With the ubiquity of spatial data, vertexes or edges in graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018, the Wikidata kn...
{"title":"Riso-Tree: An Efficient and Scalable Index for Spatial Entities in Graph Database Management Systems","authors":"Yuhan Sun, Mohamed Sarwat","doi":"10.1145/3450945","DOIUrl":"https://doi.org/10.1145/3450945","url":null,"abstract":"With the ubiquity of spatial data, vertexes or edges in graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018, the Wikidata kn...","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128559722","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}
Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollu...
利用不同的空间插值技术可以估计自然现象数据的空间分布。这些技术可用于治理城市噪声污染。
{"title":"Spatial Interpolation Techniques on Participatory Sensing Data","authors":"Asif Iqbal Middya, Sarbani Roy","doi":"10.1145/3457609","DOIUrl":"https://doi.org/10.1145/3457609","url":null,"abstract":"Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollu...","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919960","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}
Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.
{"title":"Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure","authors":"Sunanda Bose, Sujay Paul, N. Mukherjee","doi":"10.1145/3446936","DOIUrl":"https://doi.org/10.1145/3446936","url":null,"abstract":"Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116182665","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}
Christodoulos Efstathiades, Alexandros Efentakis, D. Pfoser
Novel Web technologies and resulting applications have led to a participatory data ecosystem that, when utilized properly, will lead to more rewarding services. In this work, we investigate the case of Location-Based Services, specifically how to improve the typical location-based Point-of-Interest (POI) request processed as a k-Nearest-Neighbor query. This work introduces Links-of-Interest (LOI) between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of an LOI, that is, how frequently the respective POI pair was visited and is mentioned in the same context. Our contribution is a query-processing method for so-called k-Relevant Nearest Neighbor (k-RNN) queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant (as judged by the crowd) POIs. Our method is based on intelligently combining indices for spatial data (a spatial grid) and for relevance data (a graph) during query processing. Using landmarks as a means to prune the search space in the Relevance Graph, we improve the proposed methods. Using in addition A*-directed search, the query performance can be further improved. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k-RNN problem.
{"title":"Efficient Processing of Relevant Nearest-Neighbor Queries","authors":"Christodoulos Efstathiades, Alexandros Efentakis, D. Pfoser","doi":"10.1145/2934675","DOIUrl":"https://doi.org/10.1145/2934675","url":null,"abstract":"Novel Web technologies and resulting applications have led to a participatory data ecosystem that, when utilized properly, will lead to more rewarding services. In this work, we investigate the case of Location-Based Services, specifically how to improve the typical location-based Point-of-Interest (POI) request processed as a k-Nearest-Neighbor query. This work introduces Links-of-Interest (LOI) between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of an LOI, that is, how frequently the respective POI pair was visited and is mentioned in the same context. Our contribution is a query-processing method for so-called k-Relevant Nearest Neighbor (k-RNN) queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant (as judged by the crowd) POIs. Our method is based on intelligently combining indices for spatial data (a spatial grid) and for relevance data (a graph) during query processing. Using landmarks as a means to prune the search space in the Relevance Graph, we improve the proposed methods. Using in addition A*-directed search, the query performance can be further improved. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k-RNN problem.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969337","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}
K. Pillai, R. Angryk, J. Banda, Dustin J. Kempton, Berkay Aydin, P. Martens
Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.
{"title":"Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations","authors":"K. Pillai, R. Angryk, J. Banda, Dustin J. Kempton, Berkay Aydin, P. Martens","doi":"10.1145/2936775","DOIUrl":"https://doi.org/10.1145/2936775","url":null,"abstract":"Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140210","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}
N. Pelekis, Stylianos Sideridis, Panagiotis Tampakis, Y. Theodoridis
During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolisby-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.
