An Open Analysis Ready Data Service System

Moving data distribution services into the cloud computing environment brings closer the shifting of pre-processing functions to the responsibility of data distributers. Cloud computing provides the scaling up capabilities on analysis, modeling, and processing while the demand on data readiness for modeling and analysis increases. More and more analytic tools and models are deployed into cloud computing environment. These online tools expect analysis ready data (ARD) to streamline the processes of analysis and modeling. In the cloud geoprocessing environment, ARD refers to data: (1) with sufficient metadata for discovery, (2) directly usable in subset, (3) radiometrically aligned, (4) geometrically aligned, (5) temporally aligned, and (6) quality-controlled. The service of ARD can be done through either pre-cached and pre-tiled hosting service or on-demand processing with smart caching management. With the on-demand processing service system, the readiness level of ARD can be higher than that can be achieved using static data services. The adoption of open specifications and standards in service system design and component implementation increases the interoperation with selectable pre-processing algorithms. To achieve the high level of data readiness and interoperation, this study designs an ARD service system using open specifications, especially those from ISO TC 211 and OGC for geospatial information and processing interfaces. The standards for open distributed processing (ODP) frameworks, ISO/IEC 10746 series, are adopted to blueprint the design of the service system. The open standards for service components and interfaces include interfaces for discovery (API-Records), data access (API-Coverages, API-Features, and API-Tiles), and processing (API-Processes). The open ARD service system will be prototyped with serving aerosol optical depth and albedo data from Aura OMI. Selectable pre-processing algorithms will be enabled as API-processes to support ARD customization. Cloud-ready scalable frameworks (GeoMesa or GeoWave) will be used in implementation to leverage its scalability of cloud computing resources. The ARD services are expected to achieve a high readiness level for data to be discovered and used by analytical services, especially those already in the cloud and online.