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.
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
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
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Contact
Lecturer: prof.dr. S. Manegold
Remarks
None.