Map algebra on raster datasets represented by compact data structures

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract]: 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 (Formula presented.) -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. (Formula presented.) -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 (Formula presented.) -raster without a decompression procedure. This work was supported by the National Natural Science Foundation of China (Grant Nos. 31171944, 31640068), Anhui Provincial Natural Science Foundation (Grant No. 2019B319), Earmarked Fund for Anhui Science and Technology Major Project (202003b06020016). Information CITIC, Ministerio de Ciencia e Innovación, Grant/Award Numbers: PID2020-114635RB-I00; PDC2021-120917-C21; PDC2021-121239-C31; PID2019-105221RB-C41; TED2021-129245-C21; Xunta de Galicia, Grant/Award Numbers: ED431C 2021/53; IN852D 2021/3 (CO3)This work was partially supported by CITIC, CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Department of Culture, Education, Vocational Training and Universities and the Galician universities for the reinforcement of the research centers of the Galician University System (CIGUS). IN852D 2021/3(CO3): partially funded by UE, (ERDF), GAIN, convocatoria Conecta COVID. GRC: ED431C 2021/53: partially funded by GAIN/Xunta de Galicia. TED2021-129245B-C21; PDC2021-121239-C31; PDC2021-120917-C21: partially funded by MCIN/AEI/10.13039/501100011033 and “NextGenerationEU”/PRTR. PID2020-114635RB-I00; PID2019-105221RB-C41: partially funded by MCIN/AEI/10.13039/501100011033. Funding for open access charge: Universidadeda Coruña/CISUG. Xunta de Galicia; ED431C 2021/53 Xunta de Galicia; IN852D 2021/3 (CO3) National Natural Science Foundation of China; 31171944 National Natural Science Foundation of China; 31640068 Anhui Provincial Natural Science Foundation; 2019B319