This paper introduces a qualitative reasoning model for the representation of the trajectory of a moving point with respect to a region. The approach is based on a formal model of topological relations between a directed line and a region in a two-dimensional space. The approach is flexible enough to qualify possible movements according to several topological properties such as the dimension and cardinality of the intersections between a directed line and a region. We introduce the notion of conceptual transition that favors the exploration of possible trajectories in the case of incomplete knowledge configurations. A composition of DL-RE topological relations supports the derivation of complex movement patterns. The whole approach is experimented by a prototype development and applied to a large maritime trajectory database.
{"title":"Modelling movement patterns using topological relations between a directed line and a region","authors":"Jing Wu, Christophe Claramunt, M. Deng","doi":"10.1145/2676552.2676559","DOIUrl":"https://doi.org/10.1145/2676552.2676559","url":null,"abstract":"This paper introduces a qualitative reasoning model for the representation of the trajectory of a moving point with respect to a region. The approach is based on a formal model of topological relations between a directed line and a region in a two-dimensional space. The approach is flexible enough to qualify possible movements according to several topological properties such as the dimension and cardinality of the intersections between a directed line and a region. We introduce the notion of conceptual transition that favors the exploration of possible trajectories in the case of incomplete knowledge configurations. A composition of DL-RE topological relations supports the derivation of complex movement patterns. The whole approach is experimented by a prototype development and applied to a large maritime trajectory database.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"193 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":"114862618","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}
David L. White, R. Pargas, A. Chow, J. Chong, Michelle Cook, Irfan Tak
Fireflies are a unique part of the natural landscape at a global scale. Urban development and changes in the landscape can negatively affect firefly distribution and abundance. Assessment of firefly abundance through counts of bioluminescence flashes provides an environmental quality indicator that can be easily observed and quantified by citizen scientists. Researchers at Clemson University, collaborating with resources managers, educators and teachers initiated the Vanishing Firefly Project to engage citizen scientists with the following goals: (1) Science Inquiry-Engage citizens in scientific practices to understand the impacts of urbanization on environmental quality; (2) Service Learning-Increase the skill of citizens in making critical, scientific and informed decisions through community and service activities; (3) Sustainability-Protect natural habitats through effective land and resource management practices and (4) Stewardship-Provide opportunities for citizens to participate in environmental and sustainability studies and activities. The project began in 2010, and was initially a Field Day located in Georgetown, South Carolina, USA. Since then, the project has grown from a single day event, to a statewide field survey, and now a global event in 2014. The 2010 efforts were local and to realize our goals would require increasing citizen science participation from one location in South Carolina to a regional scale. Several issues were to be addressed that varied from technology development, data quality and management, citizen scientist training and motivation for volunteers. Our initial technology framework consisted of a single Google Docs webform that allowed users to submit their firefly counts, but we had no ability to engage volunteers during and after the initial submission. The technology framework at this time (2014) now consists of an iOS app, Android app and a webform that submit firefly counts, firefly behavior, ambient light measurements (iOS and Android app only) and habitat type to a real-time reporting and geospatial data management system. Our efforts have leveraged social media platforms including Facebook, Twitter and YouTube to support training, education and engagement. This paper describes project activities focusing on how our technology framework has developed and matured to increase the scope, reach and capability of citizen scientists participating in the Vanishing Firefly Project.
{"title":"The vanishing firefly project: engaging citizen scientists with a mobile technology and real-time reporting framework","authors":"David L. White, R. Pargas, A. Chow, J. Chong, Michelle Cook, Irfan Tak","doi":"10.1145/2676552.2676563","DOIUrl":"https://doi.org/10.1145/2676552.2676563","url":null,"abstract":"Fireflies are a unique part of the natural landscape at a global scale. Urban development and changes in the landscape can negatively affect firefly distribution and abundance. Assessment of firefly abundance through counts of bioluminescence flashes provides an environmental quality indicator that can be easily observed and quantified by citizen scientists. Researchers at Clemson University, collaborating with resources managers, educators and teachers initiated the Vanishing Firefly Project to engage citizen scientists with the following goals: (1) Science Inquiry-Engage citizens in scientific practices to understand the impacts of urbanization on environmental quality; (2) Service Learning-Increase the skill of citizens in making critical, scientific and informed decisions through community and service activities; (3) Sustainability-Protect natural habitats through effective land and resource management practices and (4) Stewardship-Provide opportunities for citizens to participate in environmental and sustainability studies and activities. The project began in 2010, and was initially a Field Day located in Georgetown, South Carolina, USA. Since then, the project has grown from a single day event, to a statewide field survey, and now a global event in 2014. The 2010 efforts were local and to realize our goals would require increasing citizen science participation from one location in South Carolina to a regional scale. Several issues were to be addressed that varied from technology development, data quality and management, citizen scientist training and motivation for volunteers. Our initial technology framework consisted of a single Google Docs webform that allowed users to submit their firefly counts, but we had no ability to engage volunteers during and after the initial submission. The technology framework at this time (2014) now consists of an iOS app, Android app and a webform that submit firefly counts, firefly behavior, ambient light measurements (iOS and Android app only) and habitat type to a real-time reporting and geospatial data management system. Our efforts have leveraged social media platforms including Facebook, Twitter and YouTube to support training, education and engagement. This paper describes project activities focusing on how our technology framework has developed and matured to increase the scope, reach and capability of citizen scientists participating in the Vanishing Firefly Project.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","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":"126505313","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}
Mark McKenney, Sarita C. Viswanadham, Elizabeth Littman
Many natural phenomena can be nicely represented by concepts of moving regions. For example, hurricanes, rain clouds, pollution zones, etc., change shape and position over time. Current models of moving regions have proven to be difficult to translate effectively to implementation for two reasons: i) algorithms for operations, such as intersection, are difficult to implement, and ii) creating instances of moving regions from data sources is difficult. In this paper, we create a new model of moving regions at the abstract, discrete, and implementation levels that overcome the difficulties of previous models. The CMR Model aligns well with data collection techniques, can be implemented easily, and allows complex movement patterns to be easily depicted.
