Big Data has long been the key point in the progressive digitization of our society. However, this can also pose many problems: growth that is too fast and constantly evolving in an unstructured manner, which requires high-performance IT solutions to be able to be analyzed and used effectively.
The graph-oriented database is a database model capable of managing a large amount of information and thus a large number of data. It provides an answer to the problems posed by the traditional relational database. Too much data cannot be managed by a conventional database. This is also why at DCbrain we use graph databases. These databases are also grouped under the term NoSQL (“Not only SQL”).
So how does a graph database work and why use it?
The functioning of graph databases:
As the name suggests, a graph-oriented database is a graph-based representation. These graphs make it possible to represent a large number of data in a readable way, information which are connected to each other in a complex way, as well as their relations with each other.
Graphs are made up of nodes, which are uniquely identifiable, designated entities or data objects, and edges, which can also be called “edges”. These represent the relationships between “objects”. Visually, these two elements are respectively represented by points and by lines. The edges therefore each have a starting point and ending point, while each node always has a certain number of relations to other nodes, whether they are incoming, outgoing or undirected.
How requests work in a graph database:
When using a graph-oriented database, many queries can be processed. This is mainly due to the fact that there is no standard and uniform query language. Graph-oriented databases also implement specific algorithms to accomplish their essential mission: to facilitate and accelerate queries and the processing of large amounts of data or complex data.
One of the strengths of the graph-oriented database is that the relationships are stored in the database itself, which is extremely practical when you are having a large amount of data for a network (logistics for example) which is often the case for DCbrain. This allows rapid execution and processing speed for all types of requests.
What is the use of graph databases?
Graph-oriented databases can be used on any type of complex network or not and for any physical flow. They make it possible to analyze interconnected information, to understand, assess and exploit the processes and connections.
The analysis of relationships between users in social networks (such as Facebook or LinkedIn) or the purchasing behavior of customers in stores are concrete examples of the use of this kind of database. Based on the different data, recommendations can thus be given and networks of people or products. In supply chain management, graph-oriented databases allow monitoring of all processes, from design to sale. Finally, these databases are used for risk analysis, the detection of anomalies, forecasting flows, optimizing the network (transport route for example) or even previewing the network.
The advantages of graph databases:
The strength of a database can be mainly measured according to four criteria: integrity, performance, efficiency and scaling up. The data query must be faster and simpler, that’s the point of a graph-oriented database. The number of data and their complexity is not a problem for this kind of databases unlike so-called traditional databases, which is one of the reasons for their major use at DCbrain. In addition, “real facts can be stored naturally” (natural disasters, hazards, etc.) with the graph-oriented database model. The structure used is very similar to human thought and therefore makes the links particularly understandable and usable.
The use of graph databases at DCbrain:
The DCbrain program INeS, combines artificial intelligence and graph database because it is more effective than using only artificial intelligence. For example, a good AI program needs a lot of pre-existing data to be effective; if graph graph database is also used, the program will need less data. DCbrain data is stored in the form of a graph, because a graph is perfect for representing a network (of gas, transport etc.) and we have to use a lot of data for complex physical networks. In addition, our graph databases allow us to create our digital duplicates, which are the keystones of our work at DCbrain.