Tobias Emrich, H. Kriegel, Peer Kröger, M. Renz, Naixin Xu, Andreas Züfle
In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server architecture. The RkNN monitoring query computes for a given query object q, the set RkNN(q) of objects having q as one of their k-nearest neighbors for each point in time. In our setting the central server can poll the exact positions of the clients if needed. However in contrast to most existing approaches for this problem we argue that in various applications, the limiting factor is not the computational time needed but the amount of traffic sent via the network. We propose an approach that minimizes the amount of communication between clients and central server by an intelligent approximation of the position of the clients. Additionally we propose several poll heuristics in order to further decrease the communication costs. In the experimental section we show the significant impact of our proposed improvements to our basic algorithm.
{"title":"Reverse k-Nearest Neighbor monitoring on mobile objects","authors":"Tobias Emrich, H. Kriegel, Peer Kröger, M. Renz, Naixin Xu, Andreas Züfle","doi":"10.1145/1869790.1869870","DOIUrl":"https://doi.org/10.1145/1869790.1869870","url":null,"abstract":"In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server architecture. The RkNN monitoring query computes for a given query object q, the set RkNN(q) of objects having q as one of their k-nearest neighbors for each point in time. In our setting the central server can poll the exact positions of the clients if needed. However in contrast to most existing approaches for this problem we argue that in various applications, the limiting factor is not the computational time needed but the amount of traffic sent via the network. We propose an approach that minimizes the amount of communication between clients and central server by an intelligent approximation of the position of the clients. Additionally we propose several poll heuristics in order to further decrease the communication costs. In the experimental section we show the significant impact of our proposed improvements to our basic algorithm.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125652889","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}
Over the last 15 years spatial databases and mapping services have become one of the most important applications on the Internet. In that time we have seen transformative increases in the availability of spatial data such as detailed satellite, streetside and casual images, 3D terrain and building models, and real-time traffic information. Such data has made it possible to create a variety of new kinds of maps and online experiences of any location on earth. Yet, only a handful of different kinds of maps are available online today. Moreover, it is not always clear what task today's online maps are designed to solve. This talk presents a user-centered view of map design. Some of the difficult spatial tasks people regularly face as they navigate the world are described and the long history of mapmaking is drawn upon to show a variety of maps that are carefully designed to address these tasks. Examples include maps designed to help users find their way from one location to another, to aid viewers in better understanding the spatial layout of 3D environments, and to assist tourists in finding points of interest in a new city. The talk concludes with a set of open challenges for online map design.
{"title":"Cartography and information presentation: a graphics/visualization perspective","authors":"Maneesh Agrawala","doi":"10.1145/1869790.1869792","DOIUrl":"https://doi.org/10.1145/1869790.1869792","url":null,"abstract":"Over the last 15 years spatial databases and mapping services have become one of the most important applications on the Internet. In that time we have seen transformative increases in the availability of spatial data such as detailed satellite, streetside and casual images, 3D terrain and building models, and real-time traffic information. Such data has made it possible to create a variety of new kinds of maps and online experiences of any location on earth. Yet, only a handful of different kinds of maps are available online today. Moreover, it is not always clear what task today's online maps are designed to solve.\u0000 This talk presents a user-centered view of map design. Some of the difficult spatial tasks people regularly face as they navigate the world are described and the long history of mapmaking is drawn upon to show a variety of maps that are carefully designed to address these tasks. Examples include maps designed to help users find their way from one location to another, to aid viewers in better understanding the spatial layout of 3D environments, and to assist tourists in finding points of interest in a new city. The talk concludes with a set of open challenges for online map design.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124689138","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}
Yu Zheng, Xixuan Fen, Xing Xie, Shuang Peng, J. Fu
The quality of a local search engine, such as Google and Bing Maps, heavily relies on its geographic datasets. Typically, these datasets are obtained from multiple sources, e.g., different vendors or public yellow-page websites. Therefore, the same location entity, like a restaurant, might have multiple records with slightly different presentations of title and address in different data sources. For instance, 'Seattle Premium Outlets' and 'Seattle Premier Outlet Mall' describe the same Outlet located in the same place while their titles are not identical. This will cause many nearly-duplicated records in a location database, which would bring trouble to data management and make users confused by the various search results of a query. To detect these nearly duplicated records, we propose a machine-learning-based approach, which is comprised of three steps: candidate selection, feature extraction and training/inference. Three key features consisting of name similarity, address similarity and category similarity, as well as corresponding metrics, are proposed to model the differences between two entity records. We evaluate our method with intensive experiments based on a large-scale real dataset. As a result, both the precision and recall of our method exceeded 90%.
