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The geographic data models are mainly based on geographical entities nowadays. There still exist some problems about these models: separation of the concept and storage for a complete geographic entity (river, railway, etc.), delayed updates in some real-time applications, lack of relationship between geographical entities, difficult management and linkage update for multi-source heterogeneous data, and so on. A unified data model for geographic entities and geographic events is designed to respond to the problems. In this model, data can be organized based on the basic granularity of the conceptually complete geographic entity. Geographical events is fully described using geometry, attribute and relationship information, and interaction between geographical entities and geographical events is achieved. Based on GeoJSON, GeoEntityJSON and GeoEventJSON are designed to implement the physical model of the data. Taking a residential area in a certain city in China as an example, a real estate management model is established. The data model is used to realize the storage management and visualization of real estate.
Wei Xiong; Hao Chen; Ning Guo; Qi Gong; Wenze Luo. ENSTDM: An ENtity-Based Spatio-Temporal Data Model and Case Study in Real Estate Management. Spatial Data and Intelligence 2021, 27 -42.
AMA StyleWei Xiong, Hao Chen, Ning Guo, Qi Gong, Wenze Luo. ENSTDM: An ENtity-Based Spatio-Temporal Data Model and Case Study in Real Estate Management. Spatial Data and Intelligence. 2021; ():27-42.
Chicago/Turabian StyleWei Xiong; Hao Chen; Ning Guo; Qi Gong; Wenze Luo. 2021. "ENSTDM: An ENtity-Based Spatio-Temporal Data Model and Case Study in Real Estate Management." Spatial Data and Intelligence , no. : 27-42.
Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.
Ning Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information 2019, 8, 518 .
AMA StyleNing Guo, Shashi Shekhar, Wei Xiong, Luo Chen, Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information. 2019; 8 (11):518.
Chicago/Turabian StyleNing Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. 2019. "UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model." ISPRS International Journal of Geo-Information 8, no. 11: 518.
Online visualization and query of massive geo-spatial data are facing increasing challenges with the explosive growth of location-based spatial datasets. In the practical scenario, online visualization is carried out in a progressive way, namely, a sketchy view map is first presented, and more detailed view maps are produced gradually as the viewport scale goes deeper. One approach is to use the multi-scale spatial index technique. However, it loses the original data attribute and cannot provide spatial statistics information. The paper is to provide an improved index structure, the Geo-Gap tree, which aims to enhance online interactive access to large spatial datasets, as well as enable one to compute statistical attributes like aggregation at the coarse level. Therefore, the first focus of Geo-Gap tree is improving the efficiency of tree building. For this purpose, an adaptive geohash coding is introduced to reduce the computing of neighboring objects. And, this phase can be improved in parallel once objects are partitioned. Compare to Gap tree, the cost of building the Geo-Gap tree can be greatly reduced. The second contribution is to choose data at different level based on sampling so that a sample for each level can be served as a progressive query result. The third contribution is an estimation of progressive query results, which ensure that progressive query accuracy can be controlled within the range of theoretical analysis. With the query continuing to execute, the query results become more and more accurate. The method is now integrated successfully into a high-performance geographic information system called HiGIS.
Wei Xiong; Ruiqing Li; Jin Peng; Ye Wu; Ning Guo; Ning Jing. Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data. IEEE Access 2019, 7, 99428 -99440.
AMA StyleWei Xiong, Ruiqing Li, Jin Peng, Ye Wu, Ning Guo, Ning Jing. Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data. IEEE Access. 2019; 7 ():99428-99440.
Chicago/Turabian StyleWei Xiong; Ruiqing Li; Jin Peng; Ye Wu; Ning Guo; Ning Jing. 2019. "Geo-Gap Tree: A Progressive Query and Visualization Method for Massive Spatial Data." IEEE Access 7, no. : 99428-99440.
The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms.
Ning Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information 2017, 6, 326 .
AMA StyleNing Guo, Mengyu Ma, Wei Xiong, Luo Chen, Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information. 2017; 6 (11):326.
Chicago/Turabian StyleNing Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. 2017. "An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold." ISPRS International Journal of Geo-Information 6, no. 11: 326.
Wei Xiong; Ye Wu; Zhen Zhang; Qiu-Yun Wu. Termination Analysis for Active Rule Set Based on Triggering Path. Chinese Journal of Computers 2012, 35, 65 -75.
AMA StyleWei Xiong, Ye Wu, Zhen Zhang, Qiu-Yun Wu. Termination Analysis for Active Rule Set Based on Triggering Path. Chinese Journal of Computers. 2012; 35 (1):65-75.
Chicago/Turabian StyleWei Xiong; Ye Wu; Zhen Zhang; Qiu-Yun Wu. 2012. "Termination Analysis for Active Rule Set Based on Triggering Path." Chinese Journal of Computers 35, no. 1: 65-75.
Chip Multi-Processor(CMP) allows multiple threads to execute simultaneously. Because threads share various resources of CMP, such as L2-Cache, CMP system is inherently different from multiprocessors system and, CMP is also different from simultaneous multithreading (SMT). It could support more than two threads to execute simultaneously, and some executing units are owned by each core. We present hash join optimization based on shared cache CMP. Firstly, we propose multithreaded hash join execution framework based on Radix-Join algorithm, then we analyze the factors which affect performance of multithreaded Radix-Join algorithm in CMP. Basing on this analysis, we optimize the performance of various threads and their shared-cache access performance in the framework, and then theoretic analysis of speedup in multithreaded cluster partition phase is presents which could give some advices to cluster partition thread optimization. All of our algorithms are implemented in EaseDB. In the experiments, we evaluate performance of the multithreaded hash join execution framework, and the results show that our algorithm could effectively resolve cache access conflict and load imbalance in multithreaded environment. Hash join performance is improved.
Deng Yadan; Jing Ning; Xiong Wei; Chen Luo; Chen Hongsheng. Hash Join Optimization Based on Shared Cache Chip Multi-processor. Computer Vision 2009, 293 -307.
AMA StyleDeng Yadan, Jing Ning, Xiong Wei, Chen Luo, Chen Hongsheng. Hash Join Optimization Based on Shared Cache Chip Multi-processor. Computer Vision. 2009; ():293-307.
Chicago/Turabian StyleDeng Yadan; Jing Ning; Xiong Wei; Chen Luo; Chen Hongsheng. 2009. "Hash Join Optimization Based on Shared Cache Chip Multi-processor." Computer Vision , no. : 293-307.