The increasing computational power of HPC systems fosters the
development of complex numerical simulations of phenomena in different
domains, such as medicine , Oil & Gas  and many other fields [3,4,5].
In such applications, a huge amount of data in the form of multidimensional
arrays is produced and need to be analyzed and visualized enabling
researchers to gain insights about the phenomena being studied.
Scientists also generate huge multidimensional arrays through
environmental observations, measurements of physical conditions and
other types of sensors. For instance, satellite data for Earth's weather,
oceans, atmosphere and land  are kept in the form of multidimensional
arrays in scientific file formats. Data collected by sensors in physics
experiments, such as the ones conducted in the photon studies by SLAC
National Accelerator Laboratory , are also represented and processed in
the form of multidimensional arrays.
Machine learning is another context in which multidimensional arrays are
present. They are the basic input format for the heavily optimized linear
algebra algorithms implemented in deep learning frameworks, such as:
TensorFlow, Keras and Torch. Deep Learning algorithms were able to
achieve superhuman performance for image recognition problems in the
past few years , and they are among the most promising alternative for
tackling difficult problems in Natural Language Processing, Image and
Video Recognition, Medical Image Analysis, Recommendation Systems
and many others. Thus, managing these large arrays in the context of deep
learning is a very important task.
The traditional approach for managing data in multidimensional arrays in
scientific experiments is to store them using file formats, such as netCDF
and HDF5. The use of file formats, and not a database management
systems (DBMS), in storing scientific data has been the traditional choice
due to the fact that DBMSs are considered inadequate for scientific data
management. Even specialized scientific data management systems, such
as SciDB , are not very well accepted for a myriad of reasons listed in
● the impedance mismatch problem [12,13], that makes the process of
ingesting data into a DBMS very slow.
● the inability to directly access data from visualization tools like
Paraview Catalyst  and indexing facilities like FastQuery .
● the Inability to directly access data from custom code, which is
necessary for domain specific optimized data analysis.
However, by completely dismissing DBMSs, some nice features also
become unavailable. Including the access for out-the-box parallel
declarative data processing with the usage of query languages and query
optimization, and management of dense and sparse matrices. In this talk,
we will present SAVIME, a Database Management System for Simulation
Analysis and Visualization in-Memory. SAVIME implements a
multi-dimensional array data model and a functional query language. The
system is extensible to support data analytics requirements of numerical
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