Welcome to Design and Query Processing in Specialized Data Management Systems
Description: Relational Database Management Systems have been applied in many different domains and build the foundation of every larger software application stack. However, highly specific applications and use cases often require more tailored systems along with a non-relational data model and use-case specific query language. Within this course, we will identify such use-cases, discuss the requirements, and investigate typical architecture properties and query capabilities of four different types of systems: key-values stores with eventual consistency, document stores for JSON data, property graph database systems, and timeseries data management systems.
Prerequisites: A background in computer science is assumed. In particular, the participants should have knowledge about and experience with relational database management systems. Further, the participants should have programming experience (Java/Python).
Learning objectives: The objective of the course is to provide students with a comprehensive understanding of the key characteristics of workloads and data models that favor specialized data management systems. Students will gain hands-on experience with four different use-cases demanding different types of data management systems. The goal is to equip students with the knowledge to effectively leverage specialized data management systems (e.g. time series systems) in their own research activities.
Organizer: Wolfgang Lehner
Lecturers: Wolfgang Lehner
ECTS: 2 ECTS
Time: September 15 - 17, 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: August 25, 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Relational Database Management Systems have been applied in many different domains and build the foundation of every larger software application stack. However, highly specific applications and use cases often require more tailored systems along with a non-relational data model and use-case specific query language. Within this course, we will identify such use-cases, discuss the requirements, and investigate typical architecture properties and query capabilities of four different types of systems: key-values stores with eventual consistency, document stores for JSON data, property graph database systems, and timeseries data management systems.
Prerequisites: A background in computer science is assumed. In particular, the participants should have knowledge about and experience with relational database management systems. Further, the participants should have programming experience (Java/Python).
Learning objectives: The objective of the course is to provide students with a comprehensive understanding of the key characteristics of workloads and data models that favor specialized data management systems. Students will gain hands-on experience with four different use-cases demanding different types of data management systems. The goal is to equip students with the knowledge to effectively leverage specialized data management systems (e.g. time series systems) in their own research activities.
Organizer: Wolfgang Lehner
Lecturers: Wolfgang Lehner
ECTS: 2 ECTS
Time: September 15 - 17, 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: August 25, 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Wolfgang Lehner
Welcome to Urban Data Management, Representation and Mining
Description: Urban computing is an emerging field that aims to address the challenges of rapid urbanization, such as pollution, energy consumption, and traffic congestion. Urban computing involves acquiring, integrating, and analyzing large and heterogeneous data generated by a variety of sources in urban spaces, including sensors, devices, vehicles, buildings, and people, to tackle major urban challenges.
The course will introduce
1) urban data management, including urban data, spatial data indexing, spatial data query processing, and learned spatial indexes;
2) urban data mining, including spatial data mining, spatiotemporal prediction, and reinforcement learning;
3) geospatial entity representation for point objects, trajectories, and regions and their applications, including speed inference, region population estimation, etc.;
4) foundation models for geospatial applications.
Prerequisites: Bachelor’s and master’s degrees in computer science or software engineering, including knowledge on machine learning and data management as introduced in typical undergraduate courses.
Learning objectives: The objective of the course is to provide students with a working understanding of basic knowledge, as well as research problems and solutions of urban computing.
Organizer: Christian S. Jensen
Lecturers: Professor Gao Cong, Nanyang Technological University, Singapore
ECTS: 2 ECTS
Time: June 19 - 20, 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: May 29, 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Urban computing is an emerging field that aims to address the challenges of rapid urbanization, such as pollution, energy consumption, and traffic congestion. Urban computing involves acquiring, integrating, and analyzing large and heterogeneous data generated by a variety of sources in urban spaces, including sensors, devices, vehicles, buildings, and people, to tackle major urban challenges.
The course will introduce
1) urban data management, including urban data, spatial data indexing, spatial data query processing, and learned spatial indexes;
2) urban data mining, including spatial data mining, spatiotemporal prediction, and reinforcement learning;
3) geospatial entity representation for point objects, trajectories, and regions and their applications, including speed inference, region population estimation, etc.;
4) foundation models for geospatial applications.
Prerequisites: Bachelor’s and master’s degrees in computer science or software engineering, including knowledge on machine learning and data management as introduced in typical undergraduate courses.
Learning objectives: The objective of the course is to provide students with a working understanding of basic knowledge, as well as research problems and solutions of urban computing.
Organizer: Christian S. Jensen
Lecturers: Professor Gao Cong, Nanyang Technological University, Singapore
ECTS: 2 ECTS
Time: June 19 - 20, 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: May 29, 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Christian S. Jensen
Welcome to Distributed Data Processing with Dataflow Systems (2025)
Description: In today’s world, data is at the heart of decision-making processes across various domains. Dataflow is a programming paradigm and execution model that underpins many modern distributed data processing systems. In this model, developers create programs by defining sequences of functional transformations on input data. The system runtime then manages the execution of these programs across distributed computing infrastructures, abstracting away complexities related to development, distribution, communication, and fault tolerance.
