Neo4J Applications

The graph data model in general and Neo4J specifically renowned for the power and flexibility, and its essential expressiveness. The graph databases application in a real-world problem can be seen as the fast performance of the queries and responsiveness helps organizations with their online transactional systems. Graph databases also ideal for quick development cycles, finally Neo4J is “Enterprise” ready in terms of capabilities, performance, and characteristics.

Neo4J Applications

Neo4J Applications

Social Science Application.

Social applications allow organizations to gain competitive and operational advantage by leveraging information about the connections between people, together with discrete information about individuals, to facilitate collaboration and flow of information, and predict behavior. As Facebook’s use of the term social graph implies, graph data models and graph databases are a natural fit for this overtly relationship-centered domain (Robinson; Webber; & Eifrem, 2013). By understanding who interacts with whom, how people are connected, and what representatives within a group are likely to do or choose based on the aggregate behavior of the group, Neo4J can generate tremendous insight into the unseen forces that influence individual behaviors.

Master Data Management

Master data is data that is critical to the operation of a business, but which itself is non-transactional. Master data includes data concerning users, customers, products, suppliers, departments, geographies, sites, cost centers, and business units. Graph databases don’t provide a full MDM solution; they are, however, ideally applied to the modeling, storing, and querying of hierarchies, master data metadata, and master data models. Such models include type definitions, constraints, relationships between entities, and the mappings between the model and the underlying source systems (Robinson; Webber; & Eifrem, 2013).

Logistics and Supply Chain.

Logistics related applications of graph databases range from calculating routes between locations in an abstract network such as a road or rail network, airspace network, or logistical network to spatial operations such as find all points of interest in a bounded area, find the center of a region, and calculate the intersection between two or more regions (Robinson; Webber; & Eifrem, 2013). Logistics operations depend upon specific data structures, ranging from simple weighted and directed relationships, through to spatial indexes that layout multidimensional attributes usingtree map data structures.

Recommended Systems.

Effective recommendations are a prime example of generating end-user value through the application of an inferential or suggestive capability. Recommendation algorithms establish relationships between people and things: other people, products, services, media content—whatever is relevant to the domain in which the recommendation is employed (Robinson; Webber; & Eifrem, 2013). As nodes and relationships are created in the system, they can be used to make recommendations like “when invoicing this item, these other items are usually invoiced.” Or, it can be used to make recommendations to travelers mentioning that when other visitors come to Barcelona they usually visit Antonio Gaudi’s creations (Sadalage & Fowler, 2012).

Fraud Detection Systems.

The principle is quite simple: in many cases, the fraud of a particular nature is not defined by one transaction only, but by a chain of transactions that have their specific characteristics and that need to be compared to one another to see if they really do constitute a case of fraud. That are based on linked intelligence. Unlike the traditional relational database technologies which requires modeling the graph above as a set of tables and columns, and then carrying out a series of complex joins and self joins. This type of queries are very complex to build and expensive to run on a traditional RDBMS. Scaling them in a way that supports real-time access poses significant technical challenges, with performance becoming exponentially worse not only as the size of the ring increases, for that reason Graph databases have emerged as an ideal tool.