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}
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}
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}
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}
Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the "building blocks of public service society". Despite this, difficulties in data collection combined with the concept's subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names.
{"title":"A data driven approach to mapping urban neighbourhoods","authors":"P. Brindley, James Goulding, Max L. Wilson","doi":"10.1145/2666310.2666473","DOIUrl":"https://doi.org/10.1145/2666310.2666473","url":null,"abstract":"Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the \"building blocks of public service society\". Despite this, difficulties in data collection combined with the concept's subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"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":"128059337","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 paper describes a pedestrian population trend estimation method using location data of smartphone users. This technique is intended to be an alternative to traffic censuses using tally counters. Traffic censuses using tally counters are still commonly used to survey the number of pedestrians despite their cost and limitations in area and time. The proposed approach can replace the traffic census by using smartphone users' location data accumulated on Yahoo! Japan. Moreover, it is low cost because it uses location data collaterally acquired from smartphone users, and it has no limits in terms of area or time. This means pedestrian population trends in arbitrary and times about which we want to know can be estimated. The proposed technique is based on the assumption that the number of location data in an area is proportional to the population volume, but it also eliminates some data to increase pedestrian accuracy. In the elimination step, some location data that should not be counted as pedestrians are excluded by estimating transport modes from anteroposterior location data. The supplement step tackles the problem of data shortage when a target area is a small region by using a Gaussian kernel. The Gaussian kernel smoother is also used to deal with data interpolation in the time direction, and it enables us to estimate time-continuous pedestrian volumes in arbitrary areas. To evaluate the approach, a manual traffic survey was conducted in five areas on 11 days and the ground truth data are acquired. Experimental result shows the approach successfully estimate pedestrian population trends in areas. The proposed method makes less than one-tenth the mean squared errors of hourly pedestrian number estimation than the conventional approach.
{"title":"Hourly pedestrian population trends estimation using location data from smartphones dealing with temporal and spatial sparsity","authors":"Kentaro Nishi, K. Tsubouchi, M. Shimosaka","doi":"10.1145/2666310.2666391","DOIUrl":"https://doi.org/10.1145/2666310.2666391","url":null,"abstract":"This paper describes a pedestrian population trend estimation method using location data of smartphone users. This technique is intended to be an alternative to traffic censuses using tally counters. Traffic censuses using tally counters are still commonly used to survey the number of pedestrians despite their cost and limitations in area and time. The proposed approach can replace the traffic census by using smartphone users' location data accumulated on Yahoo! Japan. Moreover, it is low cost because it uses location data collaterally acquired from smartphone users, and it has no limits in terms of area or time. This means pedestrian population trends in arbitrary and times about which we want to know can be estimated. The proposed technique is based on the assumption that the number of location data in an area is proportional to the population volume, but it also eliminates some data to increase pedestrian accuracy. In the elimination step, some location data that should not be counted as pedestrians are excluded by estimating transport modes from anteroposterior location data. The supplement step tackles the problem of data shortage when a target area is a small region by using a Gaussian kernel. The Gaussian kernel smoother is also used to deal with data interpolation in the time direction, and it enables us to estimate time-continuous pedestrian volumes in arbitrary areas. To evaluate the approach, a manual traffic survey was conducted in five areas on 11 days and the ground truth data are acquired. Experimental result shows the approach successfully estimate pedestrian population trends in areas. The proposed method makes less than one-tenth the mean squared errors of hourly pedestrian number estimation than the conventional approach.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"19 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":"129215944","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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