RT Journal Article
T1 A survey on machine learning in array databases
A1 Villarroya Fernández, Sebastián
A1 Baumann, Peter
K1 Array data
K1 Array database managers
K1 Machine learning
K1 Efficient array machine learning
AB This paper provides an in-depth survey on the integration of machine learning and array databases. First,machine learning support in modern database management systems is introduced. From straightforward implementations of linear algebra operations in SQL to machine learning capabilities of specialized database managers designed to process specific types of data, a number of different approaches are overviewed. Then, the paper covers the database features already implemented in current machine learning systems. Features such as rewriting, compression, and caching allow users to implement more efficient machine learning applications. The underlying linear algebra computations in some of the most used machine learning algorithms are studied in order to determine which linear algebra operations should be efficiently implemented by array databases. An exhaustive overview of array data and relevant array database managers is also provided. Those database features that have been proven of special importance for efficient execution of machine learning algorithms are analyzed in detail for each relevant array database management system. Finally, current state of array databases capabilities for machine learning implementation is shown through two example implementations in Rasdaman and SciDB
PB Springer
SN 0924-669X
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29162
UL http://hdl.handle.net/10347/29162
LA eng
NO Applied Intelligence (2022).  https://doi.org/10.1007/s10489-022-03979-2
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
DS Minerva
RD 24 abr 2026