Advanced Computer Science: Advanced Web Technologies [MSc]

Querying Data on the Web

Unit code: COMP62421
Credit Rating: 15
Unit level: Level 6
Teaching period(s): Semester 1
Offered by School of Computer Science
Available as a free choice unit?: Y



Additional Requirements

The formal requirement is the attendance on Modelling Data on the Web.

However, it is strongly recommended that the student attended a previous course on fundamentals of databases. Some of the activities (assessments) will require programming skills.


The aim of this course is to provide the conceptual and practical foundations for building and optimizing systems which require accessing large-scale and heterogeneous data.


Given the changing landscape of computing towards a predominance of data-centric/data-intensive approaches in both scientific and industrial contexts, organising and querying data is set to become a primary concern in the construction of contemporary systems. The advance of Artificial Intelligence and Data Analysis applications and their requirement to process large-scale and heterogeneous data, creates the demand to build systems which can efficiently query and operate over this data.

This course unit aims to enable students to have a principled and critical understanding of contemporary mechanisms to support efficient access to large-scale and heterogeneous data. The course is organised will around the challenges present on processing different types of data on the Web (Tabular, Tree-shaped, Graph and Document-based), to cover the fundamental algorithms and data structures present “under the hood” of database systems.

Teaching and learning methods

The course is structured into 5 full-day lectures and lab sessions. Formative and summative assessments will be performed during the lectures. Some lectures will require active student engagement on the TLAs (e.g. work along exercises, changing activities, quizes).

Summative assessments consists of:

  • One closed-book 2 hour written exam
  • 5 quizes, 2 essays and 5 weekly exercises including problem-solving lab work

Some exercises might involve lightweight programming tasks.

Learning outcomes

By the end of the course, students will be able to:

  1. Describe and differentiate different types of databases and their supporting querying syntax.
  2. Describe and differentiate query processing approaches for different types of data (Tabular, Tree-shaped, Graph, Document-based).
  3. Apply and evaluate query optimization strategies.
  4. Explain how different algorithms and data structures affect query performance for different types of data.
  5. Argue, contrast and compare different architectures and query optimisation strategies.
  6. Demonstrate and program queries over different databases.
  7. Analise a new data management situation and design the appropriate methods for it.

Employability skills

  • Analytical skills
  • Problem solving
  • Research
  • Written communication

Assessment methods

  • Written exam - 50%
  • Written assignment (inc essay) - 50%


[Day 1]

Introduction to the Course Unit

Relational Query Processing (1 of 2)

            The Architectural Paradigm for Query Processing Systems

            The Relational Model of Data

            The Relational Calculi and Algebra

            The SQL Language


[Day 2]

Relational Query Processing (2 of 2)

            Logical Optimization

            Physical Optimization

            Classical Query Execution

            Parallel Query Execution


Query Processing Using XQuery

            Motivation for the Language

            Example Capabilities

            Compilation, Optimization, Evaluation



[Day 3]

Massively-Parallel Schemes




       Consistency and Consensus


NOSQL Databases

          Key-Value Store

          Document-based Store

          Column-based Store

The Map-Reduce Model

Query Processing with Map-Reduce


[Day 4]

Graph Databases


Query Processing Using SPARQL

            Example Capabilities

            Compilation, Optimization, Evaluation



[Day 5]

Contemporary Data-Intensive Architectures and Tools

Batch Processing, Stream Processing, Lambda/Kappa Architectures

Data Streams & Event-Centric Platforms

From Query to Machine Learning Pipelines

Supporting Frameworks: Kafka, Spark, Flink

Databases of the Future: Blockchain and AI applications

Recommended reading

There is no single textbook, or set which covers the course content. Books that are moderately useful for the course include:


Database systems: the complete book (2nd edition).

Hector Garcia-Molina, Jeffrey D. Ullman and Jennifer Widom.

ISBN 9780131354289.

Pearson, 2008.


Database system concepts (6th edition).

Abraham Silberschatz, Henry F. Korth and S. Sudarshan.

ISBN 9780071289597.

McGraw-Hill, 2010.


Database management systems (3rd edition).

Raghu Ramakrishnan and Johannes Gehrke.

ISBN 9780071231510.

McGraw-Hill, 2002.


Next Generation Databases: NoSQL, NewSQL, and Big Data

by Guy Harrison , 2015


Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, 2017

Martin Kleppmann


Introduction to Information Retrieval

Christopher D. Manning and Prabhakar Raghavan


A Semantic Web Primer.

Grigoris Antoniu, Paul Groth et al.

MIT Press, 2012.

Feedback methods

Coursework is assigned and lab sessions provide an opportunity for interaction. Coursework is marked offline with feedback given in writing. Lab sessions allow students to discuss the written feedback in more depth with the marker. The course unit will use the standard tools available in virtual learning environments for hints, tips, discussions, etc.

Study hours

  • Lectures - 25 hours
  • Practical classes & workshops - 10 hours
  • Independent study hours - 115 hours

Teaching staff

Alvaro Fernandes - Unit coordinator

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