Module Code: H9SSP
Long Title Scalable Systems Programming
Title Scalable Systems Programming
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Horacio Gonzalez-Velez
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science or cognate discipline. May have industry experience also.


Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate in-depth knowledge of parallel algorithms on large amounts of data
LO2 Identify and categorise search techniques including similarity search and search engine technologies.
LO3 Critically compare and contrast different data-stream processing and specialised algorithms.
LO4 Critically analyse mining and clustering algorithms on large multi-dimensional datasets.
LO5 Develop and implement efficient programming solutions for problems relating to processing data at scale.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline


Module Content & Assessment

Indicative Content
MapReduce Extensions
Recursive and workflow systems for MapReduce. Resilient data sets.
MapReduce Cost Models
Complexity and cost models for MapReduce with emphasis on communication costs and task networks
Near Neighbour search and Shingling
Collaborative filtering and similarity sets. Document shingling and sub-strings.
Locality-sensitive hashing and distance measures. Additional methods for higher degrees of similarity.
Stream Data Model
Stream sources, stream queries, and processing. Sampling data
Streams Operations
Filtering, counting, combining and estimating.
Stream Processing
Building complex pipelines and models
Link Analysis
PageRank algorithm in its application to search engines. Efficient computation of PageRank. Link Spam. Hubs and authorities.
Frequent itemsets
Market-Basket Model, many-to-many relationships. Association rules.
A-Priori / Limited Pass Algorithms
Determine stages, sets and items under different monotonicity conditions.
Clusters for Streams and Parallelism
Bucket initialisation and merging. Parallel clustering.
Using Scalable Services
Deploying concurrent stream processing and batch processing pipelines
Assessment Breakdown%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
This practical assessment will evaluate the learners’ knowledge and understanding of Scalable Systems Programming, possibly in the context of mining and/or clustering algorithms.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Reassessment of this module will be via project

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 24 Every Week 24.00
Independent Learning Independent learning (hours) 77 Every Week 77.00
Total Weekly Contact Hours 48.00

Module Resources

Recommended Book Resources
  • Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman. (2014), Mining of Massive Datasets, Cambridge University Press, p.476, [ISBN: 1107077230].
  • Martin Kleppmann. (2017), Designing Data-intensive Applications, Oreilly & Associates Incorporated, p.590, [ISBN: 1449373321].
Supplementary Book Resources
  • Andrew Kelleher,Adam Kelleher. (2018), Machine Learning in Production, [ISBN: 9780134116556].
  • K. Hwang. (2017), Cloud and Cognitive Computing: A Machine Learning Approach, MIT Press, [ISBN: 10026203641X].
  • B. Chambers, M. Zaharia. (2018), Spark - The Definitive Guide, 1st. O′Reilly Media, [ISBN: 101491912219].
  • Tom White. Hadoop: the Definitive Guide ; Storage and Analysis at Internet Scale, [ISBN: 1491901632].
Recommended Article/Paper Resources
  • B. Veloso, F. Leal, H. González-Vélez, B. Malheiro, J-C. Burguillo. (2018), Scalable data analytics using crowdsourced repositories and streams, Journal of Parallel and Distributed Computing, 122, p.1-10.
  • J. Eckroth. (2018), A course on big data analytics, Journal of Parallel and Distributed Computing, 118, p.166.
  • J. Kolodziej, H. González-Vélez, H.D. Karatza. (2017), High-performance modelling and simulation for big data applications, Simulation Modelling Practice and Theory, 76, p.1-2.
  • J. Dean, S. Ghemawat. (2010), MapReduce: a flexible data processing tool., Commun. ACM, 53(1), p.72-77.
This module does not have any other resources
Discussion Note: