Map matching has long been a fundamental yet challenging problem. However, there are currently only a few public small-scale map matching benchmark datasets. Both the GPS trajectories and the road network in the existing map matching datasets are represented by location only, which cannot support the development of data-driven and semantic-enriched map matching algorithms that have increasingly emerged in recent years. To bridge the gap, we present the first large-scale attribute-rich map matching benchmark dataset covering two cities in Southeast Asia (i.e., Singapore and Jakarta). Our GPS trajectories contain rich contextual information including the accuracy level, bearing, speed, and transport mode in addition to the latitude and longitude geo-coordinates. The underlying road network is a snapshot of the OpenStreetMap where roads are associated with rich attributes such as road type, speed limit, etc. To ensure the quality of our dataset, the annotation of the map-matched routes has been conducted by a team of professional map operators. Analysis on our dataset provides new insights into the challenges and opportunities in map matching algorithms.
{"title":"Grab-Posisi-L: A Labelled GPS Trajectory Dataset for Map Matching in Southeast Asia","authors":"Zhengmin Xu, Yifang Yin, Chengcheng Dai, Xiaocheng Huang, Robinson Kudali, Jinal Foflia, Guanfeng Wang, Roger Zimmermann","doi":"10.1145/3397536.3422218","DOIUrl":"https://doi.org/10.1145/3397536.3422218","url":null,"abstract":"Map matching has long been a fundamental yet challenging problem. However, there are currently only a few public small-scale map matching benchmark datasets. Both the GPS trajectories and the road network in the existing map matching datasets are represented by location only, which cannot support the development of data-driven and semantic-enriched map matching algorithms that have increasingly emerged in recent years. To bridge the gap, we present the first large-scale attribute-rich map matching benchmark dataset covering two cities in Southeast Asia (i.e., Singapore and Jakarta). Our GPS trajectories contain rich contextual information including the accuracy level, bearing, speed, and transport mode in addition to the latitude and longitude geo-coordinates. The underlying road network is a snapshot of the OpenStreetMap where roads are associated with rich attributes such as road type, speed limit, etc. To ensure the quality of our dataset, the annotation of the map-matched routes has been conducted by a team of professional map operators. Analysis on our dataset provides new insights into the challenges and opportunities in map matching algorithms.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117212683","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}
Location services are one of the most used applications today on mobile devices. The vast majority of localization systems propose solutions for locating the user in a 2D single floor environment. However, accurate estimation of the user's floor level, in tall multistory buildings, is a crucial basis for many applications, especially for emergency services. This paper presents a fingerprinting-based system that provides a low-cost floor localization service using the ubiquitous cellular signals received by the user's cell phone. Specifically, a convolutional neural network is trained to map the sequential change of the received cellular signals to the corresponding floor. Evaluation using different Android phones shows that the proposed system can track the user floor with at least 95.9% accuracy in different scenarios. This demonstrates the superiority of the system compared to the state-of-the-art systems in all experiments.
{"title":"Deep Learning-based Floor Prediction Using Cell Network Information","authors":"K. Alkiek, Aya Othman, Hamada Rizk, M. Youssef","doi":"10.1145/3397536.3428349","DOIUrl":"https://doi.org/10.1145/3397536.3428349","url":null,"abstract":"Location services are one of the most used applications today on mobile devices. The vast majority of localization systems propose solutions for locating the user in a 2D single floor environment. However, accurate estimation of the user's floor level, in tall multistory buildings, is a crucial basis for many applications, especially for emergency services. This paper presents a fingerprinting-based system that provides a low-cost floor localization service using the ubiquitous cellular signals received by the user's cell phone. Specifically, a convolutional neural network is trained to map the sequential change of the received cellular signals to the corresponding floor. Evaluation using different Android phones shows that the proposed system can track the user floor with at least 95.9% accuracy in different scenarios. This demonstrates the superiority of the system compared to the state-of-the-art systems in all experiments.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121231342","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}
The wide spread of GPS-enabled devices and the Internet of Things (IoT) has increased the amount of spatial data being generated every second. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial data streaming systems that scale to process in real-time large amounts of streamed spatial data. The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, it is challenging to estimate the workload of each machine because spatial data and query streams are skewed and rapidly change with time and users' interests. Moreover, a distributed spatial streaming system often does not maintain a global system workload state because it requires high network and processing overheads to be collected from the machines in the system. This paper introduces TrioStat; an online workload estimation technique that relies on a probabilistic model for estimating the workload of partitions and machines in a distributed spatial data streaming system. It is infeasible to collect and exchange statistics with a centralized unit because it requires high network overhead. Instead, TrioStat uses a decentralised technique to collect and maintain the required statistics in real-time locally in each machine. TrioStat enables distributed spatial data streaming systems to compare the workloads of machines as well as the workloads of data partitions. TrioStat requires minimal network and storage overhead. Moreover, the required storage is distributed across the system's machines.
{"title":"TrioStat","authors":"Anas Daghistani, Walid G. Aref, Arif Ghafoor","doi":"10.1145/3397536.3422220","DOIUrl":"https://doi.org/10.1145/3397536.3422220","url":null,"abstract":"The wide spread of GPS-enabled devices and the Internet of Things (IoT) has increased the amount of spatial data being generated every second. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial data streaming systems that scale to process in real-time large amounts of streamed spatial data. The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, it is challenging to estimate the workload of each machine because spatial data and query streams are skewed and rapidly change with time and users' interests. Moreover, a distributed spatial streaming system often does not maintain a global system workload state because it requires high network and processing overheads to be collected from the machines in the system. This paper introduces TrioStat; an online workload estimation technique that relies on a probabilistic model for estimating the workload of partitions and machines in a distributed spatial data streaming system. It is infeasible to collect and exchange statistics with a centralized unit because it requires high network overhead. Instead, TrioStat uses a decentralised technique to collect and maintain the required statistics in real-time locally in each machine. TrioStat enables distributed spatial data streaming systems to compare the workloads of machines as well as the workloads of data partitions. TrioStat requires minimal network and storage overhead. Moreover, the required storage is distributed across the system's machines.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126232464","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}
Philip E. Brown, Krystian Czapiga, Arun Jotshi, Y. Kanza, Velin Kounev, Poornima Suresh
In telecommunication networks, microwave backhaul links are often used as wireless connections between towers. They are used in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to obstructions and interference. When deploying microwave links, there should be a clear line of sight between every pair of receiver and transmitter, and a buffer around the line of sight defined by the first Fresnel zone should be clear of obstacles. In this paper we discuss the geospatial aspects of microwave backhaul planning and the challenges in developing a system for large scale planning, with the following requirements: (1) the need to cover all of the USA, (2) distance of up to 80 kilometers between towers, and (3) computing batches of thousands of pairs within a few minutes.
{"title":"Large-Scale Geospatial Planning of Wireless Backhaul Links","authors":"Philip E. Brown, Krystian Czapiga, Arun Jotshi, Y. Kanza, Velin Kounev, Poornima Suresh","doi":"10.1145/3397536.3422256","DOIUrl":"https://doi.org/10.1145/3397536.3422256","url":null,"abstract":"In telecommunication networks, microwave backhaul links are often used as wireless connections between towers. They are used in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to obstructions and interference. When deploying microwave links, there should be a clear line of sight between every pair of receiver and transmitter, and a buffer around the line of sight defined by the first Fresnel zone should be clear of obstacles. In this paper we discuss the geospatial aspects of microwave backhaul planning and the challenges in developing a system for large scale planning, with the following requirements: (1) the need to cover all of the USA, (2) distance of up to 80 kilometers between towers, and (3) computing batches of thousands of pairs within a few minutes.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128049955","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}
Geometric intersection algorithms are fundamental in spatial analysis in Geographic Information System (GIS). Applying high performance computing to perform geometric intersection on huge amount of spatial data to get real-time results is necessary. Given two input geometries (polygon or polyline) of a candidate pair, we introduce a new two-step geospatial filter that first creates sketches of the geometries and uses it to detect workload and then refines the sketches by the common areas of sketches to decrease the overall computations in the refine phase. We call this filter PolySketch-based CMBR (PSCMBR) filter. We show the application of this filter in speeding-up line segment intersections (LSI) reporting task that is a basic computation in a variety of geospatial applications like polygon overlay and spatial join. We also developed a parallel PolySketch-based PNP filter to perform PNP tests on GPU which reduces computational workload in PNP tests. Finally, we integrated these new filters to the hierarchical filter and refinement (HiFiRe) system to solve geometric intersection problem. We have implemented the new filter and refine system on GPU using CUDA. The new filters introduced in this paper reduce more computational workload when compared to existing filters. As a result, we get on average 7.96X speedup compared to our prior version of HiFiRe system.
{"title":"Efficient Filters for Geometric Intersection Computations using GPU","authors":"Yiming Liu, S. Puri","doi":"10.1145/3397536.3422264","DOIUrl":"https://doi.org/10.1145/3397536.3422264","url":null,"abstract":"Geometric intersection algorithms are fundamental in spatial analysis in Geographic Information System (GIS). Applying high performance computing to perform geometric intersection on huge amount of spatial data to get real-time results is necessary. Given two input geometries (polygon or polyline) of a candidate pair, we introduce a new two-step geospatial filter that first creates sketches of the geometries and uses it to detect workload and then refines the sketches by the common areas of sketches to decrease the overall computations in the refine phase. We call this filter PolySketch-based CMBR (PSCMBR) filter. We show the application of this filter in speeding-up line segment intersections (LSI) reporting task that is a basic computation in a variety of geospatial applications like polygon overlay and spatial join. We also developed a parallel PolySketch-based PNP filter to perform PNP tests on GPU which reduces computational workload in PNP tests. Finally, we integrated these new filters to the hierarchical filter and refinement (HiFiRe) system to solve geometric intersection problem. We have implemented the new filter and refine system on GPU using CUDA. The new filters introduced in this paper reduce more computational workload when compared to existing filters. As a result, we get on average 7.96X speedup compared to our prior version of HiFiRe system.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349506","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}
We present staty, a browser-based tool for quality assurance of public transit station tagging in OpenStreetMap (OSM). Building on the results of a similarity classifier for these stations, our tool visualizes name tag errors as well as incorrect and/or missing station group relations. Detailed edit suggestions are provided for individual objects. This is done intrinsically without an external ground truth. Instead, the underlying classifier is trained on the OSM data itself. We describe how our tool derives errors and suggestions from station tag similarities and provide experimental results on the OSM data of the United Kingdom, the United States, and a dataset consisting of Germany, Switzerland, and Austria. Our tool can be accessed under https://staty.cs.uni-freiburg.de.
{"title":"staty","authors":"H. Bast, P. Brosi, Markus Näther","doi":"10.1145/3397536.3422342","DOIUrl":"https://doi.org/10.1145/3397536.3422342","url":null,"abstract":"We present staty, a browser-based tool for quality assurance of public transit station tagging in OpenStreetMap (OSM). Building on the results of a similarity classifier for these stations, our tool visualizes name tag errors as well as incorrect and/or missing station group relations. Detailed edit suggestions are provided for individual objects. This is done intrinsically without an external ground truth. Instead, the underlying classifier is trained on the OSM data itself. We describe how our tool derives errors and suggestions from station tag similarities and provide experimental results on the OSM data of the United Kingdom, the United States, and a dataset consisting of Germany, Switzerland, and Austria. Our tool can be accessed under https://staty.cs.uni-freiburg.de.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121569980","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}
The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose the right dataset to use in their analysis. Unfortunately, all the publicly available repositories for geospatial datasets provide a list of datasets with some information about them with no way to explore the datasets beforehand. Through this demonstration, we propose the repository, UCR-Star, that is capable of hosting hundreds of thousands of geospatial datasets that a user can explore visually to judge their quality before even downloading them. This demo provides a deeper dive into the core engine behind UCR-Star. It provides a web interface geared towards database researchers to understand how the index internally works. It provides a comparison interface where the attendees can see side-by-side how two versions of the system work with the ability to customize each of them separately. Finally, the interface reports the response time of the indexes for a quantitative comparison.
