Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can e...Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.展开更多
Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., tr...Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.展开更多
With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space an...With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.展开更多
基金Supported by the Innovation Project of IGSNRR (No. O9V90220ZZ)the Research Plan of LREIS (O88RA700KA),CAS
文摘Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations' semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result.
基金This work is partly supported by the National Natural Science Foundation of China under Grant No. 61402532, the Science Foundation of China University of Petroleum （Beijing） under Grant No. 2462013YJRC031, and the Excellent Talents of Beijing Program under Grant No. 2013D009051000003.
文摘Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.
基金The China Postdoctoral Science Foundation(2019M650830)The National Key Research and Development Program of China(2016YFC0502903,2017YFB0504201)+1 种基金The Seed Foundation of Tianjin University(2021XSC-0036)The Natural Science Foundation of Tianjin(19JCYBJC22400)。
文摘With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.
