An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS

Bae, Wookeun; Alkobaisi, Shayma; Leutenegger, Scott T.

doi:10.1007/11827405_91

Cited by 3 publications

(7 citation statements)

References 12 publications

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“…Next, we evaluate the performance of PJ compared with an incremental sampling method presented in [3] as well as with the full R-tree join algorithm [10].…”

Section: Results Of Vector Spatial Joinsmentioning

confidence: 99%

“…We generated four sets of uniformly distributed data and four sets of exponentially distributed data (a mean of 0. 3 Table 2, the total number of data cells (pixels) is presented along with the total number of nonzero data cells and the data density for the synthetic datasets. Table 3 presents the information about water sediments datasets of the four states.…”

Section: Datasets and Experimental Methodologymentioning

confidence: 99%

“…The idea of incremental sampling techniques on vector data was proposed in [3,4] to provide interactive spatial join processing. The authors proposed two R-tree based sampling methods that were used to incrementally refine the estimated join result while providing a bounded confidence interval.…”

Section: Related Workmentioning

confidence: 99%

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An interactive framework for spatial joins: a statistical approach to data analysis in GIS

et al. 2011

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Many Geographic Information Systems (GIS) handle a large volume of geospatial data. Spatial joins over two or more geospatial datasets are very common operations in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of datasets. In this paper, we propose an interactive framework that provides faster approximate answers of spatial joins. The proposed framework utilizes two statistical methods: probabilistic join and sampling based join. The probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while the sampling based method provides an order of magnitude improvement over the full indexing tree joins of datasets and also provides running confidence intervals. The framework allows users to trade-off speed versus bounded accuracy, hence it provides truly interactive data exploration. The two methods are evaluated empirically with real and synthetic datasets.

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“…Next, we evaluate the performance of PJ compared with an incremental sampling method presented in [3] as well as with the full R-tree join algorithm [10].…”

Section: Results Of Vector Spatial Joinsmentioning

confidence: 99%

Section: Datasets and Experimental Methodologymentioning

confidence: 99%

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An interactive framework for spatial joins: a statistical approach to data analysis in GIS

et al. 2011

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“…R: Rivers r 1 r 3 r 4 r 5 r 6 r 7 r 8 r 10 r 11 r 12 r 13 r 16 r 17 r 9 r 14 4 An example of strata and clusters in the R-tree set S to obtain intersections. For each stratum, we obtain the number of intersections, and this number is used to calculate the estimate and confidence interval for the corresponding stratum.…”

Section: Irsj S ("S" For "Stratified")mentioning

confidence: 99%

“…Thus, many GIS applications can benefit from our approach. This paper is an expansion of our earlier work [4]. In that paper, we proposed the Incremental Refining Spatial Join (IRSJ) algorithm that provides interactive spatial query processing for data exploration in GIS.…”

Section: Introductionmentioning

confidence: 96%

IRSJ: incremental refining spatial joins for interactive queries in GIS

2009

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An increasing number of emerging web database applications deal with large georeferenced data sets. However, exploring these large data sets through spatial queries can be very time and resource intensive. The need for interactive spatial queries has arisen in many applications such as Geographic Information Systems (GIS) for efficient decision-support. In this paper, we propose a new interactive spatial query processing technique for GIS. We present a family of the Incremental Refining Spatial Join (IRSJ) algorithms that can be used to report incrementally refined running estimates for aggregate queries while simultaneously displaying the actual query result tuples of the data sets sampled so far. Our goal is to minimize the time until an acceptably accurate estimate of the query result is available (to users) measured by a confidence interval. Our approach enables more interactive data exploration and analysis. While similar work has been done in relational databases, to the best of our knowledge, this is the first work using this approach in GIS. We investigate and evaluate different sampling methodologies through extensive experimental performance comparisons. Experiments on both real and synthetic data show an order of magnitude response time improvement relative to the final answer obtained when using a full R-tree join. We also show the impact of 508 Geoinformatica (2010) 14:507-543 different index structures on the performance of our algorithms using three known sampling methods.

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An interactive framework for raster data spatial joins

Bae

Vojtěchovský

Alkobaisi

et al. 2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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Many Geographic Information Systems (GIS) handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic join and sampling based join. Our probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to tradeoff speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets.

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An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS

Cited by 3 publications

References 12 publications

An interactive framework for spatial joins: a statistical approach to data analysis in GIS

An interactive framework for spatial joins: a statistical approach to data analysis in GIS

IRSJ: incremental refining spatial joins for interactive queries in GIS

An interactive framework for raster data spatial joins

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