Efficient Physical Organization of R-Trees Using Node Clustering

Francis, F. Sagayaraj; Thambidurai, P.

doi:10.3844/jcssp.2007.506.514

Cited by 2 publications

(2 citation statements)

References 9 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each grid g i in the spatial is treated as area, in that each grid is having some number of vehicles [n(v i ) gi ], in order to find the co-location, we need high density area since we assign the minimum bounding rectangle mbr which is minimum number of vehicles in each grid mbr. Here the R-Tree (Francis and Thambidurai, 2007) data structure is use to satisfy the minimum bounding rectangle mbr. If the grid is not satisfy the mbr then the size of the grid is get increase g i in all side in one unit, this process is repeat up to three times to satisfy the mbr.…”

Section: Incorporate the R-tree Data Structure With The Help Of Mbr Imentioning

confidence: 99%

An Efficient Algorithm for Mining Spatially Co-Located Moving Objects

Manikandan

Srinivasan

2013

American Journal of Applied Sciences

View full text Add to dashboard Cite

Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighborsâ in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Primâs Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took much computational cost. In order to reduce the computation time, in this study, we make use of R-tree that is spatial data structure to mine the spatial co-location patterns. The important step presented in the approach is that the transformation of spatial data into the compact format that is well-suitable to mine the patterns. Here, we have adapted the R-tree structure that converts the spatial data with the feature into the transactional data format. Then, the prominent pattern mining algorithm, FP growth is used to mine the spatial co-location patterns from the converted format of data. Finally, the performance of the proposed technique is compared with the previous technique in terms of time and memory usage. From the results, we can ensure that the proposed technique outperformed of about more than 50% of previous algorithm in time and memory usage

show abstract

Section: Incorporate the R-tree Data Structure With The Help Of Mbr Imentioning

confidence: 99%

An Efficient Algorithm for Mining Spatially Co-Located Moving Objects

Manikandan

Srinivasan

2013

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In R*-trees also we should not give the redundant values (Bae et al, 2010). R*-Tree: An R-tree (Francis and Thambidurai, 2007) is a B*-tree like access method that stores multidimensional rectangles as complete objects without clipping them or transforming them to higher dimensional points. The structure of R-Tree is given the Fig.…”

Section: The Different Objects Of Spatial Databasementioning

confidence: 99%

Effective Processing of Constraints based Spatial Join using R-Trees

Parvathi¹

2011

American Journal of Applied Sciences

View full text Add to dashboard Cite

Problem statement:This study focuses on the spatial join effects with the constraintsbased spatial data without any extra cost and Finding the minimum execution time of the spatial query and spatial selection method. Approach: Spatial joins are used to combine the spatial objects. The efficient processing depends upon the spatial queries. The execution time and I/O time of spatial queries are crucial, because the spatial objects are very large and have several relations. In this article, we use several techniques to improve the efficiency of the spatial join. (1) We use R*-trees for spatial queries since R*-trees are very suitable for supporting spatial queries as it is one of the efficient member of R-tree family. (2) The different shapes namely point, line, polygon and rectangle are used for isolating and clustering the spatial onjects. (3) We use scales with the shapes for spatial distribution. We also present several techniques for improving its execution time with respect to the CPU and I /O-time. In the proposed constraints based spatial join algorithm, total execution time is improved compared with the existing approach in order of magnitude. Using a buffer of reasonable size, the I/O time is optimal. The performance of the various approaches is investigated with the synthesized and real data set and the experimental results are compared with the large data sets from real applications. Results: The R*-tree concept reduce the number of search pages to combine spatial objects. By using this, CPU utilization time increases, the number of comparisons of spatial objects can be reduced and also reduces the I/O time. Conclusion/Recommendations: The performance of the various approaches is investigated with the synthesized and real data set and the experimental results are compared with the large data sets from real applications.

show abstract

Efficient Physical Organization of R-Trees Using Node Clustering

Cited by 2 publications

References 9 publications

An Efficient Algorithm for Mining Spatially Co-Located Moving Objects

An Efficient Algorithm for Mining Spatially Co-Located Moving Objects

Effective Processing of Constraints based Spatial Join using R-Trees

Contact Info

Product

Resources

About