{"title":"The CMR model of moving regions","authors":"Mark McKenney, Sarita C. Viswanadham, Elizabeth Littman","doi":"10.1145/2676552.2676564","DOIUrl":"https://doi.org/10.1145/2676552.2676564","url":null,"abstract":"Many natural phenomena can be nicely represented by concepts of moving regions. For example, hurricanes, rain clouds, pollution zones, etc., change shape and position over time. Current models of moving regions have proven to be difficult to translate effectively to implementation for two reasons: i) algorithms for operations, such as intersection, are difficult to implement, and ii) creating instances of moving regions from data sources is difficult. In this paper, we create a new model of moving regions at the abstract, discrete, and implementation levels that overcome the difficulties of previous models. The CMR Model aligns well with data collection techniques, can be implemented easily, and allows complex movement patterns to be easily depicted.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","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":"131312016","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}
Amruta Khot, Abdeltawab M. Hendawi, A. Nascimento, R. Katti, A. Teredesai, Mohamed H. Ali
The storage and manipulation of road network graphs are critical to navigational and location-based services. The widespread use of GPS devices combined with low-cost storage has enabled portable and embedded systems to handle several spatiotemporal operations against a natively-stored version of the road network graph. However, the increase in amount of map detail data over the years poses several challenges for such systems. In this paper, we highlight the need for adoption of road network compression techniques in embedded geographic information systems. We also provide a technical overview of proposed road network compression techniques.
{"title":"Road network compression techniques in spatiotemporal embedded systems: a survey","authors":"Amruta Khot, Abdeltawab M. Hendawi, A. Nascimento, R. Katti, A. Teredesai, Mohamed H. Ali","doi":"10.1145/2676552.2676645","DOIUrl":"https://doi.org/10.1145/2676552.2676645","url":null,"abstract":"The storage and manipulation of road network graphs are critical to navigational and location-based services. The widespread use of GPS devices combined with low-cost storage has enabled portable and embedded systems to handle several spatiotemporal operations against a natively-stored version of the road network graph. However, the increase in amount of map detail data over the years poses several challenges for such systems. In this paper, we highlight the need for adoption of road network compression techniques in embedded geographic information systems. We also provide a technical overview of proposed road network compression techniques.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"30 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":"125522338","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}
Kruthika Rathinavel, G. Dixit, M. Matarazzo, Chang-Tien Lu
The emergence of internet advertising, email marketing and social networking has given rise to a new world of digital advertising used by stores and consumers alike. While retailers aim to promote all types of products, consumers also want to share this information via social media. This paper presents Shopaholic, a system that leverages social media to provide information on trending deals and store sales in any given location. It is intended to help shoppers identify great deals from the vast amounts of data scattered among social networks. Personalized search results, visualization of trends and sentiment analysis provided by Shopaholic allow the user to identify optimal deals. The application accounts for spatial and temporal data via a customized ranking algorithm and features integration with Twitter so that the user can share his or her actual experience using a deal. Ultimately, the system gives back to the shopping community by allowing users to share their experiences and evaluations of deals. A recommendation algorithm uniquely identifies the user's tastes, shopping history and current location to provide deal suggestions, thereby integrating temporal and spatial entities in recommendations.
