In some business industries there is a need to analyze a large amount of data as well as to integrate their business data with a geo spatial data.
In the utilities industry, smart grid applications generate a huge amount of data and plotting the usage of electricity for each household on a map helps analysts or engineers to see which assets are being damaged by high usage, balance load with supply to avoid power outages and predict a better maintenance plan for the electrical assets.
In the government sector, there is a tremendous amount of data available which can be used to detect tax fraud, reduce the crime, to improve disease surveillance as well as to improve any kind of government services for each citizen.
In another sector, retail, there are many source of data such as social media, systems databases and surveys or customer feedback data which could be integrated with geo spatial to have a well-defined targeted market.
Other industries such as insurance and financial can also benefit greatly from high performance database and its integration with geo spatial data. They can be used for analyzing customer behaviour in order to detect fraud, spot some clusters which inform about some high risk areas, have a better targeted marketing and also help business investments to analyze which areas have specific insurance regulations.
Big data analysis without integration with geo spatial data would make it difficult to find hidden patterns for the industries mentioned earlier.
During the last decade, the volumes of data that are being stored have increased massively. This has been called the “industrial revolution of data.” The relational database model has prevailed for decades, but a new type of database known as NoSQL has been used for big data. A graph database is one of many types of NoSQL databases and is a good fit for this integration between big data and geospatial data. Therefore, this is a new technological approach for these business opportunities.
One compelling reason for choosing a NoSQL Graph Database is that it has great performance and that it is very responsive for a large amount of data. It also includes geo spatial data type and geo operations. In contrast to relational databases, where join-intensive query performance deteriorates as the dataset gets bigger, with a graph database performance tends to remain relatively constant, even as the dataset grows.
A graph database is a database which uses graph structure with nodes, edges, and properties to represent and store data. A graph database is based on graph theory.
The graph database is designed for relations of data which are well represented as a graph. The kind of data could be social relations, public transport links, road maps or network topologies, for example.
There is an open source graph database that can include spatial functionalities as a plug-in and it is presently the most popular graph database. It is called Neo4j. Neo4j is accessible from most programming languages using its built-in REST API interface.
Neo4J Spatial extends the Neo4J Graph Database with the necessary tools and utilities to store and query spatial data in your graph models. For example, Neo4J Spatial allows for queries to find nodes within a specified geographic region or within a certain distance of a specified point.
To Sum up, in order to meet the needs of some recent business opportunities as well as others which will appear in the near future, this open source graph database Neo4j with spatial functionalities can play a key role in implementing this solution.
Neo4j is a NoSQL Graph Database.