This paper describes Triplex-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. Triplex-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences.
{"title":"A distantly supervised method for extracting spatio-temporal information from text","authors":"Seyed Iman Mirrezaei, Bruno Martins, I. Cruz","doi":"10.1145/2996913.2996967","DOIUrl":"https://doi.org/10.1145/2996913.2996967","url":null,"abstract":"This paper describes Triplex-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. Triplex-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences.","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":"89307439","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}
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.
{"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}
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.
{"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}
Youying Shi, Abdeltawab M. Hendawi, Jayant Gupta, H. Fattah, Mohamed H. Ali
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.
{"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}
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.
{"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}
We study the problem of learning individual route preferences of drivers. Most current route planning services only compute shortest or quickest paths. But many other criteria might play a role for a user to prefer a certain route, as, e.g., fuel consumption, jam likeliness, road conditions, scenicness of the route, turns, allowed maximum speeds, toll costs and many more. Specifying the importance of each criterion manually is a non-trivial, unintuitive and time consuming undertaking for a user. Therefore, we develop approaches that deduce such preferences automatically based on paths previously driven by the user. We present an LP-formulation of the problem making use of a Dijkstra-based separation oracle. The resulting algorithm runs in polynomial time and allows for the user preference computation in few seconds even if several hundred routes are taken into account. Our experiments show that new route suggestions based on these learned preferences reflect the users definition of an optimal route very well.
{"title":"Deducing individual driving preferences for user-aware navigation","authors":"S. Funke, S. Laue, Sabine Storandt","doi":"10.1145/2996913.2997004","DOIUrl":"https://doi.org/10.1145/2996913.2997004","url":null,"abstract":"We study the problem of learning individual route preferences of drivers. Most current route planning services only compute shortest or quickest paths. But many other criteria might play a role for a user to prefer a certain route, as, e.g., fuel consumption, jam likeliness, road conditions, scenicness of the route, turns, allowed maximum speeds, toll costs and many more. Specifying the importance of each criterion manually is a non-trivial, unintuitive and time consuming undertaking for a user. Therefore, we develop approaches that deduce such preferences automatically based on paths previously driven by the user. We present an LP-formulation of the problem making use of a Dijkstra-based separation oracle. The resulting algorithm runs in polynomial time and allows for the user preference computation in few seconds even if several hundred routes are taken into account. Our experiments show that new route suggestions based on these learned preferences reflect the users definition of an optimal route very well.","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":"82719057","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}
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.
{"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}
Personalization is increasingly important for a range of applications that rely on location-based modeling. A key aspect in building personalized models is using population-level information to smooth noisy sparse data at the individual level. In this paper we develop a general mixture model framework for learning individual-level location models where the model adaptively combines different types of smoothing information. In a series of experiments with Twitter geolocation data and Gowalla check-in data we demonstrate that the proposed approach can be significantly more accurate than more traditional smoothing and matrix factorization techniques. The improvement in performance over matrix factorization is pronounced and may be explained by the tendency of dimensionality reduction methods to over-smooth and not retain enough detail at the individual level.
{"title":"Personalized location models with adaptive mixtures","authors":"Moshe Lichman, Dimitrios Kotzias, Padhraic Smyth","doi":"10.1145/2996913.2996953","DOIUrl":"https://doi.org/10.1145/2996913.2996953","url":null,"abstract":"Personalization is increasingly important for a range of applications that rely on location-based modeling. A key aspect in building personalized models is using population-level information to smooth noisy sparse data at the individual level. In this paper we develop a general mixture model framework for learning individual-level location models where the model adaptively combines different types of smoothing information. In a series of experiments with Twitter geolocation data and Gowalla check-in data we demonstrate that the proposed approach can be significantly more accurate than more traditional smoothing and matrix factorization techniques. The improvement in performance over matrix factorization is pronounced and may be explained by the tendency of dimensionality reduction methods to over-smooth and not retain enough detail at the individual level.","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":"82869965","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}
Some analytic queries on road networks, usually concentrating in a local area spanning several cities, need a high-throughput solution such as performing millions of shortest distance computations per second. However, most existing solutions achieve less than 5, 000 shortest distance computations per second per machine even with multi-threads. We demonstrate a solution, termed City Distance Oracles (CDO), using our previously developed ε-distance oracle to achieve as many as 7 million shortest distance computations per second per commodity machine on a city road network, i.e., 10K × 10K origin-distance (OD) matrix can be finished in 14 seconds.
{"title":"CDO: extremely high-throughput road distance computations on city road networks","authors":"Shangfu Peng, H. Samet","doi":"10.1145/2996913.2996921","DOIUrl":"https://doi.org/10.1145/2996913.2996921","url":null,"abstract":"Some analytic queries on road networks, usually concentrating in a local area spanning several cities, need a high-throughput solution such as performing millions of shortest distance computations per second. However, most existing solutions achieve less than 5, 000 shortest distance computations per second per machine even with multi-threads. We demonstrate a solution, termed City Distance Oracles (CDO), using our previously developed ε-distance oracle to achieve as many as 7 million shortest distance computations per second per commodity machine on a city road network, i.e., 10K × 10K origin-distance (OD) matrix can be finished in 14 seconds.","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":"76111647","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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