{"title":"Shopaholic: a crowd-sourced spatio-temporal product-deals evaluation system (demo paper)","authors":"Kruthika Rathinavel, G. Dixit, M. Matarazzo, Chang-Tien Lu","doi":"10.1145/2676552.2676558","DOIUrl":"https://doi.org/10.1145/2676552.2676558","url":null,"abstract":"The emergence of internet advertising, email marketing and social networking has given rise to a new world of digital advertising used by stores and consumers alike. While retailers aim to promote all types of products, consumers also want to share this information via social media. This paper presents Shopaholic, a system that leverages social media to provide information on trending deals and store sales in any given location. It is intended to help shoppers identify great deals from the vast amounts of data scattered among social networks. Personalized search results, visualization of trends and sentiment analysis provided by Shopaholic allow the user to identify optimal deals. The application accounts for spatial and temporal data via a customized ranking algorithm and features integration with Twitter so that the user can share his or her actual experience using a deal. Ultimately, the system gives back to the shopping community by allowing users to share their experiences and evaluations of deals. A recommendation algorithm uniquely identifies the user's tastes, shopping history and current location to provide deal suggestions, thereby integrating temporal and spatial entities in recommendations.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","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":"122437872","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 continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPS-equipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location. In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.
{"title":"On the locality of keywords in Twitter streams","authors":"H. Abdelhaq, Michael Gertz","doi":"10.1145/2676552.2676554","DOIUrl":"https://doi.org/10.1145/2676552.2676554","url":null,"abstract":"The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPS-equipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location. In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","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":"128395967","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}
Today, myriads of sensors are surrounding us. Their usage ranges from environmental monitoring (e.g., weather and air quality), over sensor-equipped smart buildings, to the quantified self and other human observing applications. The data streams produced by such sensors often update with high frequencies, resulting in large data volumes. Being able to analyze those real-time sensor data streams requires efficient visualization techniques. In our work, we explore how 3D visualizations can be used to extend the available information space. More specifically, we present an approach for processing real-time sensor data streams to enable scalable Web-based 3D visualizations. Based on an event-driven architecture, our key contribution is the presentation of three processing patterns to optimize transmission of sensor data streams to 3D Web clients.
{"title":"Processing real-time sensor data streams for 3D web visualization","authors":"A. Bröring, David Vial, T. Reitz","doi":"10.1145/2676552.2676556","DOIUrl":"https://doi.org/10.1145/2676552.2676556","url":null,"abstract":"Today, myriads of sensors are surrounding us. Their usage ranges from environmental monitoring (e.g., weather and air quality), over sensor-equipped smart buildings, to the quantified self and other human observing applications. The data streams produced by such sensors often update with high frequencies, resulting in large data volumes. Being able to analyze those real-time sensor data streams requires efficient visualization techniques. In our work, we explore how 3D visualizations can be used to extend the available information space. More specifically, we present an approach for processing real-time sensor data streams to enable scalable Web-based 3D visualizations. Based on an event-driven architecture, our key contribution is the presentation of three processing patterns to optimize transmission of sensor data streams to 3D Web clients.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"651 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":"123349575","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}
GPS-based navigation systems widely available on automobiles and smartphones nowadays are essential to find the best routes in the complicated urban space. However, it is still difficult for bikers to take full advantages of such navigation systems due to the lack of consideration on the different driving conditions. Generally, motorcyclists and cyclists take rides on narrow alleys and sidewalks which have a high risk of bumping against pedestrians. Therefore, it is necessary to find comfortable driving routes, also possibly avoiding areas congested by crowds. However, it is impractical to monitor crowd's existence everywhere at all times for such crowd-aware navigation. To overcome this limitation, we attempt to utilize location-based social network services where geo-tagged microblogs from massive crowd can be a good alternative source to measure pedestrian congestion in urban areas. In this paper, we introduce a route search method for bikers particularly to exploit crowd's volunteering reports being streamed via microblogs. In order to estimate human traffic from microblogs, we develop a crowd flow network which captures probable crowd movement on an urban network. We also examine the possible intersections which are expected to be highly congested based on the model. On the crowd flow network, we will find the best routes consisting of comfortable intersections and streets for the bike navigation systems.