This course delves into the fundamental concepts of dataflow systems, covering both programming models and implementation details. Starting with basic constructs for analyzing static and streaming data, the course progresses to more advanced topics such as iterations, time-based computations, and user-defined functions. We will explore and compare different approaches to implementing these constructs, highlighting their respective advantages and disadvantages.
Throughout the course, students will engage with examples from modern dataflow systems and participate in hands-on sessions to complement the theoretical notions.
Prerequisites: Familiarity with Java
Learning objectives:
On successful completion of this course, students will be expected to be able to:
1. Gain a comprehensive understanding of the dataflow paradigm, its significance in distributed data processing systems and the use cases where it can be used.
2. Design and implement dataflow programs that efficiently process large volumes of data in real-time. Master both basic constructs for static and streaming data analysis and advanced topics such as iterations, time-based computations, and user-defined functions.
3. Evaluate dataflow systems, understand the various performance metrics, design and execute sound experiments.
4. Compare the existing dataflow frameworks, understanding the relative advantages and disadvantages.
Organizer: Daniele Dell'Aglio
Lecturers: Alessandro Margara, Politecnico di Milano
ECTS: 2.0
Time: 9 - 10 June 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 25
Deadline: 19 May 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: In today’s world, data is at the heart of decision-making processes across various domains. Dataflow is a programming paradigm and execution model that underpins many modern distributed data processing systems. In this model, developers create programs by defining sequences of functional transformations on input data. The system runtime then manages the execution of these programs across distributed computing infrastructures, abstracting away complexities related to development, distribution, communication, and fault tolerance.
This course delves into the fundamental concepts of dataflow systems, covering both programming models and implementation details. Starting with basic constructs for analyzing static and streaming data, the course progresses to more advanced topics such as iterations, time-based computations, and user-defined functions. We will explore and compare different approaches to implementing these constructs, highlighting their respective advantages and disadvantages.
Throughout the course, students will engage with examples from modern dataflow systems and participate in hands-on sessions to complement the theoretical notions.
Prerequisites: Familiarity with Java
Learning objectives:
On successful completion of this course, students will be expected to be able to:
1. Gain a comprehensive understanding of the dataflow paradigm, its significance in distributed data processing systems and the use cases where it can be used.
2. Design and implement dataflow programs that efficiently process large volumes of data in real-time. Master both basic constructs for static and streaming data analysis and advanced topics such as iterations, time-based computations, and user-defined functions.
3. Evaluate dataflow systems, understand the various performance metrics, design and execute sound experiments.
4. Compare the existing dataflow frameworks, understanding the relative advantages and disadvantages.
Organizer: Daniele Dell'Aglio
Lecturers: Alessandro Margara, Politecnico di Milano
ECTS: 2.0
Time: 9 - 10 June 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 25
Deadline: 19 May 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Daniele Dell'Aglio
Welcome to Trusted AI methods for sequential decision making (2025)
Description: The theme of this course is automatic reasoning and learning, with a focus on decision making and in particular sequential decision making. These techniques are relevant whenever repeated interaction with an environment is required. As an example, consider the task of moving a robot from A to B. While a complete path can be statically computed, if the environment is dynamic and uncertain, the planned path can become inaccessible. Thus, on-the-fly adaptation is required.
This course will introduce a number of algorithmic solutions such as planning and safe reinforcement learning, explaining the algorithmic principles behind several offline and online sequential decision-making approaches.
The course will consist of several lectures as well as some hands-on exercises.
Prerequisites: You will need background in a mathematical discipline; in particular, you should have attended a course covering propositional logic. You also need basic programming experience (read and write simple computer programs) and familiarity with standard algorithms like search algorithms. Basic familiarity with machine learning is useful but not required.
Learning objectives: The students are familiar with algorithmic solutions to sequential decision making.
Key literature: TBA
Organizer: Christian Schilling
Lecturers: Alvaro Torralba and Christian Schilling
ECTS: 2.0
Time: 3, 4, 7, 10 and 11 March 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 10 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: The theme of this course is automatic reasoning and learning, with a focus on decision making and in particular sequential decision making. These techniques are relevant whenever repeated interaction with an environment is required. As an example, consider the task of moving a robot from A to B. While a complete path can be statically computed, if the environment is dynamic and uncertain, the planned path can become inaccessible. Thus, on-the-fly adaptation is required.
This course will introduce a number of algorithmic solutions such as planning and safe reinforcement learning, explaining the algorithmic principles behind several offline and online sequential decision-making approaches.
The course will consist of several lectures as well as some hands-on exercises.
Prerequisites: You will need background in a mathematical discipline; in particular, you should have attended a course covering propositional logic. You also need basic programming experience (read and write simple computer programs) and familiarity with standard algorithms like search algorithms. Basic familiarity with machine learning is useful but not required.
Learning objectives: The students are familiar with algorithmic solutions to sequential decision making.
Key literature: TBA
Organizer: Christian Schilling
Lecturers: Alvaro Torralba and Christian Schilling
ECTS: 2.0
Time: 3, 4, 7, 10 and 11 March 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 10 February 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Christian Schilling
- Teacher: Alvaro Torralba