{"title":"Scalable Overlay Operations over DCEL Polygon Layers","authors":"Andres Calderon-Romero, V. Tsotras, A. Magdy","doi":"10.1145/3609956.3609964","DOIUrl":null,"url":null,"abstract":"The Doubly Connected Edge List (DCEL) is an edge-list structure that has been widely utilized in spatial applications for planar topological computations. An important operation is the overlay which combines the DCELs of two input layers and can easily support spatial queries like the intersection, union and difference between these layers. However, existing sequential implementations for computing the overlay do not scale and fail to complete for large datasets (for example the US census tracks). In this paper we propose a distributed and scalable way to compute the overlay operation and its related supported queries. We address the issues involved in efficiently distributing the overlay operator and offer various optimizations that improve performance. Our scalable solution can compute the overlay of very large real datasets (32M edges) in few minutes.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3609956.3609964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Doubly Connected Edge List (DCEL) is an edge-list structure that has been widely utilized in spatial applications for planar topological computations. An important operation is the overlay which combines the DCELs of two input layers and can easily support spatial queries like the intersection, union and difference between these layers. However, existing sequential implementations for computing the overlay do not scale and fail to complete for large datasets (for example the US census tracks). In this paper we propose a distributed and scalable way to compute the overlay operation and its related supported queries. We address the issues involved in efficiently distributing the overlay operator and offer various optimizations that improve performance. Our scalable solution can compute the overlay of very large real datasets (32M edges) in few minutes.