A flexible framework to cross-analyze heterogeneous multi-source geo-referenced information: the J-CO-QL proposal and its implementation

The need for cross-analyzing JSON objects representing heterogeneous geo-referenced information coming from multiple sources, such as open data published on the Web by public administrations and crowd-sourced posts and images from social networks, is becoming common for studying, predicting and planning social dynamics. Nevertheless, although NoSQL databases have emerged as a de facto standard means to store JSON objects, a query language that can be easily used by not-programmers to manipulate and correlate such data is still missing. Furthermore, when the information is geo-referenced, we also need both spatial analysis and mapping facilities. In the paper, we motivate the need for a novel flexible framework, named J-CO, that provides a query language, named J-CO-QL, enabling novel declarative (spatial) queries for JSON objects. We will illustrate the basic concepts of the proposal and the possible use of its spatial and non-spatial operators for cross-analyzing open data and crowd-sourced information. This framework is powered by a plug-in for QGIS that can be used to write and execute queries on MongoDB databases.