With the increased shift towards GeoSpatial Web Services on both the Web and mobile platforms especially in the user-centric services, there is a need to improve the query response time. This paper attempt to evaluate the performance of an existing NoSQL database and SQL database with respect to LineIntersection problem across a range of databases. SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days. While NoSQL databases can handle the rise in the data stored and frequency at which it is accessed and processed which are essential features needed in a multiple geospatial data where we do not need a fixed schema(geometry) and fixed data size. For this comparative study, MongoDB is the NoSQL engine while the PostGIS is the chosen SQL engine. The dataset consisted of two independent layers of horizontal lines and vertical lines with incremental lengths and their size varied from ten lines to ten thousand lines in each layer. All the data in the analysis was processed using In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 25x which increases exponentially as the data size increases in both indexed and non-indexed operations. Given these results NoSQL databases may be better stated for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.
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