谷歌(Google)和必应地图(Bing Maps)等本地搜索引擎的质量严重依赖于其地理数据集。通常，这些数据集是从多个来源获得的，例如，不同的供应商或公共黄页网站。因此，相同的位置实体(如餐馆)在不同的数据源中可能有多条记录，这些记录的标题和地址表示略有不同。例如，“Seattle Premium Outlets”和“Seattle Premier Outlet Mall”描述的是位于同一地点的同一家奥特莱斯，但它们的名称并不相同。这将导致位置数据库中有许多几乎重复的记录，这将给数据管理带来麻烦，并使用户对查询的各种搜索结果感到困惑。为了检测这些几乎重复的记录，我们提出了一种基于机器学习的方法，该方法由三个步骤组成:候选项选择、特征提取和训练/推理。提出了名称相似度、地址相似度和类别相似度三个关键特征，以及相应的度量标准来对两个实体记录之间的差异进行建模。我们通过基于大规模真实数据集的密集实验来评估我们的方法。结果表明，该方法的查准率和查全率均超过90%。
{"title":"Detecting nearly duplicated records in location datasets","authors":"Yu Zheng, Xixuan Fen, Xing Xie, Shuang Peng, J. Fu","doi":"10.1145/1869790.1869812","DOIUrl":"https://doi.org/10.1145/1869790.1869812","url":null,"abstract":"The quality of a local search engine, such as Google and Bing Maps, heavily relies on its geographic datasets. Typically, these datasets are obtained from multiple sources, e.g., different vendors or public yellow-page websites. Therefore, the same location entity, like a restaurant, might have multiple records with slightly different presentations of title and address in different data sources. For instance, 'Seattle Premium Outlets' and 'Seattle Premier Outlet Mall' describe the same Outlet located in the same place while their titles are not identical. This will cause many nearly-duplicated records in a location database, which would bring trouble to data management and make users confused by the various search results of a query. To detect these nearly duplicated records, we propose a machine-learning-based approach, which is comprised of three steps: candidate selection, feature extraction and training/inference. Three key features consisting of name similarity, address similarity and category similarity, as well as corresponding metrics, are proposed to model the differences between two entity records. We evaluate our method with intensive experiments based on a large-scale real dataset. As a result, both the precision and recall of our method exceeded 90%.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122233779","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 paper presents an extension of the particle filtering algorithm that is applicable when an accurate state prediction model cannot be specified but a database of prior state evolution tracks is available. The conventional particle filtering algorithm represents the belief state as a collection of particles, where each particle is a sample from the state space. The particles are updated by applying the state space equations. In the proposed approach, each particle is an instance of a complete state trajectory, drawn from the database of historic state trajectories. An explicit state update model is not required as the trajectory represented by each particle is covers the entire modeling time period. When new observations become available, a proportion of the particles are replaced using trajectories from the database, selected based on distance from the observation. This tracking algorithm is applicable where the state evolves in a complex manner as in the eye of tropical cyclones. The proposed technique is evaluated by tracking selected cyclones from 2005 using a database of cyclone tracks from the previous 25 years.
