Geospatiallntelligence on a graph

Abstract

Geographical Information Systems (GIS) bas being used oyer few decades across various organizations and also used by public in the day to da. for scenarios such as map representations, road navigation and Global Positi ning System (GPS) tracking. There are so many commercial and free GIS softw re is available out there for enterprises and also there are applications for per onal usage which are becoming very popular with increased usage of computer and mobile devices. On the other hand with the rapid increment computer systems usage and internet usage data is getting piled in exponentially. As a result of that concepts such as big data and NoSQL came to the picture in order to manipulate those data effectively. Graph databases arc one data model of NoSQL datab iscs which delivers significant advantages such as agility, flexibility and performance. Since geographic data is naturally structured like a graph, representing a GIS data in a graph structure can be useful for spatial index g, storage and topology in much effective way compared to other database type. There is a gap when representing organizational on maps whereas there are limitations when it comes to details. For an instance when restaurant cl ain company is representing their branch outlets on a map. They have to stick to basic set of attributes allowed by mapping service provider. Tbey can ot represent beyond that such as types of food they are selling and curr nt availability of them in' branch wise. This research will develop a implementation by combining these two technologies GIS and graph databases in order to achieve a central Geospatial intelligence on graph which livers benefits in the context of multiple data source integration and querying and analyzing them to generate new knowledge. In the implementation architecture a graph database stands in the back end and all the transactions are exposed and carried out by a managed Representational state transfer (REST) application programming interface (API) implementation. This API facility mak s the implementation as an interoperable platform which can be integrated with many other applications. Front end is implemented in Javascripr and mapping libraries which co ect to REST API backend and loads mapping information from a map engine. From the front end map routing and graph searching can be carried out. For graph results an optimal routing algorithm will be carried to for effective result s. At the end this research work outcome is a geospatial intelligence platform wl ich is implemented on a graph database.