. 0105 ~ 0112 (2018. 03) |
Çѱ۳»¿ë (Korean Abstract) |
µ¥ÀÌÅÍÀÇ ¾çÀÌ Áõ°¡ÇÏ¸é¼ ´ÜÀÏ ³ëµå µ¥ÀÌÅͺ£À̽º·Î´Â ÀúÀå°ú 󸮸¦ µ¿½Ã¿¡ ¼öÇàÇϱ⿡´Â ºÎÁ·ÇÏ´Ù. µû¶ó¼, µ¥ÀÌÅ͸¦ ºÐ»ê½ÃÄÑ º¹¼ö ³ëµå·Î ±¸¼ºµÈ ºÐ»ê µ¥ÀÌÅͺ£À̽º¿¡ ÀúÀåµÇ°í ÀÖÀ¸¸ç ºÐ¼® ¿ª½Ã È¿À²¼ºÀ» À§ÇØ º´·Ä ±â´ÉÀ» Á¦°øÇؾßÇÑ´Ù. ÀüÅëÀûÀÎ ºÐ¼® ¹æ½ÄÀº µ¥ÀÌÅͺ£À̽º¿¡¼ ºÐ¼® ³ëµå·Î µ¥ÀÌÅ͸¦ À̵¿½ÃŲ ÈÄ ºÐ¼®À» ¼öÇàÇϱ⠶§¹®¿¡ ³×Æ®¿öÅ©ÀÇ ºñ¿ëÀÌ ¹ß»ýÇÏ¸ç »ç¿ëÀÚ°¡ ºÐ¼®À» À§ÇØ ºÐ¼® ÇÁ·¹ÀÓ¿öÅ©µµ ´Ù¸¦ ¼ö ÀÖ¾î¾ßÇÑ´Ù. º» ¿¬±¸´Â ±ºÁýÈ ºÐ¼® ±â¹ýÀÎ K-Means ±ºÁýÈ ¾Ë°í¸®ÁòÀ» °ü°èÇü µ¥ÀÌÅͺ£À̽º¿Í Ä®·³ ±â¹Ý µ¥ÀÌÅͺ£À̽º¸¦ ÀÌ¿ëÇÑ ºÐ»ê µ¥ÀÌÅͺ£À̽º ȯ°æ¿¡¼ SQL·Î ±¸ÇöÇÏ´Â In-database ºÐ¼® ÇÔ¼ö·ÎÀÇ ¼³°è¿Í ±¸Çö ±×¸®°í °ü°èÇü µ¥ÀÌÅͺ£À̽º¿¡¼ÀÇ ¼º´É ÃÖÀûÈ ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
As data size increase, a single database is not enough to serve current volume of tasks. Since data is partitioned and stored into multiple databases, analysis should also support parallelism in order to increase efficiency. However, traditional analysis requires data to be transferred out of database into nodes where analytic service is performed and user is required to know both database and analytic framework. In this paper, we propose an efficient way to perform K-means clustering algorithm inside the distributed column-based database and relational database. We also suggest an efficient way to optimize K-means algorithm within relational database.
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Å°¿öµå(Keyword) |
In-database ºÐ¼®
K-Means ±ºÁýÈ
ºÐ»ê µ¥ÀÌÅͺ£À̽º
in-database analytics
K-means clustering
distributed database
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