{"title":"A Demonstration of Interactive Exploration of Big Geospatial Data on UCR-Star","authors":"Saheli Ghosh, Akil Sevim, A. Eldawy","doi":"10.1145/3397536.3422334","DOIUrl":"https://doi.org/10.1145/3397536.3422334","url":null,"abstract":"The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose the right dataset to use in their analysis. Unfortunately, all the publicly available repositories for geospatial datasets provide a list of datasets with some information about them with no way to explore the datasets beforehand. Through this demonstration, we propose the repository, UCR-Star, that is capable of hosting hundreds of thousands of geospatial datasets that a user can explore visually to judge their quality before even downloading them. This demo provides a deeper dive into the core engine behind UCR-Star. It provides a web interface geared towards database researchers to understand how the index internally works. It provides a comparison interface where the attendees can see side-by-side how two versions of the system work with the ability to customize each of them separately. Finally, the interface reports the response time of the indexes for a quantitative comparison.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121741836","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}
Although large-scale 3D reconstruction by photogrammetry has been well studied and applied, the reconstruction of night scenery in urban areas has not been thoroughly considered. At night, low-light conditions often cause the images to lack sharpness and high-dynamic range issue leads to saturation. The SFM reconstruction pipeline that works well in daylight is likely to recover only limited dense points of bright fragmented objects near artificial lighting. Here, we propose a novel solution based on registration and synthesis between the night-time reconstruction and that of the same region in daytime. A registration pipeline is developed for conformal matching of the day and night point clouds. For the coarse registration step, we use detected plane features to search and match 4-plane congruent sets. For the fine registration step, we consider the positions of windows, a commonly-occurring object cue in urban building scenes as markers for accurate positioning. This leads to final registration error less than 0.2 degrees in rotation, and 0.2% in scale and translation. Finally, we synthesize the daytime textured model and the night point clouds to produce vivid visual effects of urban night scenery.
{"title":"Urban Night Scenery Reconstruction by Day-night Registration and Synthesis","authors":"A. Dai, D. Meger","doi":"10.1145/3397536.3422200","DOIUrl":"https://doi.org/10.1145/3397536.3422200","url":null,"abstract":"Although large-scale 3D reconstruction by photogrammetry has been well studied and applied, the reconstruction of night scenery in urban areas has not been thoroughly considered. At night, low-light conditions often cause the images to lack sharpness and high-dynamic range issue leads to saturation. The SFM reconstruction pipeline that works well in daylight is likely to recover only limited dense points of bright fragmented objects near artificial lighting. Here, we propose a novel solution based on registration and synthesis between the night-time reconstruction and that of the same region in daytime. A registration pipeline is developed for conformal matching of the day and night point clouds. For the coarse registration step, we use detected plane features to search and match 4-plane congruent sets. For the fine registration step, we consider the positions of windows, a commonly-occurring object cue in urban building scenes as markers for accurate positioning. This leads to final registration error less than 0.2 degrees in rotation, and 0.2% in scale and translation. Finally, we synthesize the daytime textured model and the night point clouds to produce vivid visual effects of urban night scenery.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136277","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}
This paper proposes an efficient method for the weighted region problem (WRP) on the surface of three-dimensional terrains. WRP is a classical path planning problem, asking for the minimum cost path between two given points crossing different regions in which each region is assigned a traversal cost per unit distance. Although WRP has been studied for decades, the exact solution for WRP, even in a two-dimensional environment, is unknown. Thus, the existing solutions for WRP are all approximations with decomposition-based and heuristic methods being the most widely-used in practice. However, when a very-close to optimal path is required, especially on real terrains with many regions, these approaches are not guaranteed or cannot return a satisfactory result in reasonable time. In this paper, we first present a new algorithm of finding a very-close optimal path, based on a user-defined parameter &dgr;, between two points, crossing the surface of a sequence of regions in 3D, using Snell's law of physical refraction. We then show how to combine this algorithm with one existing decomposition-based method to compute a close optimal path over the whole terrain. In addition to a theoretical analysis, with an extensive set of test cases, the practicality and feasibility of our method are confirmed by that, our method always runs faster and returns closer to optimal paths in comparison with the existing ones.
