ACIT4011 Graph Data Management Course description

In today's data-driven world, the effective management of graph data has emerged as a vital skill across various domains, including business, technology, and sciences. Graphs provide a powerful way to represent and analyse interconnected data, and they play a crucial role in fields such as complex systems and network science, social sciences, biology and genetics, and more.

In the area of Artificial Intelligence and Data Science, graph databases enable the modelling of complex relationships between data points, enhancing machine learning algorithms and data analysis techniques. Application areas such as social media can benefit greatly from graph databases as they allow for efficient representation and querying of complex relationships between entities, such as the connections between users, their interactions, and shared content, making them an ideal choice for such applications.

This course focuses on Graph Data Management, covering the fundamentals of creating, organising, querying, and deriving insights from graph-structured data. With the exponential growth of interconnected data sources, the need for skilled professionals who can navigate graph data, construct knowledge graphs, leverage graph databases, perform graph analytics, and contribute to the emerging field of Graph Data Science has become increasingly evident. This course equips students with the foundational knowledge and skills required to work with graph data.

This course encompasses topics such as graph data concepts and representations, building and constructing knowledge graphs, exploring graph databases and their applications, graph querying, graph analytics.

All answers are assessed by two examiners. An external examiner is used regularly, at a minimum of every third completion of the course. When selecting answers for external evaluation, a minimum of 10 percent of the answers shall be included, with no fewer than 5 answers. The external examiner’s assessment of the selected answers shall benefit all students.

No formal requirements over and above the admission requirements.

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student has:

• understanding of graph data and its characteristics
• familiarity with constructing and modelling knowledge graphs
• awareness of graph analytics techniques

Skills

The student can:

• use graph databases for efficient data storage and retrieval
• create, manipulate, and manage graph data
• design and implement graph-based data management processes
• employ graph analytics for complex data exploration

General competence

The student can:

• optimise data organisation through graph-based representations
• evaluate and select appropriate graph-based solutions for various tasks
• articulate the significance of graph data management in diverse domains.

This is a project-based course, divided into two parts, with focus on basics of graph data management at the beginning and student project work towards the end. The forms of teaching will include lectures, group discussions, and project work.

The student must have passed all the courses from the first year of study and may lack a maximum of one passed course from the second year.

After completing the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• has knowledge of research work in a selected area in the field of pharmacy
• can describe key scientific methods and different research designs
• is familiar with the strengths and weaknesses of the research method applied in his/her own project
• can explain research ethics aspects of his/her own project

Skills

The student

• can apply research-based knowledge to plan and carry out a methodical and academic project in cooperation with others
• can obtain relevant subject matter and integrate it with his/her own knowledge of the field in his/her project work
• can describe, analyse and discuss the research question and results of the project in relation to other published scientific work
• can carry out independent source searches and use references and source references in a consistent and correct manner
• can discuss, assess and argue for his/her own opinions by taking on the role of both opponent and respondent

General competence

The student

• can combine academic knowledge and relevant results from research and development work in a written thesis
• can reflect orally and in writing on academic and scientific issues

The following must have been approved in order for the student to take the exam:

• Minimum attendance of 80% at concluding seminars with oral presentations

1) A written thesis in groups of 3-6 students. For groups of 3-4 students, the scope of the thesis should be approx. 10,000 words (+/- 10%) and for groups of 5-6 students, approx. 12,000 words (+/- 10%). The thesis can be written in either English or Norwegian.

2) Individual oral exam of up to 30 minutes Students can take the oral exam in either English or Norwegian.

The written thesis must be awarded a grade of A-E in order for the student to take the oral exam. A preliminary grade is awarded for the written thesis. The final grade is set after the oral exam. The grade can be adjusted up or down by one grade based on the oral exam.

Students can appeal the grade awarded for the written part of the exam.

Resit exam: If the bachelor’s thesis is awarded the grade F (fail), the student will be given one (1) opportunity to submit a reworked version.

All aids are permitted for the written part of the exam. The student can bring a copy of his/her bachelor’s thesis to the oral exam.

Grade scale A-F