{"title":"Simulating Our LifeSteps by Example","authors":"N. Pelekis, Stylianos Sideridis, Panagiotis Tampakis, Y. Theodoridis","doi":"10.1145/2937753","DOIUrl":"https://doi.org/10.1145/2937753","url":null,"abstract":"During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolisby-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122918106","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}
Due to several important features, such as high performance, low power consumption, and shock resistance, NAND flash has become a very popular stable storage medium for embedded mobile devices, personal computers, and even enterprise servers. However, the peculiar characteristics of flash memory require redesigning the existing data storage and indexing techniques that were devised for magnetic hard disks. In this article, we propose TRIFL, an efficient and generic TRajectory Index for FLash. TRIFL is designed around the key requirements of trajectory indexing and flash storage. TRIFL is generic in the sense that it is efficient for both simple flash storage devices such as SD cards and more powerful devices such as solid state drives. In addition, TRIFL is supplied with an online self-tuning algorithm that allows adapting the index structure to the workload and the technical specifications of the flash storage device to maximize the index performance. Moreover, TRIFL achieves good performance with relatively low memory requirements, which makes the index appropriate for many application scenarios. The experimental evaluation shows that TRIFL outperforms the representative indexing methods on magnetic disks and flash disks.
{"title":"TRIFL: A Generic Trajectory Index for Flash Storage","authors":"Dai Hai Ton That, I. S. Popa, K. Zeitouni","doi":"10.1145/2786758","DOIUrl":"https://doi.org/10.1145/2786758","url":null,"abstract":"Due to several important features, such as high performance, low power consumption, and shock resistance, NAND flash has become a very popular stable storage medium for embedded mobile devices, personal computers, and even enterprise servers. However, the peculiar characteristics of flash memory require redesigning the existing data storage and indexing techniques that were devised for magnetic hard disks.\u0000 In this article, we propose TRIFL, an efficient and generic TRajectory Index for FLash. TRIFL is designed around the key requirements of trajectory indexing and flash storage. TRIFL is generic in the sense that it is efficient for both simple flash storage devices such as SD cards and more powerful devices such as solid state drives. In addition, TRIFL is supplied with an online self-tuning algorithm that allows adapting the index structure to the workload and the technical specifications of the flash storage device to maximize the index performance. Moreover, TRIFL achieves good performance with relatively low memory requirements, which makes the index appropriate for many application scenarios. The experimental evaluation shows that TRIFL outperforms the representative indexing methods on magnetic disks and flash disks.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125700657","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}
Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering “semantics” from the raw geometric trajectories by relating them to the spatial environment or finding patterns, for example, by data mining techniques. A question is how the resulting “meaningful” trajectories can be represented or further queried. In this article, we propose a systematic study of annotated trajectory databases. We define a very simple generic model called symbolic trajectory to capture a wide range of meanings derived from a geometric trajectory. Essentially, a symbolic trajectory is just a time-dependent label; variants have sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well-established framework of data types and operations for moving objects. Symbolic trajectories can represent, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories. Besides the model, the main technical contribution of the article is a language for pattern matching and rewriting of symbolic trajectories. A symbolic trajectory can be represented as a sequence of pairs (called units) consisting of a time interval and a label. A pattern consists of unit patterns (specifications for time interval and/or label) and wildcards, matching units and sequences of units, respectively, and regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment, which makes the language extensible and quite powerful. We formally define the data model and syntax and semantics of the pattern language. Query operations are offered to integrate pattern matching, rewriting, and classification of symbolic trajectories into a DBMS querying environment. Implementation of the model using finite state machines is described in detail. An experimental evaluation demonstrates the efficiency of the implementation. In particular, it shows dramatic improvements in storage space and response time in a comparison of symbolic and geometric trajectories for some simple queries that can be executed on both symbolic and raw trajectories.
