Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第8页

Gloria

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2997013

Rachid Kachemir, Brad Kellett, Krishna Behara

{"title":"Gloria","authors":"Rachid Kachemir, Brad Kellett, Krishna Behara","doi":"10.1145/2996913.2997013","DOIUrl":"https://doi.org/10.1145/2996913.2997013","url":null,"abstract":"Indexing and delivering spatial data to a massive user base composed of over a billion devices around the world stretches the limits of traditional infrastructure and operational tools. For instance, offline bulk indexing and loading fall short of viable solutions when it comes to data at scale; Integration with distributed systems such as Apache Hadoop© or Spark© is sparse, while data loading is often performed in a sub-optimal fashion by relying on intermediate file formats. We present in this paper an approach toward a hybrid on- line/offline indexing framework called Gloria that has been running in production settings for the past year at over 350k requests per seconds with lookup latencies under 5μs. The resulting output is an in-memory key-value store and we show that by leveraging higher level MapReduce [7] constructs as defined in FlumeJava [5], Gloria can achieve large scale key-value offline indexing in a fraction of the time required by traditional datastores while maintaining similar operational performance. Gloria also provides a spatial layer based on improvements to pointer-less quadtrees [12] and locational identifiers we call shift key that reduces the nearest neighbor problem in spatial data to simple key-value lookups. Shift keys have shown to outperform well established solutions such as Google S2 with locational key operations.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81523982","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

A distantly supervised method for extracting spatio-temporal information from text 一种从文本中提取时空信息的远程监督方法

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996967

Seyed Iman Mirrezaei, Bruno Martins, I. Cruz

引用次数: 3

Automatic geographic metadata correction for sensor-rich video sequences 传感器丰富的视频序列的自动地理元数据校正

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2997015

Yifang Yin, Guanfeng Wang, Roger Zimmermann

{"title":"Automatic geographic metadata correction for sensor-rich video sequences","authors":"Yifang Yin, Guanfeng Wang, Roger Zimmermann","doi":"10.1145/2996913.2997015","DOIUrl":"https://doi.org/10.1145/2996913.2997015","url":null,"abstract":"Videos recorded with current mobile devices are increasingly geotagged at fine granularity and used in various location- based applications and services. However, raw sensor data collected is often noisy, resulting in subsequent inaccurate geospatial analysis. In this study, we focus on the challenging correction of compass readings and present an automatic approach to reduce these metadata errors. Given the small geo-distance between consecutive video frames, image-based localization does not work due to the high ambiguity in the depth reconstruction of the scene. As an alternative, we collect geographic context from OpenStreetMap and estimate the absolute viewing direction by comparing the image scene to world projections obtained with different external camera parameters. To design a comprehensive model, we further incorporate smooth approximation and feature-based rotation estimation when formulating the error terms. Experimental results show that our proposed pyramid-based method outperforms its competitors and reduces orientation errors by an average of 58.8%. Hence, for downstream applications, improved results can be obtained with these more accurate geo-metadata. To illustrate, we present the performance gain in landmark retrieval and tag suggestion by utilizing the accuracy-enhanced geo-metadata.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90119195","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: 3

Managing massive trajectories on the cloud 管理云上的大量轨迹

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996916

Jie Bao, Ruiyuan Li, Xiuwen Yi, Yu Zheng

{"title":"Managing massive trajectories on the cloud","authors":"Jie Bao, Ruiyuan Li, Xiuwen Yi, Yu Zheng","doi":"10.1145/2996913.2996916","DOIUrl":"https://doi.org/10.1145/2996913.2996916","url":null,"abstract":"With advances in location-acquisition techniques, such as GPS- embedded phones, an enormous volume of trajectory data is generated, by people, vehicles, and animals. This trajectory data is one of the most important data sources in many urban computing applications, e.g., traffic modeling, user profiling analysis, air quality inference, and resource allocation. To utilize large scale trajectory data efficiently and effectively, cloud computing platforms, e.g., Microsoft Azure, are the most convenient and economic way. However, traditional cloud computing platforms are not designed to deal with spatio-temporal data, such as trajectories. To this end, we design and implement a holistic cloud-based trajectory data management system on Microsoft Azure to bridge the gap between trajectory data and urban applications. Our system can efficiently store, index, and query large trajectory data with three functions: 1) trajectory ID-temporal query, 2) trajectory spatio-temporal query, and 3) trajectory mapmatching. The efficiency of the system is tested and tuned based on real-time trajectory data feeds. The system is currently used in many internal urban applications, as we will illustrate using case studies.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89114553","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: 50

