Database System Report: Analyzing Data Modeling and Graph Databases

This report provides an overview of database systems, with a specific focus on graph databases and data modeling. Part A delves into graph database research, explaining its structure based on nodes and edges, and highlighting its efficiency in processing and storing data, especially in handling relationships. It contrasts graph databases with relational databases, emphasizing the advantages of faster data retrieval through relationships and nodes, particularly in managing large datasets. The report also discusses the property graph model, the role of graph databases in applications like social media and fraud detection, and the benefits of reduced latency. Part B focuses on data modeling, presenting an entity-relation diagram of DiscPeezy. The report includes references to relevant research papers. The assignment covers the core concepts of database systems, making it useful for students studying database design and management.

Running head: DATABASE SYSTEM
DATABASE SYSTEM
Name of the Student
Name of the University
Author Note

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Table of Contents
Part A: Database research:.........................................................................................................2
Part B: Data Modelling:.............................................................................................................4
References:.................................................................................................................................5

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Part A: Database research:
A graph database is one type of database management system that is designed to treat
the connection between data, and the data is equally important to itself. The graph database
follows a graph model. A group of nodes called graph and edges connect it. Every node
contains information about the graph. Nowadays, the world is connected to everything
(Pokorný, 2016). There is no Inaccessible information available around us. A graph database
can process, store and use queries efficiently. Other relational database uses a time-
consuming method like join but graph database uses the data model that is more fast and
secure. Using relationships and nodes in the graph database can reduce the data retrieval time
and this the most efficient way for the data retrieval process. By using this method, the user
can fetch millions of data quickly. A large dataset is manageable with a graph database
(Labouseur et al., 2015). With the help of patterns and nodes graph database can explore the
data and aggregating information from the relationship and nodes. In the property graph
model, data is most organized like nodes, properties and relationships. A graph database is
one type of NoSQL. This database is helpful because it can highlight the relationship between
data.
JOIN operation, and foreign key is not required in this database. The graph database
mostly focused on the relationship. The result of using a relationship, it can increase the
flexibility, enhanced agility and traversal capabilities. A popular application like social
media, forensic analysis and fraud detection use the graph database to process rich datasets.
By using a graph database, reduce the latency in reading, create, delete and update (Ghrab et
al., 2016).
Relationship: In the graph, a database relationship provides the two-node connections. Every
relationship must have a direction, start node, end node and a type. Relationships can contain

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properties like nodes. In the data model, a relationship can have weights, rating, costs, time
intervals and properties (van Rest et al., 2016). Two nodes are able to share any number of
relationships and their types. It can store in the exact direction. In every direction, it is able to
navigate.
Nodes: One of the entities in the graph database is node. This database is able to hold and a
number of attributes. A node can be marked with labels and it can represent its role in the
domain model.
Due to Data explosion, big businesses are finding different technologies. This is the
main reason for using graph databases in business. Oracle, Sap, Microsoft and IBM are
continuously working on the graph database. Amazon Neptune is the most recent graph
database that is provided by Amazon. Day by day graph database is becoming a part of a
multi-model database.

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Part B: Data Modelling:
Figure 1: Entity relation diagram of DiscPeezy.
(Source: Created by author)

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References:
Ghrab, A., Romero, O., Skhiri, S., Vaisman, A., & Zimányi, E. (2016). Grad: On graph
database modeling. arXiv preprint arXiv:1602.00503.
Labouseur, A. G., Birnbaum, J., Olsen, P. W., Spillane, S. R., Vijayan, J., Hwang, J. H., &
Han, W. S. (2015). The G* graph database: efficiently managing large distributed
dynamic graphs. Distributed and Parallel Databases, 33(4), 479-514.
Pokorný, J. (2016, July). Conceptual and database modelling of graph databases.
In Proceedings of the 20th International Database Engineering & Applications
Symposium (pp. 370-377). ACM.
van Rest, O., Hong, S., Kim, J., Meng, X., & Chafi, H. (2016, June). PGQL: a property graph
query language. In Proceedings of the Fourth International Workshop on Graph Data
Management Experiences and Systems (p. 7). ACM.