{"title":"Crowd-sourced prediction of pedestrian congestion for bike navigation systems","authors":"Shoko Wakamiya, Yukiko Kawai, Hiroshi Kawasaki, Ryong Lee, K. Sumiya, Toyokazu Akiyama","doi":"10.1145/2676552.2676562","DOIUrl":"https://doi.org/10.1145/2676552.2676562","url":null,"abstract":"GPS-based navigation systems widely available on automobiles and smartphones nowadays are essential to find the best routes in the complicated urban space. However, it is still difficult for bikers to take full advantages of such navigation systems due to the lack of consideration on the different driving conditions. Generally, motorcyclists and cyclists take rides on narrow alleys and sidewalks which have a high risk of bumping against pedestrians. Therefore, it is necessary to find comfortable driving routes, also possibly avoiding areas congested by crowds. However, it is impractical to monitor crowd's existence everywhere at all times for such crowd-aware navigation. To overcome this limitation, we attempt to utilize location-based social network services where geo-tagged microblogs from massive crowd can be a good alternative source to measure pedestrian congestion in urban areas. In this paper, we introduce a route search method for bikers particularly to exploit crowd's volunteering reports being streamed via microblogs. In order to estimate human traffic from microblogs, we develop a crowd flow network which captures probable crowd movement on an urban network. We also examine the possible intersections which are expected to be highly congested based on the model. On the crowd flow network, we will find the best routes consisting of comfortable intersections and streets for the bike navigation systems.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"2 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":"124533785","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}
Technological advances have created an unprecedented availability of inexpensive sensors able to stream environmental data in real-time. However, we still seek appropriate data management technology capable of handling this onslaught of sampling in previously unavailable spatial and temporal density. Data stream engines (DSEs) are state of the art data management tools that have update throughput rates of up to 500k tuples/s. In previous work we have shown that DSEs can be extended to generate smooth representations of continuous spatio-temporal fields sampled by up to 250K sensors on-the-fly in near real-time, creating a new representation every second. In this paper we investigate a spatio-temporal stream operator framework that can efficiently execute predicate operators over such spatio-temporal fields. Typical predicates are e.g. "find all sub-areas in a field that are below or above a certain threshold value". We present the requirements, the approach taken, and our results along with a performance evaluation.
{"title":"Evaluating stream predicates over dynamic fields","authors":"J. Whittier, Qinghan Liang, Silvia Nittel","doi":"10.1145/2676552.2676553","DOIUrl":"https://doi.org/10.1145/2676552.2676553","url":null,"abstract":"Technological advances have created an unprecedented availability of inexpensive sensors able to stream environmental data in real-time. However, we still seek appropriate data management technology capable of handling this onslaught of sampling in previously unavailable spatial and temporal density. Data stream engines (DSEs) are state of the art data management tools that have update throughput rates of up to 500k tuples/s. In previous work we have shown that DSEs can be extended to generate smooth representations of continuous spatio-temporal fields sampled by up to 250K sensors on-the-fly in near real-time, creating a new representation every second. In this paper we investigate a spatio-temporal stream operator framework that can efficiently execute predicate operators over such spatio-temporal fields. Typical predicates are e.g. \"find all sub-areas in a field that are below or above a certain threshold value\". We present the requirements, the approach taken, and our results along with a performance evaluation.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"67 4 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":"126773247","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 booming industry of location-based services, the analysis of human location histories is increasingly important. Next location prediction is essential to many location-based services. Predicting user's next location usually involves obtaining significant places from the history trajectories and predicting location with a certain statistic model. This paper presents new approaches to deal with both of above problems. For the former problem, a hierarchical clustering algorithm is proposed. We first identify specific features of stay points and then group the GPS points satisfying the identified features to form stay points by a new algorithm which is a variant of DBSCAN clustering algorithm. After that these stay points can be clustered to form significant places. For the later problem, taking the drawbacks like high space complexity and zero frequency problem in N-order Markov Model into consideration, we train a variable order Markov Model to predict next location. The variable order Markov Model uses escape mechanism to address the zero frequency problem and uses a tree structure to decrease the amount of memory needed in N-order Markov Model. An extensive set of experiments have been conducted to demonstrate the performance of proposed methods based on a real-world dataset, GeoLife.
{"title":"Predicting next location using a variable order Markov model","authors":"Jie Yang, Jian Xu, Ming Xu, Ning Zheng, Yu Chen","doi":"10.1145/2676552.2676557","DOIUrl":"https://doi.org/10.1145/2676552.2676557","url":null,"abstract":"Due to the booming industry of location-based services, the analysis of human location histories is increasingly important. Next location prediction is essential to many location-based services. Predicting user's next location usually involves obtaining significant places from the history trajectories and predicting location with a certain statistic model. This paper presents new approaches to deal with both of above problems. For the former problem, a hierarchical clustering algorithm is proposed. We first identify specific features of stay points and then group the GPS points satisfying the identified features to form stay points by a new algorithm which is a variant of DBSCAN clustering algorithm. After that these stay points can be clustered to form significant places. For the later problem, taking the drawbacks like high space complexity and zero frequency problem in N-order Markov Model into consideration, we train a variable order Markov Model to predict next location. The variable order Markov Model uses escape mechanism to address the zero frequency problem and uses a tree structure to decrease the amount of memory needed in N-order Markov Model. An extensive set of experiments have been conducted to demonstrate the performance of proposed methods based on a real-world dataset, GeoLife.","PeriodicalId":272840,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"46 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":"127198012","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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