RxSpatial: the reactive spatial library RxSpatial:响应空间库

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996948

Youying Shi, Abdeltawab M. Hendawi, Jayant Gupta, H. Fattah, Mohamed H. Ali

{"title":"RxSpatial: the reactive spatial library","authors":"Youying Shi, Abdeltawab M. Hendawi, Jayant Gupta, H. Fattah, Mohamed H. Ali","doi":"10.1145/2996913.2996948","DOIUrl":"https://doi.org/10.1145/2996913.2996948","url":null,"abstract":"The spatial libraries that have been developed by Microsoft, IBM and Oracle have substantially changed the capabilities of geospatial computing. These libraries implement several functionalities that include intersection, distance, and area for various geospatial objects. These libraries came out to address a wealth of use cases that were challenging in that era. As time goes by, GPS devices and location-aware mobile technologies increased the demand for geospatial computing, in general, and for real time geostreaming, in particular. Existing commercial spatial libraries were originally designed to support operations on stationary objects with limited or no capabilities for moving objects. In this paper, we introduce the RxSpatial library, a real time reactive spatial library for spatiotemporal stream query processing. RxSpatial provides, (1) a front-end, which is a programming interface for developers who are familiar with the Microsoft. NET Reactive framework and the Microsoft SQL Server Spatial Library, and (2) a back-end for processing spatial operations in a streaming fashion. RxSpatial provides the programming convenience at the front end and the query processing efficiency at the back end.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89118045","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

GCMF: an efficient end-to-end spatial join system over large polygonal datasets on GPGPU platform GCMF:基于GPGPU平台的大型多边形数据集的高效端到端空间连接系统

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996982

D. Aghajarian, S. Puri, S. Prasad

{"title":"GCMF: an efficient end-to-end spatial join system over large polygonal datasets on GPGPU platform","authors":"D. Aghajarian, S. Puri, S. Prasad","doi":"10.1145/2996913.2996982","DOIUrl":"https://doi.org/10.1145/2996913.2996982","url":null,"abstract":"Given two layers of large polygonal datasets, detecting those pairs of cross-layer polygons which satisfy a join predicate, such as intersection or contain, is one of the most computationally intensive primitive operations in the spatial domain applications. In this work, we introduce GCMF, an end-to-end software system, that is able to handle spatial join (with ST_Intersect operation) over non-indexed polygonal datasets with over 3 GB file size comprising more than 600, 000 polygons on a single GPU within less than 8 sec by applying innovative filter and refinement techniques. GCMF performs a two-step filtering phase. 1) A sort-based Minimum Bounding Rectangle (MBR) filtering step detects potentially overlapping polygon pairs up to 20 times faster than the optimized GEOS library routine. 2) A linear time Common MBR filtering step (based on the overlapping area of two given MBRs) that not only eliminates two-third of the candidate polygon pairs but also reduces the number of edges to be considered in the refinement phase by 40-fold on an average based on our experimental results with real datasets. Furthermore, for the refinement phase, GCMF implements a load-balanced parallel point-in-polygon and edge-intersection tests over GPU. Our experimental results with three different real datasets show up to 39-fold end-to- end speedup versus optimized sequential routines of GEOS C++ library as well as PostgreSQL spatial database with PostGIS.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86568832","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: 26

Deducing individual driving preferences for user-aware navigation 为用户感知导航推断个人驾驶偏好

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2997004

S. Funke, S. Laue, Sabine Storandt

引用次数: 12

A visual and computational analysis approach for exploring significant locations and time periods along a bus route 一种可视化和计算分析方法，用于探索沿公交路线的重要位置和时间段

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996936

J. Mazimpaka, S. Timpf

{"title":"A visual and computational analysis approach for exploring significant locations and time periods along a bus route","authors":"J. Mazimpaka, S. Timpf","doi":"10.1145/2996913.2996936","DOIUrl":"https://doi.org/10.1145/2996913.2996936","url":null,"abstract":"Understanding human mobility is important for planning and delivering various services in urban area. An important element for mobility understanding is to understand the context in which the movement takes place. In this direction, we propose a method for identifying significant locations and time periods along a bus route. The significance is based on special characteristics that locations have during specific time periods as determined from their effect of these locations on the movement of the bus. The method extracts discriminative features from the space, time and other selected attributes and then classifies locations and time periods into 5 significance classes. The classes are then rendered in different views for discovering and understanding patterns. The novelty of the method is an explicit consideration of the time dimension at different granularity levels and a visualization that facilitates comparison across the space and time dimensions while avoiding a visual clutter. We demonstrate the applicability of our approach by applying it on a large set of bus trajectories.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86140283","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

Personalized location models with adaptive mixtures 具有自适应混合的个性化位置模型

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996953

Moshe Lichman, Dimitrios Kotzias, Padhraic Smyth

引用次数: 4

CDO: extremely high-throughput road distance computations on city road networks CDO:在城市道路网络上进行极高吞吐量的道路距离计算

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996921

Shangfu Peng, H. Samet

引用次数: 6