{"title":"Close Weighted Shortest Paths on 3D Terrain Surfaces","authors":"N. Tran, Michael J. Dinneen, S. Linz","doi":"10.1145/3397536.3422216","DOIUrl":"https://doi.org/10.1145/3397536.3422216","url":null,"abstract":"This paper proposes an efficient method for the weighted region problem (WRP) on the surface of three-dimensional terrains. WRP is a classical path planning problem, asking for the minimum cost path between two given points crossing different regions in which each region is assigned a traversal cost per unit distance. Although WRP has been studied for decades, the exact solution for WRP, even in a two-dimensional environment, is unknown. Thus, the existing solutions for WRP are all approximations with decomposition-based and heuristic methods being the most widely-used in practice. However, when a very-close to optimal path is required, especially on real terrains with many regions, these approaches are not guaranteed or cannot return a satisfactory result in reasonable time. In this paper, we first present a new algorithm of finding a very-close optimal path, based on a user-defined parameter &dgr;, between two points, crossing the surface of a sequence of regions in 3D, using Snell's law of physical refraction. We then show how to combine this algorithm with one existing decomposition-based method to compute a close optimal path over the whole terrain. In addition to a theoretical analysis, with an extensive set of test cases, the practicality and feasibility of our method are confirmed by that, our method always runs faster and returns closer to optimal paths in comparison with the existing ones.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396412","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}
The continuous k-Nearest Neighbor queries over spatial-textual data streams (abbr. CkQST) retrieve and continuously monitor at most k nearest neighbor (abbr. kNN) objects to the user-specified location containing all the user-specified keywords, which is the core operation of numerous location-based publish/subscribe systems. Such a system is usually subscribed with a massive number of CkQST and evaluated simultaneously whenever new objects are incoming and old objects are expiring. The approach to evaluating CkQST is to construct a spatial-textual hybrid index for subscribed queries and matching the incoming objects utilizing the filtering capabilities of the index. For CkQST, the minimal spatial search range covering kNN objects changes frequently with the arrival and expiration of qualified objects, and the cost of updating the index is prohibitively high. To efficiently evaluate CkQST, we extend Quad-tree with an inverted index, and exploit it with three techniques, i.e. a memory-based cost model, a block-based ordered inverted index and an adaptive insertion strategy. The experiments on comprehensive datasets demonstrate the effectiveness and efficiency of our proposed techniques.
{"title":"Optimizing Continuous kNN Queries over Large-Scale Spatial-Textual Data Streams","authors":"Rong Yang, Baoning Niu","doi":"10.1145/3397536.3422225","DOIUrl":"https://doi.org/10.1145/3397536.3422225","url":null,"abstract":"The continuous k-Nearest Neighbor queries over spatial-textual data streams (abbr. CkQST) retrieve and continuously monitor at most k nearest neighbor (abbr. kNN) objects to the user-specified location containing all the user-specified keywords, which is the core operation of numerous location-based publish/subscribe systems. Such a system is usually subscribed with a massive number of CkQST and evaluated simultaneously whenever new objects are incoming and old objects are expiring. The approach to evaluating CkQST is to construct a spatial-textual hybrid index for subscribed queries and matching the incoming objects utilizing the filtering capabilities of the index. For CkQST, the minimal spatial search range covering kNN objects changes frequently with the arrival and expiration of qualified objects, and the cost of updating the index is prohibitively high. To efficiently evaluate CkQST, we extend Quad-tree with an inverted index, and exploit it with three techniques, i.e. a memory-based cost model, a block-based ordered inverted index and an adaptive insertion strategy. The experiments on comprehensive datasets demonstrate the effectiveness and efficiency of our proposed techniques.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602185","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}
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