TARS: An Array Model with Rich Semantics for Multidimensional Data

Relational DBMSs have been shown to be inefficient for scientific data management. One main reason is the difficulty to represent arrays, which are frequently adopted as a data model for scientific datasets representation. Array DBMSs, e.g. SciDB, were proposed to bridge this gap, building on a native array representation. Unfortunately, important scientific applications, such as numerical simulation, have additional requirements, in particular to deal with mesh topology and geometry. First, transforming simulation results datasets into DBMS array format incurs in huge latency due to the fixed format of array DBMSs layouts and data transformations to adapt to mesh data characteristics. Second, simulation applications require data visualization or computing uncertainty quantification (UQ), both requiring metadata beyond the simulation output array. To address these problems, we propose a novel data model called TARS (Typed ARray Schema), which extends the basic array data model with typed arrays. In TARS, the support of application dependent data characteristics, such as data visualization and UQ computation, is provided through the definition of TAR objects, ready to be manipulated by TAR operators. This approach provides much flexibility for capturing internal data layouts through mapping functions, which makes data ingestion independent of how simulation data has been produced, thus minimizing ingestion time. In this paper, we present the TARS data model and illustrate its use in the context of numerical simulation application.