Efficient computation of map algebra over raster data stored in the k2-acc compact data structure

Caniupan, Monica; Torres-Avilés, Rodrigo; Gutierrez-Bunster, Tatiana; Lepe, Manuel

doi:10.1007/s10707-021-00445-y

Cited by 3 publications

(10 citation statements)

References 29 publications

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“…The closest work to ours is the development of map algebra operations on rasters represented using k 2 -acc. 31 They presented algorithms for the following operations: thresholding, sum and multiplication by a scalar, point-wise sum, and zonal sum. However, as explained, k 2 -acc has poor performance in comparison with k 2 -raster, especially when the raster has a moderate or high number of different values, which is the most common situation.…”

Section: 4mentioning

confidence: 99%

“…The other case is when the minimum value of the processed node is greater than the threshold, which results in returning a 1 in both maxval and minval. Otherwise, recursion continues performing a call for each of the k 2 children of the processed node (lines 14-17), and after returning from that call, the output tree is built (lines [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Global Operations: Thresholdingmentioning

confidence: 99%

“…The second experiment compares our method with the algorithms proposed for the k 2 -acc. 31 The reason of this division is that we use the authors' implementation of the algorithms on k 2 -acc, which do not write the output, that is, they only compute the result in main memory, but they do not write that result back to disk. Since NetCDF does that, in order to be fair, we decided to run a second experiment with our algorithms modified to behave in that way.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…For the second experiment, the source code for the algorithms of the map algebra operations over the k 2 -acc was provided by their authors. 31 It uses k = 2.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Therefore, to demonstrate the feasibility of using the

{k}^2

‐raster as the basis of an in‐memory system, we have to show the efficiency of

{k}^2

‐raster when performing map algebra operations. Algorithms for executing map algebra operations over compressed data using another compact data structure for rasters, called

{k}^2

‐acc, 21,22 have been previously presented 31 . However, as explained,

{k}^2

‐raster has been shown to be superior in all aspects to

{k}^2

‐acc.…”

Section: Introductionmentioning

confidence: 99%

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Map algebra on raster datasets represented by compact data structures

2023

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The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in‐memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called k2$$ {k}^2 $$‐raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. k2$$ {k}^2 $$‐raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in‐memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on k2$$ {k}^2 $$‐raster without a decompression procedure.

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Section: 4mentioning

confidence: 99%

Section: Global Operations: Thresholdingmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

“…For the second experiment, the source code for the algorithms of the map algebra operations over the k 2 -acc was provided by their authors. 31 It uses k = 2.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Therefore, to demonstrate the feasibility of using the

{k}^2

‐raster as the basis of an in‐memory system, we have to show the efficiency of

{k}^2

‐raster when performing map algebra operations. Algorithms for executing map algebra operations over compressed data using another compact data structure for rasters, called