The OTree: Multidimensional Indexing with efficient data Sampling for HPC

Authors Cesare Cugnasco, Hadrien Calmet, Pol Santamaria, Raül Sirvent, Ane Beatriz Eguzkitza, Guillaume Houzeaux, Yolanda Becerra, Jordi Torres, Jesús Labarta
Title The OTree: Multidimensional Indexing with efficient data Sampling for HPC
Abstract 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.
ISBN 978-1-7281-0858-2
Conference 2019 IEEE International Conference on Big Data (Big Data)
Date 9-12 December 2019
Location Los Angeles, CA, USA
Url https://zenodo.org/record/3872783#.XtYLci2w1TZ
DOI https://doi.org/10.1109/BigData47090.2019.9006121