User-Friendly Extendibility of Two Temporal Data Models to Spatiotemporal Data Models

Abstract

Data arising in real world applications have space and time dimensions that require database support. Because of this there is growing attention on spatiotemporal databases. In this paper, we introduce two temporal data models extendable to spatiotemporal data ones - point-based and temporal element-based data models. Our goal is to understand which data model is less complex than the other when space dimension is incorporated into the data models. To this end, we compare two query languages for the data models in spatiotemporal context - SQL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ST</sup> and ParaSQL. Since query languages are tightly coupled to their underlying data models, their complexity is influenced by their data models. We use Guting's use case in our comparison and show that ParaSQL is less complex than SQL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ST</sup> ; suffice to say that the temporal, element-based data model is more user-friendly extendible to spatiotemporal data models if data has similar properties to Guting's use case.