{"title":"MEMOCODE 2015设计竞赛:连续的天际线计算","authors":"Peter Milder","doi":"10.1109/MEMCOD.2015.7340467","DOIUrl":null,"url":null,"abstract":"The skyline query operation (also called the “maximum vector problem”) is used to identify potentially interesting or useful data points in large sets of multi-dimensional data. When the data change over time (through addition and subtraction of points), this is called the “continuous skyline” query. The 2015 MEMOCODE Design Contest problem is to implement a system to efficiently compute the continuous skyline of dynamic data. Contestants were given one month to develop a system to perform the skyline query, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The two winning teams, which have been invited to contribute papers describing their techniques, combined careful algorithmic and implementation optimizations; both implemented the system on multicore CPUs.","PeriodicalId":106851,"journal":{"name":"2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"MEMOCODE 2015 design contest: Continuous skyline computation\",\"authors\":\"Peter Milder\",\"doi\":\"10.1109/MEMCOD.2015.7340467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The skyline query operation (also called the “maximum vector problem”) is used to identify potentially interesting or useful data points in large sets of multi-dimensional data. When the data change over time (through addition and subtraction of points), this is called the “continuous skyline” query. The 2015 MEMOCODE Design Contest problem is to implement a system to efficiently compute the continuous skyline of dynamic data. Contestants were given one month to develop a system to perform the skyline query, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The two winning teams, which have been invited to contribute papers describing their techniques, combined careful algorithmic and implementation optimizations; both implemented the system on multicore CPUs.\",\"PeriodicalId\":106851,\"journal\":{\"name\":\"2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MEMCOD.2015.7340467\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEMCOD.2015.7340467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The skyline query operation (also called the “maximum vector problem”) is used to identify potentially interesting or useful data points in large sets of multi-dimensional data. When the data change over time (through addition and subtraction of points), this is called the “continuous skyline” query. The 2015 MEMOCODE Design Contest problem is to implement a system to efficiently compute the continuous skyline of dynamic data. Contestants were given one month to develop a system to perform the skyline query, aiming to maximize performance or cost-adjusted performance. Teams were encouraged to consider a variety of computational targets including CPUs, FPGAs, and GPGPUs. The two winning teams, which have been invited to contribute papers describing their techniques, combined careful algorithmic and implementation optimizations; both implemented the system on multicore CPUs.