The ubiquity of mobile location aware devices and the proliferation of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influential LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence propagation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more restrictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method.
{"title":"Regionally influential users in location-aware social networks","authors":"Panagiotis Bouros, Dimitris Sacharidis, Nikos Bikakis","doi":"10.1145/2666310.2666489","DOIUrl":"https://doi.org/10.1145/2666310.2666489","url":null,"abstract":"The ubiquity of mobile location aware devices and the proliferation of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influential LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence propagation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more restrictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121207683","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 this paper, we introduce obstructed group nearest neighbor (OGNN) queries, that enable a group to meet at a point of interest (e.g., a restaurant) with the minimum aggregate travel distance in an obstructed space. In recent years, researchers have focused on developing algorithms for processing GNN queries in the Euclidean space and road networks, which ignore the impact of obstacles such as buildings and lakes in computing distances. We propose the first comprehensive approach to process an OGNN query. We present an efficient algorithm to compute aggregate obstructed distances, which is an essential component for processing OGNN queries. We exploit geometric properties to develop pruning techniques that reduce the search space and incur less processing overhead. We validate the efficacy and efficiency of our solution through extensive experiments using both real and synthetic datasets.
{"title":"Group nearest neighbor queries in the presence of obstacles","authors":"Nusrat Sultana, T. Hashem, L. Kulik","doi":"10.1145/2666310.2666484","DOIUrl":"https://doi.org/10.1145/2666310.2666484","url":null,"abstract":"In this paper, we introduce obstructed group nearest neighbor (OGNN) queries, that enable a group to meet at a point of interest (e.g., a restaurant) with the minimum aggregate travel distance in an obstructed space. In recent years, researchers have focused on developing algorithms for processing GNN queries in the Euclidean space and road networks, which ignore the impact of obstacles such as buildings and lakes in computing distances. We propose the first comprehensive approach to process an OGNN query. We present an efficient algorithm to compute aggregate obstructed distances, which is an essential component for processing OGNN queries. We exploit geometric properties to develop pruning techniques that reduce the search space and incur less processing overhead. We validate the efficacy and efficiency of our solution through extensive experiments using both real and synthetic datasets.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225954","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}
Quality control for near-real-time spatial-temporal data is often presented from the perspective of the original owner and provider of the data, and focuses on general techniques for outlier detection or uses domain-specific knowledge and rules to assess quality. The impact of quality control on the data aggregator and redistributor is neglected. The focus of this paper is to define and demonstrate quality control measures for real-time, spatial-temporal data from the perspective of the aggregator to provide tools for assessment and optimization of system operation and data redistribution. We define simple measures that account for temporal completeness and spatial coverage. The measures and methods developed are tested on real-world data and applications.
{"title":"Quality control from the perspective of a near-real-time, spatial-temporal data aggregator and (re)distributor","authors":"D. Galarus, R. Angryk","doi":"10.1145/2666310.2666426","DOIUrl":"https://doi.org/10.1145/2666310.2666426","url":null,"abstract":"Quality control for near-real-time spatial-temporal data is often presented from the perspective of the original owner and provider of the data, and focuses on general techniques for outlier detection or uses domain-specific knowledge and rules to assess quality. The impact of quality control on the data aggregator and redistributor is neglected. The focus of this paper is to define and demonstrate quality control measures for real-time, spatial-temporal data from the perspective of the aggregator to provide tools for assessment and optimization of system operation and data redistribution. We define simple measures that account for temporal completeness and spatial coverage. The measures and methods developed are tested on real-world data and applications.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"4 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128765058","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 queries are widely used in many data mining and analytics applications. However, a huge and growing size of spatial data makes it challenging to process the spatial queries efficiently. In this paper we present a lightweight and scalable spatial index for big data stored in distributed storage systems. Experimental results show the efficiency and effectiveness of our spatial indexing technique for different spatial queries.
