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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) À̺¥Æ® ¼Ò½Ì°ú CQRS ÆÐÅÏÀ» È°¿ëÇÑ µ¥ÀÌÅÍ ÀçÇö ¹× ºÐ»êó¸® »ç·Ê ¹× ¿¬±¸ µ¿Çâ
¿µ¹®Á¦¸ñ(English Title) Case Studies and Trends in Data Reproduction and Distributed Processing using Event Sourcing and CQRS Pattern
ÀúÀÚ(Author) °­¼ºÁÖ   Á¤Ã¤Àº   Á¤±¤¼ö   Seongju Kang   Chaeeun Jeong   Kwangsue Chung   °íÀ±¹Î   ³ëÇö¹Î   ÃÖÁõ¿ø   ±è°æ¿ì   ±è±âÈÆ   ÀÌÁ¾¸¸   ¹Ú Çö   ¼ÛȲÁØ   Yunmin Go   Hyunmin Noh   Jeung Won Choi   Kyungwoo Kim   Kihun Kim   Jongman Lee   Hyun Park   Hwangjun Song   ÇÑ»ó°ï   ÃÖÁ¤ÀΠ  ¿ì ±Õ   Sangkon Han   Jung-in Choi   Gyun Woo  
¿ø¹®¼ö·Ïó(Citation) VOL 47 NO. 12 PP. 1101 ~ 1110 (2020. 12)
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(Korean Abstract)
V2X, ÀΰøÁö´É, ¹× ÀÚÀ²ÁÖÇà µî°ú °°Àº ºÐ¾ß¿¡¼­ ¼öÁýµÇ´Â Á¤º¸ÀÇ ¾çÀÌ ¸Å³â ±ÞÁõÇÏ°í ÀÖ´Ù. µ¥ÀÌÅÍÀÇ ¾çÀÌ ±ÞÁõÇÏ°í, Á¾·ù¿Í Çü½ÄÀÌ ´Ù¾çÇØÁ³±â ¶§¹®¿¡ À̱âÁ¾ µ¥ÀÌÅ͸¦ ¼öÁý ó¸® ÀúÀåÇÏ´Â ¹æ¹ýÀÌ Áß¿äÇØÁ³´Ù. ½Ã½ºÅÛ ¶Ç´Â ¾ÖÇø®ÄÉÀ̼ÇÀÇ »óÅ º¯°æ¿¡ ´ëÇÑ ¸ðµç »çÇ×À» ÀÏ·ÃÀÇ À̺¥Æ®·Î ÀúÀåÇÏ´Â ¹æ½ÄÀÎ À̺¥Æ® ¼Ò½ÌÀº ¸Þ½ÃÁö¸¦ ±â¹ÝÀ¸·Î ÇÑ À̺¥Æ® Áß½É(event-driven) ¼³°è°¡ °¡´ÉÇÏ°í, ½Ã½ºÅÛÀ̳ª ¾ÖÇø®ÄÉÀ̼ÇÀÇ »óÅ º¹¿ø°ú ÀçÇöÀÌ °¡´ÉÇÏ´Ù. Á¶È¸¿Í ¸í·ÉÀ» ºÐ¸®ÇÏ´Â ÆÐÅÏÀÎ CQRS(Command and Query Responsibility Segregation)¿Í À̺¥Æ® ¼Ò½ÌÀÇ Æ¯Â¡À» °áÇÕÇÏ¿© ´ë±Ô¸ð µ¥ÀÌÅÍ Ã³¸®¸¦ À§ÇÑ ºÐ»ê ½Ã½ºÅÛ ¾ÆÅ°ÅØó, À̺¥Æ® ¼Ò½ÌÀÇ »óÅ ÀçÇöÀ» È°¿ëÇÑ µ¥ÀÌÅÍ ºÐ¼®°ú µð¹ö±ëÀ» À§ÇÑ ½Ã½ºÅÛ°ú ¾ÖÇø®ÄÉÀÌ¼Ç µî¿¡ È°¿ëÇÒ ¼ö ÀÖ´Ù. À̺¥Æ® ¼Ò½ÌÀ» È°¿ëÇÑ µ¥ÀÌÅÍ ÀçÇö ¹× ºÐ»ê󸮿¡ °üÇÑ »ç·Ê ¹× ¿¬±¸ ³»¿ëÀ» ¼Ò°³ÇÑ´Ù.
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(English Abstract)
The amounts of information collected in areas such as V2X, artificial intelligence, and self-driving are increasing rapidly every year. It is important to collect, process, and store heterogeneous data. This is because the amounts of relevant data have increased rapidly and the types and formats of those data have diversified. Event sourcing, which is a way to store a whole set of events about changing the state of a system or an application through some set of events, enables an event-driven design based on messages, and also enables the restoration and reproduction of the state of a system or an application. Combined with CQRS (Command and Query Responsibility Segmentation), a pattern that separates queries and commands, the characteristics of event sourcing can be utilized for a distributed system architecture for large data processing, as well as systems and applications for data analysis and debugging using reproducibility of the state of event sourcing. This paper introduces the cases and research contents on data reproduction and dispersal processing using event sourcing.
Å°¿öµå(Keyword) ¸ÂÃãÇü ±¤°í ¼­ºñ½º   Ãßõ ½Ã½ºÅÛ   Æ®¸® ±¸Á¶   Ãßõ ¾Ë°í¸®Áò   personalized advertisement service   recommendation system   tree structure   recommendation algorithm   HTTP ÀûÀÀÀû ½ºÆ®¸®¹Ö   ¿¡³ÊÁö È¿À²   TCP/UDP   ·¦ÅÍ Äڵ堠 ÀÌÁ¾ ¹«¼± ³×Æ®¿öÅ©   HTTP adaptive streaming   energy efficiency   TCP/UDP   raptor code   heterogeneous wireless networks   À̺¥Æ® ¼Ò½Ì   CQRS   ºÐ»ê ½Ã½ºÅÛ   event sourcing   CQRS   logging   distributed system  
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