MBA510 Data Relationship Modelling & Analysis Essay Report 4 Sample

MBA510 Data Relationship Modelling & Analysis. Ass 4

Management – Report/ Essay

Your Task

1. Select one of the Graph CQL Workbooks.

2. Using this Workbook of graph database queries, write an online guide / instruction manual for a general (non-technical audience).

3. Reflect on your experiences in MBA510.

Assessment Description

Select, from any of the relevant weeks, a graph database topic e.g., Sports Analytics.

Assessment Instructions

Section 1: Instruction Manual (10 marks)

The audience for the below is someone from your family who have never heard of graph databases before. Using your one graph query topic, write a guide, for one of your family members, that should: (500-600 words):

1. Show how the entire sets of queries work.

2. Make good use of diagrams as aids to non-verbal communications, especially graph relationship diagrams.

3. Demonstrate the kinds of insights that the queries provide.

Section 2: Reflective Essay (10 marks)

Reflect on how this course has changed your view of graph database and how it could be used in your future career (500-600 words):

In your reflection you may want to consider:

• What were you most proud of in doing MBA510?

• What difficulties did you have to overcome during the course?

• What did you learn about data relationships modelling and analysis?

• What advice would you have for a student doing MBA510 next trimester?

• What applications does graph databases have and will have in the future?

Solution

Section 1 – Instruction Manual

Graph queries are a method to analyze useful information from interconnected data. It helps to fetch data from a graph database.  As the graph has edges and nodes, graph query can be executed by matching similar patterns using the common query language – GraphQL (Lei, Ozsoyoglu and Ozsoyoglu, 1999). As per the MBA Assignment Expert, The graph database is a database that is used to store and manage data in the form of graph structure where data represent nodes or vertices and the relationships between these nodes can be shown through edges. The main function of this type of database is to provide availability and flexibility so that it can be easy to deal with the interconnected data. It helps in analyzing interconnected and complex network structures like fraud detection and knowledge graph. The key operation of a graph database is to deal with interconnected nodes and their relationships using a specialized query language and operations like creating, updating, or deleting nodes and their relationships (AWS, 2017).

Sports Analytics

Statistical analysis in sports: This study is about how graph databases may help us better understand the sporting world. This article will explain the notion of graph databases and show how they may discover useful information, whether a sports fan or simply intrigued about the convergence of data and sports.
To store and portray data, graph databases use graph topologies consisting of nodes, edges, and attributes. Players, teams, matches, statistics, and the connections between them may all be stored in a graph database for use in sports analytics. As a result, we can analyze intricate interconnections and get insights that could be overlooked by more conventional databases.
 

Figure 1 Example of interconnectedness in graph query
(Source: Abdelaziz, 2017)

Relationship Visualisation in Graph Databases: Here, the Liverpool Football Club will use sports analytics to identify favorite sports as nodes in a graph, and the edges between them as their affiliations and rivalries on the pitch. 

Players (A, B, C, and D) are represented as nodes, while the connections between them (team membership, rivalry, etc.) are shown as edges. We can better see patterns and conclude the data thanks to this visual aid.
 

Figure 2 Query graph example for sports analytics

Querying a Graph for Answers:

Question 1: The first query seeks everyone who has ever suited up for Team X.

With this query, we can find every player that has ever played on Team X. We can get a list of players that are part of Team X by going over the graph and tracing the edges that link the players and the team. Team managers, scouts, and historians may all benefit from this data.

Question 2: Who are Player A and Player B's shared foes?

The teams that both Player A and Player B have faced may be determined by looking at the relationships between the two. Matchups between players, comparisons of performance, and the identification of players with similar playing styles may all benefit from this data.

Question 3: Find the quickest route from Player A to Player D.

We can determine the quickest route between two players by using graph algorithms. This may help trace the chain of command and see where ties are being established indirectly. Even though A and D have never faced each other in competition, we could find out that they have played on the same team in the past.

Examples like this show how useful graph searches can be for sports analytics. Traditional data models sometimes fail to reveal intricate linkages, patterns, and trends; this is where graph databases shine. In this way, graph databases are a useful resource for mining insights from large amounts of sports data. Graph databases let us see patterns in data that could otherwise be concealed by displaying information as nodes and edges (Besta et al., 2019). There are infinite ways to use graph databases for sports analytics, and the queries we investigated are simply the tip of the iceberg. Dive into the data-driven realm of sports analytics to discover the untold tales that exist there.

