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NOTE: Course offerings are subject to budgetary approval
Posting Reference: CS CS4545 FR01B
| Course Prefix: | CS | Course Number: | CS4545 |
| Course Section: | FR01B | Course Name: | Big Data Systems |
| Term: | Winter | Campus: | Fredericton |
| Faculty: | Computer Science, Faculty of | Department: | Computer Science |
| Position Start Date: | 2026-01-01 | Position End Date: | 2026-04-30 |
| Posting Date: | 2025-05-28 | Application Deadline: | 2025-06-22 |
| Applications to be submitted via: | fcs@unb.ca | ||
| Requested By: | Kelley Nelson |
| Days: | T,TH | Time: | 11:30-12:50 |
| Number of Positions Available: | 1 | Total Credit Hours: | 3 |
| Mode of Delivery: | Face to Face | Location: | On Campus |
| Stipend Amount: | $7193 |
Data systems are going through a major transition due to the challenges of Big Data processing. The outcome of this shift is the emergence of a new breed of systems that can handle data at massive scales. This course presents some of these systems, along with the principles of query processing. Specifically, it compares Relational vs. NoSQL data models and covers the foundations of query processing, including index-based access and join processing. It presents the principles of parallel databases, and explores batch processing frameworks, as well as iterative processing frameworks. It also covers SQL interfaces over these frameworks. It introduces update-intensive systems and graph data stores. It includes the special topics of spatial and spatio-temporal data processing.
Requisites:
Take CS*1103 or CS*2545 and 75 credits
Take CS*3543 - Recommended prior to taking this course, but is not required.
Data systems are going through a major transition due to the challenges of Big Data processing. The outcome of this shift is the emergence of a new breed of systems that can handle data at massive scales. This course presents some of these systems, along with the principles of query processing. Specifically, it compares Relational vs. NoSQL data models and covers the foundations of query processing, including index-based access and join processing. It presents the principles of parallel databases, and explores batch processing frameworks, as well as iterative processing frameworks. It also covers SQL interfaces over these frameworks. It introduces update-intensive systems and graph data stores. It includes the special topics of spatial and spatio-temporal data processing.
Requisites:
Take CS*1103 or CS*2545 and 75 credits
Take CS*3543 - Recommended prior to taking this course, but is not required.
Ph.D. or Master degree in Computer Science. Demonstrated knowledge of the subject matter and relevant experience with teaching or professional presentations .Professional IT-related experience is a strong plus and teaching experience (DUT certificate is an asset).