Geodesic
R-Tree for Phase Change Memory
Computer Science and Information Systems, Tome 14 (2017) no. 2.

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Nowadays, many applications use spatial data for instance-location information, so storing spatial data is important.We suggest using R-Tree over PCM. Our objective is to design a PCM-sensitive R-Tree that can store spatial data as well as improve the endurance problem. Initially, we examine how R-Tree causes endurance problems in PCM, and we then optimize it for PCM. We propose doubling the leaf node size, writing a split node to a blank node, updating parent nodes only once and not merging the nodes after deletion when the minimum fill factor requirement does not meet. Based on our experimental results while using benchmark dataset, the number of write operations to PCM in average decreased by 56 times by using the proposed R -Tree. Moreover, the proposed R-Tree scheme improves the performance in terms of processing time in average 23% compared to R-Tree.
Keywords: spatial database, spatial data, PCM, R -Tree, spatial tree, endurance, indexing algorithm 
@article{CSIS_2017_14_2_a4,
     author = {Elkhan Jabarov and Byung-Won On and Gyu Sang Choi and Myong-Soon Park},
     title = {R-Tree for {Phase} {Change} {Memory}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {14},
     number = {2},
     year = {2017},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2017_14_2_a4/}
}
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Elkhan Jabarov; Byung-Won On; Gyu Sang Choi; Myong-Soon Park. R-Tree for Phase Change Memory. Computer Science and Information Systems, Tome 14 (2017) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2017_14_2_a4/