Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

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Key and referential constraints are two main integrity constraints in database applications. These constraints can be automatically enforced by the Database Management System with their exception—violation from these constraints—handled by programmers. This paper proposes an approach to relieve the burden of programmers from mechanical coding for handling exceptions of these constraints violation by using program transformation. We first propose an extended abstract syntax tree to include SQL query semantics. Based on it, each code pattern that requires exception handling together with the exception handling code to be inserted is represented as a transformation rule. We provide two alternatives to handle the exceptions: one is to handle the exceptions in conjunction with the built-in enforcement feature in Database Management System; the other is handling them without using the feature provided in Database Management System. Hence, two types of transformation rules are provided accordingly. A tool GEHPHP (Generation of Exception Handling for PHP Systems) has been developed to implement the proposed approach. Experiments have also been conducted to evaluate the applicable of the approach.