{"title":"Efficient spatial query processing for big data","authors":"Kisung Lee, R. Ganti, M. Srivatsa, Ling Liu","doi":"10.1145/2666310.2666481","DOIUrl":"https://doi.org/10.1145/2666310.2666481","url":null,"abstract":"Spatial queries are widely used in many data mining and analytics applications. However, a huge and growing size of spatial data makes it challenging to process the spatial queries efficiently. In this paper we present a lightweight and scalable spatial index for big data stored in distributed storage systems. Experimental results show the efficiency and effectiveness of our spatial indexing technique for different spatial queries.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130503493","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 work is motivated by a real-life application that exploits sensor data available from traffic light control systems currently deployed in many cities around the world. Each sensor consists of an induction loop that generates a stream of events triggered whenever a metallic object e.g. car, bus, or a bicycle, is detected above the sensor. Because of the red phase of traffic lights objects are usually divided into groups that move together. Detecting these groups of objects as long as they pass through the sensor is useful for estimating the status of the toad networks such as car queue length or detecting traffic anomalies. In this work, given a data stream that contains observations of an event, e.g. detection of a moving object, together with the timestamps indicating when the events happen, we study the problem that clusters the events together in real-time based on the proximity of the event's occurrence time. We propose an efficient real-time algorithm that scales up to the large data streams extracted from thousands of sensors in the city of London. Moreover, our algorithm is better than the baseline algorithms in terms of clustering accuracy. We demonstrate motivations of the work by showing a real-life use-case in which clustering results are used for estimating the car queue lengths on the road and detecting traffic anomalies.
{"title":"Online event clustering in temporal dimension","authors":"Hoang Thanh Lam, E. Bouillet","doi":"10.1145/2666310.2666393","DOIUrl":"https://doi.org/10.1145/2666310.2666393","url":null,"abstract":"This work is motivated by a real-life application that exploits sensor data available from traffic light control systems currently deployed in many cities around the world. Each sensor consists of an induction loop that generates a stream of events triggered whenever a metallic object e.g. car, bus, or a bicycle, is detected above the sensor. Because of the red phase of traffic lights objects are usually divided into groups that move together. Detecting these groups of objects as long as they pass through the sensor is useful for estimating the status of the toad networks such as car queue length or detecting traffic anomalies. In this work, given a data stream that contains observations of an event, e.g. detection of a moving object, together with the timestamps indicating when the events happen, we study the problem that clusters the events together in real-time based on the proximity of the event's occurrence time. We propose an efficient real-time algorithm that scales up to the large data streams extracted from thousands of sensors in the city of London. Moreover, our algorithm is better than the baseline algorithms in terms of clustering accuracy. We demonstrate motivations of the work by showing a real-life use-case in which clustering results are used for estimating the car queue lengths on the road and detecting traffic anomalies.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825091","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}
Riccardo Fellegara, F. Iuricich, L. Floriani, K. Weiss
We consider the problem of efficient computing and simplifying Morse complexes on a Triangulated Irregular Network (TIN) based on discrete Morse theory. We develop a compact encoding for the discrete Morse gradient field, defined by the terrain elevation, by attaching it to the triangles of the TIN. This encoding is suitable to be combined with any TIN data structure storing just its vertices and triangles. We show how to compute such gradient field from the elevation values given at the TIN vertices, and how to simplify it effectively in order to reduce the number of critical elements. We demonstrate the effectiveness and scalability of our approach over large terrains by developing algorithms for extracting the cells of the Morse complexes as well as the graph joining the critical elements from the discrete gradient field. We compare implementations of our approach on a widely-used and compact adjacency-based topological data structure for a TIN and on a compact spatio-topological data structure that we have recently developed, the PR-star quadtree.
