Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM...To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM（ 1,1 ） model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object＇s movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.展开更多
In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-...In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.展开更多
Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been ...Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects(FMOs) and network-constrained objects(NCOs), which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy(FMOUS), to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update(meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, the FMO update procedure(FMOUP) is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy(NCOUS) and the NCO update procedure(NCOUP) to inform each object when and how to update location information. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.展开更多
Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over ...Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS （branch-and-bound skyline with rescanning and repacking） and TPBBS （time-parameterized branch- and-bound skyline）, each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE （TPBBS with expansion） to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.展开更多
Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the fun...Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures.The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories,but also of the inspection of the embedded geographical context.In this paper,we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments.Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters,which are then represented as polygons.For representing temporal variations of the created polygons,we enrich these with vehicle trajectories of other times of the day and additional road network information.In a case study,we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project.The first test results show strong correlations with periodical traffic events in Shanghai.Based on these results,we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.展开更多
Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work...Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.展开更多
Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques.However,its progress so far is not impressing.We recognize that a main obstacle comes from that the ...Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques.However,its progress so far is not impressing.We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision,i.e.,the biological vision is targeted for processing spatio-temporal patterns.Recently,a new paradigm for developing brain-inspired computer vision is emerging,which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data.In this paper,we review some recent primary works towards this new paradigm,including the development of spike cameras which acquire spiking signals directly from visual scenes,and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns,including models for object detection,tracking,and recognition.We also discuss about the future directions to improve the paradigm.展开更多
基金National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
文摘To incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM（ 1,1 ） model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object＇s movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects.
基金the International Collaborative Research Program of Shanghai Science and Technology Committee(No.12510708400)the Summit Filmology Program of Shanghai University in 2015(No.n.13-a303-15-w23)
基金supported in part by Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT0652)the National Natural Science Foundation of China (Grant No. 60603044).
文摘In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.
基金supported by the National Science Council of Taiwan(Nos.NSC-102-2119-M-244-001 and MOST-103-2119-M-244-001)
文摘Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects(FMOs) and network-constrained objects(NCOs), which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy(FMOUS), to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update(meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, the FMO update procedure(FMOUP) is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy(NCOUS) and the NCO update procedure(NCOUP) to inform each object when and how to update location information. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.
基金supported by the National Natural Science Foundation of China (Nos. 60603044 and 60803003)the Program for Changjiang Scholars and Innovative Research Team in University(No. IRT0652)
文摘Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS （branch-and-bound skyline with rescanning and repacking） and TPBBS （time-parameterized branch- and-bound skyline）, each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE （TPBBS with expansion） to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.
文摘Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures.The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories,but also of the inspection of the embedded geographical context.In this paper,we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments.Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters,which are then represented as polygons.For representing temporal variations of the created polygons,we enrich these with vehicle trajectories of other times of the day and additional road network information.In a case study,we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project.The first test results show strong correlations with periodical traffic events in Shanghai.Based on these results,we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.
文摘Nearest Neighbor （κNN） search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on κNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing κNN （κ≥ 1） search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPκNN and BFTκNN, which handle the κNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.
基金supported by National Key R&D Program of China(No.2020AAA0105200)Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project(No.2021ZD0200204)+1 种基金National Key Research and Development Program of China(No.2020AAA0130401)Huawei Technology Co.,Ltd,China(No.YBN2019105137)。
文摘Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques.However,its progress so far is not impressing.We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision,i.e.,the biological vision is targeted for processing spatio-temporal patterns.Recently,a new paradigm for developing brain-inspired computer vision is emerging,which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data.In this paper,we review some recent primary works towards this new paradigm,including the development of spike cameras which acquire spiking signals directly from visual scenes,and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns,including models for object detection,tracking,and recognition.We also discuss about the future directions to improve the paradigm.
