COMP62421 Querying Data on the Web syllabus 2020-2021
This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Current students should see Blackboard/course unit related emails for any further updates.
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.
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.
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)
Classical Query Execution
Parallel Query Execution
Query Processing Using XQuery
Motivation for the Language
Compilation, Optimization, Evaluation
Consistency and Consensus
The Map-Reduce Model
Query Processing with Map-Reduce
Query Processing Using SPARQL
Compilation, Optimization, Evaluation
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
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.
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.
- Lectures (25 hours)
- Practical classes & workshops (10 hours)
- Analytical skills
- Problem solving
- Written communication
On successful completion of this unit, a student will be able to:
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.
|Learning SPARQL : querying and updating with SPARQL 1.1||DuCharme, Bob.||9781449313616 (e-book); 1449313612 (e-book)||Sebastopol California ; O'Reilly||2011.|
Course unit materials
Links to course unit teaching materials can be found on the Department of Computer Science website for current students.