A graph database uses nodes and edges to store, map, and query relationships between data in a way that is optimized for highly connected information. It allows for schema-less and efficient storage of semi-structured data connected by rich relationships. Example applications include graph compute engines, recommendation systems, search engines, and social networks. While graph databases are well-suited for exploring connected data, they may not support data partitioning or use the single relational concept that traditional databases use.

1. SEMINAR
ON
GRAPHICAL DATABASE
PRESENTED BY:
NABAJYOTI SAIKIA
MCA 4th SEMESTER
ROLLNO.-14

2. INTRODUCTION
A graph database also called a graph
oriented database that uses graph to
store , map and query relationship. It is
optimised for managing highly related
data.
It is essentially a collection of nodes and
edges. An obvious example is the
facebook social network of people and
their activities.

3. Example

4. Applications
 Graph Compute Engine
 Real time recommendation engine
 Graph based search engine
 Social network

5. Advantages
 Schema less and efficient storage of semi-
structured information.
 Ease of storing and retrieving varried data
connected by rich relationships.

6. Disadvantages
 Not suitable for network
partitioning.
 Graph database use two concept to
represent information i.e; nodes
and edges ,whereas relational
database uses only one i.e;
relation.

7. Conclusion
A graph database is good fit for exploring
data that are like a graph, In this presentation
we reviewed the graph property model, a simple
yet expressive tool for representing connected
data. Property graphs capture complex domains
in an expressive and flexible fashion, while
graph databases make it easy to develop
applications that manipulate our graph models.

8. www.wikipedia.com
Graphdatabase.com
Whatis.techtarget.com
References

9. THANK YOU