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Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data最新文献

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Improved Locally Linear Embedding for Big-data Classification 面向大数据分类的改进局部线性嵌入
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-11-07 DOI: 10.1145/3150919.3150925
Andres Ramirez, M. Rahnemoonfar
{"title":"Improved Locally Linear Embedding for Big-data Classification","authors":"Andres Ramirez, M. Rahnemoonfar","doi":"10.1145/3150919.3150925","DOIUrl":"https://doi.org/10.1145/3150919.3150925","url":null,"abstract":"A hyperspectral image provides a multidimensional data consisting of hundreds of spectral dimensions. Even though having an abundance of spectral might seem favorable, classification of hyperspectral data tends to collide with the curse of dimensionality. Therefore, reducing the number of dimensions before classification is always favorable. For this research, the feature extraction method will consist of a nonlinear manifold learning technique named locally linear embedding (LLE). Additionally, another problem that we attempt to overcome is the high computational time required to run manifold learning methods. In order to help overcome this problem, this research compares one implementation of LLE against an improved version that runs much quicker than the original version.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Parallel intersection detection in massive sets of cubes 平行交集检测在大量集的立方体
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-11-07 DOI: 10.1145/3150919.3150921
W. Randolph Franklin, S. V. G. Magalhães
{"title":"Parallel intersection detection in massive sets of cubes","authors":"W. Randolph Franklin, S. V. G. Magalhães","doi":"10.1145/3150919.3150921","DOIUrl":"https://doi.org/10.1145/3150919.3150921","url":null,"abstract":"We present ParCube, which finds the pairwise intersections in a set of millions of congruent cubes. This operation is required when computing boolean combinations of meshes or polyhedra in CAD/CAM and additive manufacturing, and in determining close points in a 3D set. ParCube is very compact because it is uses a uniform grid with a functional programming API. ParCube is very fast; even single threaded it usually beats CGAL's elapsed time, sometimes by a factor of 3. Also because it is FP, ParCube parallelizes very well. On an Nvidia GPU, processing 10M cubes to find 6M intersections, it took 0.33 elapsed seconds, beating CGAL by a factor of 131. ParCube is independent of the specific parallel architecture, whether shared memory multicore Intel Xeon using either OpenMP or TBB, or Nvidia GPUs with thousands of cores. We expect the principles used in ParCube to apply to other computational geometry problems. Efficiently finding all bipartite intersections would be an easy extension.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122024183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Bulk-Loading an Index for Temporally Overlaying Spatio-Textual Trajectories 批量加载时间覆盖空间文本轨迹的索引
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-11-07 DOI: 10.1145/3150919.3150922
Michael Beckemeyer, J. Vahrenhold
{"title":"Bulk-Loading an Index for Temporally Overlaying Spatio-Textual Trajectories","authors":"Michael Beckemeyer, J. Vahrenhold","doi":"10.1145/3150919.3150922","DOIUrl":"https://doi.org/10.1145/3150919.3150922","url":null,"abstract":"The IR-With-Identifiers (IRWI) tree, proposed by Issa and Damiani, indexes temporally overlaying spatio-textual trajectories and allows for efficiently answering sequenced spatio-textual range queries. We investigate the efficiency of constructing such a tree and show that its hybrid nature, combining spatial and textual information, poses challenges to the design of an efficient bulk-loading algorithm. We show that a variant of the Sort-Tile-Recursive algorithm and, to a lesser extent, an adaptation of the generic QuickLoad algorithm, both along with a bulk-loading scheme for the inverted indexes of the IRWI-tree significantly improve the construction time while maintaining or even improving query performance.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114158804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimized Discrete Fréchet Distance between trajectories 优化离散帧轨迹之间的距离
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-11-07 DOI: 10.1145/3150919.3150924
T. Devogele, Laurent Étienne, Maxence Esnault, Florian Lardy
{"title":"Optimized Discrete Fréchet Distance between trajectories","authors":"T. Devogele, Laurent Étienne, Maxence Esnault, Florian Lardy","doi":"10.1145/3150919.3150924","DOIUrl":"https://doi.org/10.1145/3150919.3150924","url":null,"abstract":"Distance computation between polylines or trajectories is a key point to assess similarity between geometrical objects. This paper describes a new optimized algorithm to compute discrete Fréchet distance which aims to lower computation time and improve precision. This algorithm is applied to GPS trajectories. It includes a filtering, pruning and an enhancement process. Thanks to this algorithm, big data trajectory repositories can be mined. This process is validated on a large trajectory dataset.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127126594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Geospatial Big Data Analytics Engine for Spark 地理空间大数据分析引擎的Spark
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-11-07 DOI: 10.1145/3150919.3150923
Shaohua Wang, Y. Zhong, Hao Lu, E. Wang, W. Yun, W. Cai
{"title":"Geospatial Big Data Analytics Engine for Spark","authors":"Shaohua Wang, Y. Zhong, Hao Lu, E. Wang, W. Yun, W. Cai","doi":"10.1145/3150919.3150923","DOIUrl":"https://doi.org/10.1145/3150919.3150923","url":null,"abstract":"With the rapid development of geospatial data acquisition and processing technology, the scale of spatial data is expanding. Mass production applications put forward higher requirements for the performance of geospatial data analysis. In this study, we developed a geospatial big data analytics engine based on SuperMap iObject for Java and Apache Spark. The geospatial big data analytics engine can increase the RDD representation ability of spatial data. The spatial indexing can make the spatial calculation on the nodes of the Spark cluster distributed and efficient. The experimental results show that compared with the traditional algorithm, the geospatial big data analytics engine for Spark has better execution efficiency.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Efficient Detection of Points of Interest from Georeferenced Visual Content 从地理参考视觉内容中有效检测兴趣点
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 2017-06-02 DOI: 10.1145/3150919.3150920
Ying Lu, Juan A. Colmenares
{"title":"Efficient Detection of Points of Interest from Georeferenced Visual Content","authors":"Ying Lu, Juan A. Colmenares","doi":"10.1145/3150919.3150920","DOIUrl":"https://doi.org/10.1145/3150919.3150920","url":null,"abstract":"Many people take photos and videos with smartphones and more recently with 360° cameras at popular places and events, and share them in social media. Such visual content is produced in large volumes in urban areas, and it is a source of information that online users could exploit to learn what has got the interest of the general public on the streets of the cities where they live or plan to visit. A key step to providing users with that information is to identify the most popular k spots in specified areas. In this paper, we propose a clustering and incremental sampling (C&IS) approach that trades off accuracy of top-k results for detection speed. It uses clustering to determine areas with high density of visual content, and incremental sampling, controlled by stopping criteria, to limit the amount of computational work. It leverages spatial metadata, which represent the scenes in the visual content, to rapidly detect the hotspots, and uses a recently proposed Gaussian probability model to describe the capture intention distribution in the query area. We evaluate the approach with metadata, derived from a non-synthetic, user-generated dataset, for regular mobile and 360° visual content. Our results show that the C&IS approach offers 2.8x-19x reductions in processing time over an optimized baseline, while in most cases correctly identifying 4 out of 5 top locations.","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123069663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data 第六届ACM SIGSPATIAL大地理空间数据分析研讨会论文集
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data Pub Date : 1900-01-01 DOI: 10.1145/3150919
{"title":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","authors":"","doi":"10.1145/3150919","DOIUrl":"https://doi.org/10.1145/3150919","url":null,"abstract":"","PeriodicalId":225242,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117057960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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