{"title":"Symbolic Trajectories","authors":"R. H. Güting, Fabio Valdés, M. Damiani","doi":"10.1145/2786756","DOIUrl":"https://doi.org/10.1145/2786756","url":null,"abstract":"Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering “semantics” from the raw geometric trajectories by relating them to the spatial environment or finding patterns, for example, by data mining techniques. A question is how the resulting “meaningful” trajectories can be represented or further queried.\u0000 In this article, we propose a systematic study of annotated trajectory databases. We define a very simple generic model called symbolic trajectory to capture a wide range of meanings derived from a geometric trajectory. Essentially, a symbolic trajectory is just a time-dependent label; variants have sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well-established framework of data types and operations for moving objects. Symbolic trajectories can represent, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories.\u0000 Besides the model, the main technical contribution of the article is a language for pattern matching and rewriting of symbolic trajectories. A symbolic trajectory can be represented as a sequence of pairs (called units) consisting of a time interval and a label. A pattern consists of unit patterns (specifications for time interval and/or label) and wildcards, matching units and sequences of units, respectively, and regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment, which makes the language extensible and quite powerful.\u0000 We formally define the data model and syntax and semantics of the pattern language. Query operations are offered to integrate pattern matching, rewriting, and classification of symbolic trajectories into a DBMS querying environment. Implementation of the model using finite state machines is described in detail. An experimental evaluation demonstrates the efficiency of the implementation. In particular, it shows dramatic improvements in storage space and response time in a comparison of symbolic and geometric trajectories for some simple queries that can be executed on both symbolic and raw trajectories.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130884954","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}
Geo-fencing, as a spatial join between points (moving objects) and polygons (spatial range), is widely used in emerging location-based services to trigger context-aware events. It faces the challenge of real-time processing a large number of time-variant complex polygons, when points are constantly moving. Following the filter-and-refine policy, in our previous work, we proposed to organize edges per polygon in hash tables to improve the performance of the refining stage. The number of edges, however, is uneven among buckets. As a result, some points that happen to match big buckets with many edges will have much longer responses than usual. In this article, we solve this problem from two aspects: (i) Constructing multiple parallel hash tables and dynamically selecting the bucket with fewest edges and (ii) sorting edges in a bucket so as to realize the crossing number algorithm by binary search. We further combine the two to suggest a hybrid hashing scheme that takes a better tradeoff between real-time pairing points with polygons and system overhead of building hash tables. Extensive analyses and evaluations on two real-world datasets confirm that the proposed scheme can effectively reduce the pairing time in terms of both the average and distribution.
{"title":"Efficient Geo-Fencing via Hybrid Hashing: A Combination of Bucket Selection and In-Bucket Binary Search","authors":"Suhua Tang, Yi Yu, Roger Zimmermann, S. Obana","doi":"10.1145/2774219","DOIUrl":"https://doi.org/10.1145/2774219","url":null,"abstract":"Geo-fencing, as a spatial join between points (moving objects) and polygons (spatial range), is widely used in emerging location-based services to trigger context-aware events. It faces the challenge of real-time processing a large number of time-variant complex polygons, when points are constantly moving. Following the filter-and-refine policy, in our previous work, we proposed to organize edges per polygon in hash tables to improve the performance of the refining stage. The number of edges, however, is uneven among buckets. As a result, some points that happen to match big buckets with many edges will have much longer responses than usual. In this article, we solve this problem from two aspects: (i) Constructing multiple parallel hash tables and dynamically selecting the bucket with fewest edges and (ii) sorting edges in a bucket so as to realize the crossing number algorithm by binary search. We further combine the two to suggest a hybrid hashing scheme that takes a better tradeoff between real-time pairing points with polygons and system overhead of building hash tables. Extensive analyses and evaluations on two real-world datasets confirm that the proposed scheme can effectively reduce the pairing time in terms of both the average and distribution.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256564","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}
Accumulated cost surfaces are used in a variety of fields that employ spatial analysis. Several algorithms have been suggested in the past for solving them efficiently or with minimal errors. Meanwhile, a new wave on the technological frontier has brought about general-purpose computing on GPUs. In this article, we describe how accumulated cost surfaces can be solved with CUDA. To verify the performance of our solution, we performed an experimental comparison against implementations run on a CPU. Our results with realistic cost models indicate that the move to GPUs can engender a speed-up of an order of magnitude.
{"title":"Tilewise Accumulated Cost Surface Computation with Graphics Processing Units","authors":"J. Kovanen, T. Sarjakoski","doi":"10.1145/2803172","DOIUrl":"https://doi.org/10.1145/2803172","url":null,"abstract":"Accumulated cost surfaces are used in a variety of fields that employ spatial analysis. Several algorithms have been suggested in the past for solving them efficiently or with minimal errors. Meanwhile, a new wave on the technological frontier has brought about general-purpose computing on GPUs. In this article, we describe how accumulated cost surfaces can be solved with CUDA. To verify the performance of our solution, we performed an experimental comparison against implementations run on a CPU. Our results with realistic cost models indicate that the move to GPUs can engender a speed-up of an order of magnitude.","PeriodicalId":202328,"journal":{"name":"ACM Trans. Spatial Algorithms Syst.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138721","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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