{"title":"Breaking skyline computation down to the metal: the skyline breaker algorithm","authors":"D. Köppl","doi":"10.1145/2513591.2513637","DOIUrl":null,"url":null,"abstract":"Given a sequential input connection, we tackle parallel skyline computation of the read data by means of a spatial tree structure for indexing fine-grained feature vectors. For this purpose, multiple local split decision trees are simultaneously filled before the actual computation starts. We exploit the special tree structure to clip parts of the tree without depth-first search. The split of the data allows us to do this step in a divide and conquer manner. With this schedule we seek to provide an algorithm robust against the \"dimension curse\" and different data distributions.","PeriodicalId":93615,"journal":{"name":"Proceedings. International Database Engineering and Applications Symposium","volume":"5 1","pages":"132-141"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Database Engineering and Applications Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2513591.2513637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Given a sequential input connection, we tackle parallel skyline computation of the read data by means of a spatial tree structure for indexing fine-grained feature vectors. For this purpose, multiple local split decision trees are simultaneously filled before the actual computation starts. We exploit the special tree structure to clip parts of the tree without depth-first search. The split of the data allows us to do this step in a divide and conquer manner. With this schedule we seek to provide an algorithm robust against the "dimension curse" and different data distributions.