COMPSCI 752 : Big Data Management


2020 Semester One (1203) (15 POINTS)

Course Prescription

Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-vie), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.

Course Overview

Many companies must manage large volumes of diverse data in order to stay competitive. The deep diversity of modern day data requires data scientists to master many technologies that rely on new principles to represent, describe, and access data. The course will provide insight into the rich landscape of big data. The main aim of the course is to prepare students for big data modelling and large-scale data management in distributed and heterogeneous environments. On the one hand, learning the principles of big data management will prepare students for a career as data scientists, independently of continuous technology changes. In particular, learning how to model, query, and integrate big data are necessary skills to get data ready for analytical purposes. For example, MapReduce algorithms such as PageRank illustrate how to efficiently rank billions of Web pages. On the other hand, investigating current big data technologies will demonstrate what is and what is not possible today, but also highlight opportunities for future work. For instance, Spark offers an integrated technology framework for preparing and analysing big data, while the disruptive Blockchain technology exemplifies a distributed computing system with high fault tolerance with application potential that we are only beginning to understand.

Course Requirements

Prerequisite: Approval of the Academic Head or nominee

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities

Learning Outcomes

By the end of this course, students will be able to:
  1. Apply state-of-the-art in representation formalisms for big data, including the eXtensible Markup Language (XML), Graph databases (Neo4j), the Resource Description Framework (RDF), the JavaScript Object Notation (JSON), and NoSQL (Capability 1, 2 and 3)
  2. Model big data with schema languages, including Document Type Definitions (DTDs), XML Schema, Cypher DDL, JSON schema, and RDF schema (Capability 1, 2 and 3)
  3. Access big data with query languages, including XPath, XQuery, Cypher, SPARQL, Hive, Spark SQL (Capability 1, 2 and 3)
  4. Integrate big data with ontologies, including the Web Ontology Language OWL and Knowledge graphs (Capability 1, 2 and 3)
  5. Understand how to manage and analyse big data, including techniques for searching, indexing and processing such as PageRank, MapReduce, Spark and Graph databases (Capability 1, 2 and 3)
  6. Present as a group to fellow students and teachers slides on your joint understanding about the state-of-the-art knowledge on a topic about big data (Capability 1, 2, 4 and 5)
  7. Communicate their individual understanding of the state-of-the-art knowledge on a research topic about big data in the form of a written report, including the fair use of this knowledge in business and society (Capability 1, 2, 3, 4, 5 and 6)


Assessment Type Percentage Classification
Assignment 15% Individual Coursework
Presentation 15% Group Coursework
Report 30% Individual Coursework
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam

Learning Resources

Recommended readings:
  • Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart: Web data management. Cambridge University Press, 2011, ISBN: 9781107012431.
  • Bryce Merkl Sasaki, Joy Chao & Rachel Howard: Graph databases for beginners.
References to research articles and other textbooks will be made available at the beginning of the course

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 30 hours of lectures, a 10 hours of tutorials and industry lectures, 55 hours of reading and thinking about the content and 25 hours of work on assignments, the presentation, and research report.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 15/12/2019 10:16 p.m.