{"title":"A variant of particle filtering using historic datasets for tracking complex geospatial phenomena","authors":"A. Panangadan, A. Talukder","doi":"10.1145/1869790.1869824","DOIUrl":"https://doi.org/10.1145/1869790.1869824","url":null,"abstract":"The paper presents an extension of the particle filtering algorithm that is applicable when an accurate state prediction model cannot be specified but a database of prior state evolution tracks is available. The conventional particle filtering algorithm represents the belief state as a collection of particles, where each particle is a sample from the state space. The particles are updated by applying the state space equations. In the proposed approach, each particle is an instance of a complete state trajectory, drawn from the database of historic state trajectories. An explicit state update model is not required as the trajectory represented by each particle is covers the entire modeling time period. When new observations become available, a proportion of the particles are replaced using trajectories from the database, selected based on distance from the observation. This tracking algorithm is applicable where the state evolves in a complex manner as in the eye of tropical cyclones. The proposed technique is evaluated by tracking selected cyclones from 2005 using a database of cyclone tracks from the previous 25 years.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128871160","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}
In the classical triad of vector, raster, and meta data, it is the raster part which is not yet sufficiently supported in SDIs nowadays. Consequently, integration of earth observation imagery, LIDAR, legacy map scans, etc. into Spatial Data Infrastructures (SDIs) remains incomplete. In terms of standards, the OGC Web Coverage Service (WCS) Standard defines open interfaces for accessing and processing of raster data, more generally: coverages. In August 2010, the completely overhauled WCS 2.0 has been adopted by OGC. To make coverages interchangeable across all OGC-based services, WCS 2.0 has been based on Geography Markup Language (GML) 3.2.1, with a small, backwards compatible addition to achieve informational completeness. In parallel to specification writing, its reference implementation and an online demo are being pursued. WCS 2.0 offers several advantages over previous versions, such as: support for general n-D raster data and non-raster coverage types; crisp, modular, and easy to understand; flexible and adaptive; harmonized with GML and Sensor Web Enablement (SWE); improved testability; and allows for efficient and scalable implementations. In this paper we present WCS 2.0 and some central design rationales. Further, we inspect the reference implementation architecture discussing some features critical for scalability. Finally, we give an outlook on next steps, such as the planned WCS Earth Observation Application Profile.
在矢量、栅格和元数据的经典三元组合中，栅格部分目前在sdi中还没有得到足够的支持。因此，将地球观测图像、激光雷达、遗留地图扫描等整合到空间数据基础设施(sdi)中仍然不完整。在标准方面，OGC Web Coverage Service (WCS)标准定义了用于访问和处理栅格数据的开放接口，更一般地说:覆盖。2010年8月，OGC采用了经过全面修改的WCS 2.0。为了使覆盖范围在所有基于ogc的服务之间可互换，WCS 2.0基于地理标记语言(GML) 3.2.1，并添加了少量向后兼容的内容以实现信息的完整性。在编写规范的同时，它的参考实现和在线演示也在进行中。与以前的版本相比，WCS 2.0提供了几个优势，例如:支持一般的n-D栅格数据和非栅格覆盖类型;清晰，模块化，易于理解;灵活和适应性强的;与GML和传感器网络支持(SWE)协调;改进的可测试性;并且允许高效和可伸缩的实现。在本文中，我们介绍了WCS 2.0和一些主要的设计原理。此外，我们考察了参考实现体系结构，讨论了一些对可伸缩性至关重要的特性。最后，展望了WCS对地观测应用概况等后续工作。
{"title":"Beyond rasters: introducing the new OGC web coverage service 2.0","authors":"P. Baumann","doi":"10.1145/1869790.1869835","DOIUrl":"https://doi.org/10.1145/1869790.1869835","url":null,"abstract":"In the classical triad of vector, raster, and meta data, it is the raster part which is not yet sufficiently supported in SDIs nowadays. Consequently, integration of earth observation imagery, LIDAR, legacy map scans, etc. into Spatial Data Infrastructures (SDIs) remains incomplete. In terms of standards, the OGC Web Coverage Service (WCS) Standard defines open interfaces for accessing and processing of raster data, more generally: coverages. In August 2010, the completely overhauled WCS 2.0 has been adopted by OGC. To make coverages interchangeable across all OGC-based services, WCS 2.0 has been based on Geography Markup Language (GML) 3.2.1, with a small, backwards compatible addition to achieve informational completeness. In parallel to specification writing, its reference implementation and an online demo are being pursued.\u0000 WCS 2.0 offers several advantages over previous versions, such as: support for general n-D raster data and non-raster coverage types; crisp, modular, and easy to understand; flexible and adaptive; harmonized with GML and Sensor Web Enablement (SWE); improved testability; and allows for efficient and scalable implementations.\u0000 In this paper we present WCS 2.0 and some central design rationales. Further, we inspect the reference implementation architecture discussing some features critical for scalability. Finally, we give an outlook on next steps, such as the planned WCS Earth Observation Application Profile.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114638870","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}