Section 2 – Reflective Essay

MBA510 has been essential in expanding my knowledge of graph databases and the many fields in which they may be used. The course's introduction to graph databases and their potential for modeling and analyzing data interactions has me enthusiastic about my future in the field.

My understanding of graph databases from MBA510 is something I'm very pleased with. Initially, I found it difficult to get my head around the concept of encoding data via nodes and edges. However, I was able to get a strong grounding in graph databases with the aid of the course materials, interactive exercises, and interesting conversations. I'm pleased with how far I've come in learning about graph databases and how they differ from conventional relational databases.

I had to show resilience and creative problem-solving to get over the challenges I had throughout the course. Complex graph searches were difficult to grasp and implement (Zhou et al., 2020). This was a barrier that I was able to overcome via the diligent study of the course materials and repeated practice with simulated data sets. In addition, understanding graph connection diagrams were difficult at first, but growing familiarity with them and practicing on real-world instances helped.

I gained a lot of knowledge about data connection modeling and analysis from this training. As I've seen, connections between data points may be just as informative as the data itself. Graph databases are particularly useful for collecting and analyzing complicated interactions, which in turn facilitates more nuanced analysis and better decisions. Graph algorithms and queries allow us to see relationships and trends in data that may be obscured by more conventional database organizations.

I recommend that the next MBA510 class fully immerse themselves in the experiential learning opportunity it provides. Participate in class discussions, practice composing inquiries, and investigate applicable case studies. Taking an active role in class discussions and working together with other students can greatly improve the educational experience. Tutorials, case studies, and online groups are just a few examples of supplementary materials that may supplement learning and give further encouragement and insight. Graph databases show promise for future use in a wide range of contexts. Graph databases have many applications outside sports analytics, including social network analysis, recommendation systems, fraud detection, supply chain optimization, and healthcare analytics, all of which were touched on in this article. The need for efficient data connection modeling and analysis will rise as data continues to expand in complexity and interconnectivity. Graph databases provide a robust approach to overcoming these difficulties and gaining useful information.

It is my hope to one day use graph databases in my work to better grasp data linkages and address difficult challenges (Sakr et al., 2021). Knowing how to make use of graph databases will provide me an advantage in several fields, including business intelligence, data science, and strategic decision-making. The training not only gave me a good background but also piqued my interest in learning more about this fascinating area. So, MBA510 was an eye-opening experience that completely altered my perspective on graph databases. I am pleased with how far I have come, from struggling to understand the ideas to feeling comfortable with querying and analyzing data connections. The course has given me a new outlook on the world, and I can't wait to put what I've learned about graph databases to use in my future work to help me get insightful data and make better choices in our interconnected world.

References

Abdelaziz, I. 2017. Combining vertex-centric graph processing with sparql for large-scale RDF Data Analytics, IEEE Transactions on Parallel and Distributed Systems, 28(12), pp. 3374–3388. doi:10.1109/tpds.2017.2720174. 

AWS. (2017). What Is a Graph Database? [online] Available at: https://aws.amazon.com/nosql/graph/#:~:text=Graph%20databases%20are%20purpose%2Dbuilt,to%20store%20relationships%20between%20entities. [Accessed 15 Jun. 2023].

Besta, M., Peter, E., Gerstenberger, R., Fischer, M., Podstawski, M., Barthels, C., Alonso, G. and Hoefler, T., 2019. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. arXiv preprint arXiv:1910.09017.

Lei, S., Ozsoyoglu, Z.M. and Ozsoyoglu, G. 1999. A graph query language and its query processing, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337), pp. 1–21. doi:10.1109/icde.1999.754973. 

Sakr, S., Bonifati, A., Voigt, H., Iosup, A., Ammar, K., Angles, R., Aref, W., Arenas, M., Besta, M., Boncz, P.A. and Daudjee, K., 2021. The future is big graphs: a community view on graph processing systems. Communications of the ACM, 64(9), pp.62-71.

Zhou, X., Sun, J., Li, G. and Feng, J., 2020. Query performance prediction for concurrent queries using graph embedding. Proceedings of the VLDB Endowment, 13(9), pp.1416-1428.