Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies

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.

Designing a location update strategy for free-moving and network-constrained objects with varying velocity

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.

位置不确定持续移动对象的离散表示(英文)

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.

Long-Term Tracking Based on Spatio-Temporal Context

Aiming at the problem that the fast tracking algorithm using spatio-temporal context(STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.

Efficient κ-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories

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.

Towards a New Paradigm for Brain-inspired Computer Vision

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.

一种使用多粒度时空对象的新冠肺炎病例信息可视分析方法

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.

Conflict Analysis and Detection Based on Model Checking for Spatial Access Control Policy

In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

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.