Admission requirements
Assumed/recommended prior knowledge
The course builds on standard (relational) database concepts, techniques, and algorithms --- including Entity-Relationship model, Relational Data model, Relational algebra, SQL, transaction management, storage formats, access structures (indexes), and client-server (software-)architectures - as usually tought in bachelor's level database courses. Familiarity with these, as well as with using database managements systems (DBMS) in practice, are assumed for this course. (Pointers for self-study material to refresh are provided, but go beyond the envisioned workload of this course.)
This course does not (only) aim at learning to use existing systems, but rather (predominantly) at learning to build (parts of) data management and analysis systems. Consequently, programming experience in system-oriented programming languages like C or C++ is also assumed.
Students will use their own desktop or laptop computers for homework / assignments as well as for occasional hands-on sessions during classes. Students are assumed be familiar with installing open-source software on their own computer(s).
Description
Going beyond standard "textbook" (relational) database concepts and techniques --- see "Assumed/recommended prior knowledge" above ---, the course discusses state-of-the-art advanced data management concepts and techniques --- including storage models, data structures, algorithms, hardware-conscious implementation techniques, and overall data management system architectures --- to facilitate efficient and scalable analysis of large amounts of data ("Big Data"). Most of these concepts and techniques form the basis for leading analytical data management systems, both commercial and open-source.
The course material is based on recent tutorials and publications at leading international scientific venues (journals and conferences).
Implementing selected parts / components of data management and analysis systems is part of the course, mainly as homework / assignments; partly also via occasional hands-on sessions during classes.
Course objectives
The course will teach advanced data management concepts techniques --- including storage models, data structures, algorithms, hardware-conscious implementation techniques, and overall data management system architectures --- to facilitate efficient and scalable analysis of large amounts of data ("Big Data"). Implementing selected parts / components of data management and analysis systems is part of the course.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures
Assignments / Implementation projects
Reports
Literature studies including presentations and discussions of selected literature
Course load
Total hours of study: 168 hrs. (= 6 EC)
Lectures: 26:00 hrs.
Practical work/assignments: 72:00 hrs.
Self-study: 70:00 hrs.
Assessment method
Homework assignments (programming, experimentation/evaluation, written report), both individually as well as in small groups.
The final grade is the average (using equal weights) of the grades of four (4) assignments, i.e., each assignment accounts for 25% (1/4) of the final grade.
The teacher will inform the students how the inspection of and follow-up discussion of the assignments will take place.
Reading list
A reading list of background and accompanying literature will be provided and discussed during the first lecture.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: prof.dr. S. Manegold
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.