Fall 2021

Data Science Seminar: Time Series Operators

Lecturers: Mourad Khayati, Alberto Lerner 

Teaching language: English

Level: MSc students

Academic year: Fall 2021



Evaluation and Expectations


List of Papers


The seminar on data science involves presentations that cover recent topics on data science. The area of this year’s seminar is time series operators.  Modern SQL databases do not strictly support time series, but two features come close. First, SQL:2011 introduced extensions to support temporal data. Chief among the extensions is the concept of periods, which allows a time interval to be associated with every row of a table. Second, the older SQL:1999 introduced the notion of window functions. These allow data to be manipulated as if it was a sequence.

In the scope of this seminar, we investigate papers that describe algorithms and operators to process time series data. The papers explore mechanisms that highlight the strengths and weaknesses of these operators when applied to time series.


The goal for the students is to learn how to critically read and study research papers, how to describe a paper in a report, and how to present it in a seminar. Under supervision, students will select one paper to study and compare with related work. This seminar aims to help students to gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the time series field in the form of both a presentation, a written report, and an empirical evaluation.

IMPORTANT NOTE: The papers will be distributed on a first come first serve basis.

Evaluation and Expectations

The final grade depends on the quality of the report, presentation, reproducibility experiments, and active participation during the seminar. Each participant prepares a self-contained report of min 6 pages and gives a presentation of 30 minutes. The report should describe in detail the proposed technique(s). The report might contain a small running example, counterexample(s) if any, and should explore the extreme cases where the proposed approach would perform best and worst. The reproducibility part consists in reproducing the same set of experiments introduced in the paper using a different setup (dataset, metric, parameters, etc.).

Advice on how to:

IMPORTANT NOTE: Attendance is mandatory for the two-class seminar sessions. The total number of participants will be limited to 10.


Kickoff Meeting. Date: Tue, 28.09.2021, 15:00-17:00, room: PER 21 F207

Setup and organization of the seminar, and paper assignment


Date: Tue, 30.11.2021
Report deadline

Date: Tue,  07.12.2021, all day, room: TBD

Office meeting with students

First Seminar Session. Date: Tue, 21.12.2021, time: 13.00, room: C421



Date: Tue, 11.01.2022
Deadline final Report

Paper Assignment

The papers will be distributed on a first come first serve basis. To select one paper from the list of papers, please use the following link.

Paper & code

Presentation Date



Efficient Learning Interpretable Shapelets for Accurate Time Series Classification. ICDE 2018. Code: https://github.com/House1993/ELIS


Stefan Kissmann

Mourad Khayati

Building Advanced SQL Analytics From Low-Level Plan Operators, SIGMOD 2021. Code: on-demand


Alain Schaller

Alberto Lerner