Map matching is a fundamental operation in many applications such as traffic analysis and location-aware services, the killer apps for ubiquitous computing. In past, several map matching approaches have been proposed. Roughly, they can be categorized into four groups: geometric, topological, probabilistic, and other advanced techniques. Surprisingly, kernel methods have not received attention yet although they are very popular in the machine learning community due to their solid mathematical foundation, tendency toward easy geometric interpretation, and strong empirical performance in a wide variety of domains. In this paper, we show how to employ kernels for map matching. Specifically, ignoring map constraints, we first maximize the consistency between the similarity measures captured by the kernel matrices of the trajectory and relevant part of the street map. The resulting relaxed assignment is then "rounded" into a hard assignment fulfilling the map constraints. On synthetic and real-world trajectories, we show that kernels methods can be used for map matching and perform well compared to probabilistic methods such as HMMs.
{"title":"Kernelized map matching","authors":"A. Jawad, K. Kersting","doi":"10.1145/1869790.1869860","DOIUrl":"https://doi.org/10.1145/1869790.1869860","url":null,"abstract":"Map matching is a fundamental operation in many applications such as traffic analysis and location-aware services, the killer apps for ubiquitous computing. In past, several map matching approaches have been proposed. Roughly, they can be categorized into four groups: geometric, topological, probabilistic, and other advanced techniques. Surprisingly, kernel methods have not received attention yet although they are very popular in the machine learning community due to their solid mathematical foundation, tendency toward easy geometric interpretation, and strong empirical performance in a wide variety of domains. In this paper, we show how to employ kernels for map matching. Specifically, ignoring map constraints, we first maximize the consistency between the similarity measures captured by the kernel matrices of the trajectory and relevant part of the street map. The resulting relaxed assignment is then \"rounded\" into a hard assignment fulfilling the map constraints. On synthetic and real-world trajectories, we show that kernels methods can be used for map matching and perform well compared to probabilistic methods such as HMMs.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121839265","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 use an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. In this demonstration, we present a prototype system, ESTATE (Exploring Spatial daTa Association patTErns), inverting such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. The system consists of several core components including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. We demonstrate two applications of ESTATE in the domains of ecology and sociology. The ecology application is to discover the associations of between environmental factors and the spatial distribution of biodiversity across the contiguous United States, and the sociology application aims to discover different spatio-social motifs of support for Barack Obama in the 2008 presidential election.
{"title":"Exploring labeled spatial datasets using association analysis","authors":"T. Stepinski, Josue Salazar, W. Ding","doi":"10.1145/1869790.1869882","DOIUrl":"https://doi.org/10.1145/1869790.1869882","url":null,"abstract":"We use an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. In this demonstration, we present a prototype system, ESTATE (Exploring Spatial daTa Association patTErns), inverting such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. The system consists of several core components including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. We demonstrate two applications of ESTATE in the domains of ecology and sociology. The ecology application is to discover the associations of between environmental factors and the spatial distribution of biodiversity across the contiguous United States, and the sociology application aims to discover different spatio-social motifs of support for Barack Obama in the 2008 presidential election.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124994411","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}
Studies in cognitive science have shown that people have different optimization goals in mind for route selection: beyond shortest travel distance (or time), criteria such as smallest number of turns or straightest path are often considered. A common query that a traveller in a foreign city may ask is "where is a facility of type X". When multiple facilities of the same type are available in the nearby area, usually not the nearest neighbor but the one which is easiest to find is preferred for giving instructions by locals, especially in an unfamiliar and complex urban environment. This paper studies a novel type of neighboring object selection problem, taking cognitive complexity of navigation into account. The main difficulty arises from incorporating spatial chunking and landmark information into neighbor comparisons. We propose an algorithm based on network expansion, which uses incremental processing of graph transformation that models instruction complexity. Our approach can efficiently find the easiest-to-reach neighbor with the guaranteed smallest navigation cost. Through experimental evaluation on real road networks, the performance of the proposed algorithm is demonstrated under various settings. Our comparison results reveal that on average the travel distance of the easiest-to-reach neighbor is only 19.3% longer than that of the nearest neighbor, whereas the navigation cost can achieve a 64.8% reduction.