{"title":"Efficient computation and simplification of discrete morse decompositions on triangulated terrains","authors":"Riccardo Fellegara, F. Iuricich, L. Floriani, K. Weiss","doi":"10.1145/2666310.2666412","DOIUrl":"https://doi.org/10.1145/2666310.2666412","url":null,"abstract":"We consider the problem of efficient computing and simplifying Morse complexes on a Triangulated Irregular Network (TIN) based on discrete Morse theory. We develop a compact encoding for the discrete Morse gradient field, defined by the terrain elevation, by attaching it to the triangles of the TIN. This encoding is suitable to be combined with any TIN data structure storing just its vertices and triangles. We show how to compute such gradient field from the elevation values given at the TIN vertices, and how to simplify it effectively in order to reduce the number of critical elements. We demonstrate the effectiveness and scalability of our approach over large terrains by developing algorithms for extracting the cells of the Morse complexes as well as the graph joining the critical elements from the discrete gradient field. We compare implementations of our approach on a widely-used and compact adjacency-based topological data structure for a TIN and on a compact spatio-topological data structure that we have recently developed, the PR-star quadtree.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131979305","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, there is a rapid growth in the use of microblogs, such as Twitter, and of social networks, such as Instagram, to publish geo-tagged posts that indicate the location of the user at the time when the message is sent. This provides abundant geospatial data that can be analyzed to understand the behavior of masses of people, in particular in urban. Such analysis can improve and facilitate the work of urban planners and of policy makers, e.g., when deciding where to add transportation routes or public institutes. In this demonstration, we present a system that utilizes geo-tagged posts to discover places that were jointly visited by many people. We present the management and the analysis of the data, to illustrate the feasibility of the approach and to indicate new research questions in this domain.
{"title":"City nexus: discovering pairs of jointly-visited locations based on geo-tagged posts in social networks","authors":"Y. Kanza, Elad Kravi, Uri Motchan","doi":"10.1145/2666310.2666378","DOIUrl":"https://doi.org/10.1145/2666310.2666378","url":null,"abstract":"Recently, there is a rapid growth in the use of microblogs, such as Twitter, and of social networks, such as Instagram, to publish geo-tagged posts that indicate the location of the user at the time when the message is sent. This provides abundant geospatial data that can be analyzed to understand the behavior of masses of people, in particular in urban. Such analysis can improve and facilitate the work of urban planners and of policy makers, e.g., when deciding where to add transportation routes or public institutes. In this demonstration, we present a system that utilizes geo-tagged posts to discover places that were jointly visited by many people. We present the management and the analysis of the data, to illustrate the feasibility of the approach and to indicate new research questions in this domain.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121245752","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 this paper we present a time-aware, density-based clustering technique for the identification of stay regions in trajectories of low-sampling-rate GPS points, and its application to the study of animal migrations. A stay region is defined as a portion of space which generally does not designate a precise geographical entity and where an object is significantly present for a period of time, in spite of relatively short periods of absence. Stay regions can delimit for example the residence of animals, i.e. the home-range. The proposed technique enables the extraction of stay regions represented by dense and temporally disjoint sub-trajectories, through the specification of a small set of parameters related to density and presence. While this work takes inspiration from the field of animal ecology, we argue that the approach can be of more general concern and used in perspective in different domains, e.g. the study of human mobility over large temporal scales. We experiment with the approach on a case study, regarding the seasonal migration of a group of roe deer.
{"title":"Extracting stay regions with uncertain boundaries from GPS trajectories: a case study in animal ecology","authors":"M. Damiani, H. Issa, F. Cagnacci","doi":"10.1145/2666310.2666417","DOIUrl":"https://doi.org/10.1145/2666310.2666417","url":null,"abstract":"In this paper we present a time-aware, density-based clustering technique for the identification of stay regions in trajectories of low-sampling-rate GPS points, and its application to the study of animal migrations. A stay region is defined as a portion of space which generally does not designate a precise geographical entity and where an object is significantly present for a period of time, in spite of relatively short periods of absence. Stay regions can delimit for example the residence of animals, i.e. the home-range. The proposed technique enables the extraction of stay regions represented by dense and temporally disjoint sub-trajectories, through the specification of a small set of parameters related to density and presence. While this work takes inspiration from the field of animal ecology, we argue that the approach can be of more general concern and used in perspective in different domains, e.g. the study of human mobility over large temporal scales. We experiment with the approach on a case study, regarding the seasonal migration of a group of roe deer.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147569","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}
Sander P. A. Alewijnse, K. Buchin, M. Buchin, A. Kölzsch, H. Kruckenberg, M. A. Westenberg
We present an algorithmic framework for criteria-based segmentation of trajectories that can efficiently process a large class of criteria. Criteria-based segmentation is the problem of subdividing a trajectory into a small number of parts such that each part satisfies a global criterion. Our framework can handle criteria that are stable, in the sense that these do not change their validity along the trajectory very often. This includes both increasing and decreasing monotone criteria. Our framework takes O(n log n) time for preprocessing and computation, where n is the number of data points. It surpasses the two previous algorithmic frameworks on criteria-based segmentation, which could only handle decreasing monotone criteria, or had a quadratic running time, respectively. Furthermore, we develop an efficient data structure for interactive parameter selection, and provide mechanisms to improve the exact position of break points in the segmentation. We demonstrate and evaluate our framework by performing case studies on real-world data sets.
