Abstract- Graph Data Management is all about to maintain the large data in the form of Graphs. It is good for database researchers. Graph database is a database where the data structure for instances are modeled as graph by Quering. Graph Data Management System is Neo4j, Which implements the graphs of data.The application domains where graph or network analytics are regularly applied include social media, finance, communication networks, biological networks, and many others. Despite much work on the topic, graph data management is still a nascent topic with many open questions. In this research, we present the basic notation of graph databases, give an historical overview of its main development and study main current system that implement them.
It has been long recognized that graphs are a natural way to represent information and knowledge.Graph Data Management is managing, querying a set of entities(nodes) and interconnection(edges) between them. Both of which may have attributes with them. Both academia and industry show interest in graph data management because of adoption of graph models in several new applications domains. Graph Data Management System “Neo4j” is used to implement the graphs on large scale. This system uses the Cypher Query Language to make graphs of the data by querying the data.
The property graph contains connected entities (the nodes) which can hold any number of attributes (key-value-pairs). Nodes can be tagged with labels representing their different roles in your domain.Relationships provide directed, named semantically relevant connections between two node-entities. A relationship always has a direction, a type, a start node, and an end node. Like nodes, relationships can have any properties. In most cases, relationships have quantitative properties, such as weights, costs, distances, ratings, time intervals, or strengths. As relationships are stored efficiently, two nodes can share any number or type of relationships without sacrificing performance.