MSc ACS: Data and Knowledge Management
Year of entry: 2023
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Course unit details:
Querying Data on the Web
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 1|
|Available as a free choice unit?||Yes|
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.
|Unit title||Unit code||Requirement type||Description|
|Modelling Data on the Web||COMP60411||Pre-Requisite||Compulsory|
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.
By the end of the course, students will be able to:
- Describe and differentiate different types of databases and their supporting querying syntax.
- Describe and differentiate query processing approaches for different types of data (Tabular, Tree-shaped, Graph, Document-based).
- Apply and evaluate query optimization strategies.
- Explain how different algorithms and data structures affect query performance for different types of data.
- Argue, contrast and compare different architectures and query optimisation strategies.
- Demonstrate and program queries over different databases.
- Analise a new data management situation and design the appropriate methods for it.
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
Relational Query Processing (2 of 2)
- Logical Optimization
- Physical Optimization
- Classical Query Execution
- Parallel Query Execution
- Querying XML Data
- XML Query Processing
- NoSQL Databases
- NoSQL Rules and Features
- Graph data management
- Querying with SPARQL
- Optimizing SPARQL
- Evaluating SPARQL
Parallelism and Big Data
- Parallel Query Processing
- Parallel Relational Databases
Data Intensive Systems: Patterns and Trends
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 exam
- Quizzes and lab work
Some exercises might involve lightweight programming tasks.
- Analytical skills
- Problem solving
- Written communication
|Written assignment (inc essay)||50%|
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.
|Scheduled activity hours|
|Practical classes & workshops||10|
|Independent study hours|
|Norman Paton||Unit coordinator|