Abstract
Spatial-temporal computing refers to the modeling, management, and analysis of spatial and temporal information. Despite the recent advances in massive data manipulation, software system approaches that support the massive spatial-temporal data integration and analysis still face numerous challenges, including the lack of: (i) a high-level architectural framework for massive data integration and analysis; (ii) explicit integration and analysis abstractions; (iii) representations of integration and analysis resources; (iv) explicit provenance representation; (v) reusability of integration and analysis steps; (vi) reproducibility of studies; and (vii) models to build and customize integration and analysis applications. This paper proposes the design and implementation of a high-level domain-specific architecture for data integration and analysis that supports building applications in the spatial-temporal domain. The proposed approach describes three types of first-class citizens, which include abstractions to represent data sources, analysis models, and integration operations. It also benefits from domain-specific languages (DSLs) for high-level representations. To make provenance explicit, the proposed approach identifies three types of provenance information, namely description, analysis, and execution, which help to address reusability and reproducibility. Finally, this approach also supports a model-driven technique to generate integration and analysis steps.
Published Version
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