{"title":"Easiest-to-reach neighbor search","authors":"Jie Shao, L. Kulik, E. Tanin","doi":"10.1145/1869790.1869840","DOIUrl":"https://doi.org/10.1145/1869790.1869840","url":null,"abstract":"Studies in cognitive science have shown that people have different optimization goals in mind for route selection: beyond shortest travel distance (or time), criteria such as smallest number of turns or straightest path are often considered. A common query that a traveller in a foreign city may ask is \"where is a facility of type X\". When multiple facilities of the same type are available in the nearby area, usually not the nearest neighbor but the one which is easiest to find is preferred for giving instructions by locals, especially in an unfamiliar and complex urban environment. This paper studies a novel type of neighboring object selection problem, taking cognitive complexity of navigation into account. The main difficulty arises from incorporating spatial chunking and landmark information into neighbor comparisons. We propose an algorithm based on network expansion, which uses incremental processing of graph transformation that models instruction complexity. Our approach can efficiently find the easiest-to-reach neighbor with the guaranteed smallest navigation cost. Through experimental evaluation on real road networks, the performance of the proposed algorithm is demonstrated under various settings. Our comparison results reveal that on average the travel distance of the easiest-to-reach neighbor is only 19.3% longer than that of the nearest neighbor, whereas the navigation cost can achieve a 64.8% reduction.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127273138","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}
Jonathan Muckell, Jeong-Hyon Hwang, C. Lawson, S. Ravi
The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.
{"title":"Algorithms for compressing GPS trajectory data: an empirical evaluation","authors":"Jonathan Muckell, Jeong-Hyon Hwang, C. Lawson, S. Ravi","doi":"10.1145/1869790.1869847","DOIUrl":"https://doi.org/10.1145/1869790.1869847","url":null,"abstract":"The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131024017","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}
Recently, a wide range of applications like hurricane research, fire management, navigation systems, and transportation has shown increasing interest in managing and analyzing space and time-referenced objects, so-called moving objects, that continuously change their positions over time. In the same way as moving objects can change their location over time, the spatial relationships between them can change over time. An important class of spatial relationships are cardinal directions like north and southeast. In spatial databases and GIS, they characterize the relative directional position between static objects in space and are frequently used as selection and join criteria in spatial queries. Transferred to a spatiotemporal context, the simultaneous location change of different moving objects can imply a temporal evolution of their directional relationships, called development. The goal of this paper is to illustrate, explain, and formally define cardinal direction developments between two moving points.
{"title":"Evaluation of cardinal direction developments between moving points","authors":"Tao Chen, Hechen Liu, Markus Schneider","doi":"10.1145/1869790.1869854","DOIUrl":"https://doi.org/10.1145/1869790.1869854","url":null,"abstract":"Recently, a wide range of applications like hurricane research, fire management, navigation systems, and transportation has shown increasing interest in managing and analyzing space and time-referenced objects, so-called moving objects, that continuously change their positions over time. In the same way as moving objects can change their location over time, the spatial relationships between them can change over time. An important class of spatial relationships are cardinal directions like north and southeast. In spatial databases and GIS, they characterize the relative directional position between static objects in space and are frequently used as selection and join criteria in spatial queries. Transferred to a spatiotemporal context, the simultaneous location change of different moving objects can imply a temporal evolution of their directional relationships, called development. The goal of this paper is to illustrate, explain, and formally define cardinal direction developments between two moving points.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125335732","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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