2026-04-14T03:42:41Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1803582026-01-21T10:17:22Zcom_2072_1033col_2072_452950

urn:hdl:2117/180358 The OTree: multidimensional indexing with efficient data sampling for HPC Cugnasco, Cesare Calmet, Hadrien Santamaria Mateu, Pol Sirvent Pardell, Raül Eguzkitza, Ane Beatriz Houzeaux, Guillaume Becerra Fontal, Yolanda Torres Viñals, Jordi Labarta Mancho, Jesús José Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Emmagatzematge i recuperació de la informació Big data Distributed databases High performance computing Multidimensional indexing Distributed data store Macrodades Bases de dades distribuïdes Càlcul intensiu (Informàtica) Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability. Peer Reviewed Postprint (author's final draft) 2019 Conference report https://ieeexplore.ieee.org/document/9006121 info:eu-repo/grantAgreement/AGAUR/2017 SGR 1414 info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ info:eu-repo/grantAgreement/MINECO//SEV-2015-0493/ES/BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION/ info:eu-repo/grantAgreement/EC/H2020/780787/EU/Industrial-Driven Big Data as a Self-Service Solution/I-BiDaaS info:eu-repo/grantAgreement/EC/H2020/785907/EU/Human Brain Project Specific Grant Agreement 2/HBP SGA2 info:eu-repo/grantAgreement/EC/H2020/720270/EU/Human Brain Project Specific Grant Agreement 1/HBP SGA1 Open Access Institute of Electrical and Electronics Engineers (IEEE)