{"title":"A framework for trajectory segmentation by stable criteria","authors":"Sander P. A. Alewijnse, K. Buchin, M. Buchin, A. Kölzsch, H. Kruckenberg, M. A. Westenberg","doi":"10.1145/2666310.2666415","DOIUrl":"https://doi.org/10.1145/2666310.2666415","url":null,"abstract":"We present an algorithmic framework for criteria-based segmentation of trajectories that can efficiently process a large class of criteria. Criteria-based segmentation is the problem of subdividing a trajectory into a small number of parts such that each part satisfies a global criterion. Our framework can handle criteria that are stable, in the sense that these do not change their validity along the trajectory very often. This includes both increasing and decreasing monotone criteria. Our framework takes O(n log n) time for preprocessing and computation, where n is the number of data points. It surpasses the two previous algorithmic frameworks on criteria-based segmentation, which could only handle decreasing monotone criteria, or had a quadratic running time, respectively. Furthermore, we develop an efficient data structure for interactive parameter selection, and provide mechanisms to improve the exact position of break points in the segmentation. We demonstrate and evaluate our framework by performing case studies on real-world data sets.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115523567","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 the proliferation of Internet-connected, location-aware mobile devices, such as smartphones, we are also witnessing a proliferation and increased use of map-based services that serve information about relevant Points of Interest (PoIs) to their users. We provide an efficient and practical foundation for the processing of queries that take a keyword and a spatial region as arguments and return the k most relevant PoIs that belong to the region, which may be the part of the map covered by the user's screen. The paper proposes a novel technique that encodes the spatio-textual part of a PoI as a compact bit string. This technique extends an existing spatial encoding to also encode the textual aspect of a PoI in compressed form. The resulting bit strings may then be indexed using index structures such as B-trees or hashing that are standard in DBMSs and key-value stores. As a result, it is straightforward to support the proposed functionality using existing data management systems. The paper also proposes a novel top-k query algorithm that merges partial results while providing an exact result. An empirical study with real-world data indicates that the proposed techniques enable excellent indexing and query execution performance on a standard DBMS.
{"title":"Top-k point of interest retrieval using standard indexes","authors":"Anders Skovsgaard, Christian S. Jensen","doi":"10.1145/2666310.2666399","DOIUrl":"https://doi.org/10.1145/2666310.2666399","url":null,"abstract":"With the proliferation of Internet-connected, location-aware mobile devices, such as smartphones, we are also witnessing a proliferation and increased use of map-based services that serve information about relevant Points of Interest (PoIs) to their users. We provide an efficient and practical foundation for the processing of queries that take a keyword and a spatial region as arguments and return the k most relevant PoIs that belong to the region, which may be the part of the map covered by the user's screen. The paper proposes a novel technique that encodes the spatio-textual part of a PoI as a compact bit string. This technique extends an existing spatial encoding to also encode the textual aspect of a PoI in compressed form. The resulting bit strings may then be indexed using index structures such as B-trees or hashing that are standard in DBMSs and key-value stores. As a result, it is straightforward to support the proposed functionality using existing data management systems. The paper also proposes a novel top-k query algorithm that merges partial results while providing an exact result. An empirical study with real-world data indicates that the proposed techniques enable excellent indexing and query execution performance on a standard DBMS.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398305","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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