The Deconstructed Cloud Databases project stems from a simple question: What are the minimum components required to build a data management system in the cloud? Our motivation for this project is based on the idea that reducing a system to its minimum set of components makes it easier to build, test, and maintain cloud data management systems. This approach requires less engineering effort, …
Supervisors: Martin Hentschel
Semester: Fall 2024
Tags: data management, performance, benchmarking, hacking

The Deconstructed Cloud Databases project stems from a simple question: What are the minimum components required to build a data management system in the cloud? Our motivation for this project is based on the idea that reducing a system to its minimum set of components makes it easier to build, test, and maintain cloud data management systems. This approach requires less engineering effort, …
Supervisors: Martin Hentschel
Semester: Fall 2024
Tags: data management, security, open source, open standards

Observing how well machine learning systems utilize hardware resources is a crucial preliminary step to improve system performance and reduce hardware waste. To do such observations, one has to collect a lot of monitoring data on hardware behavior through experiments. In our group, we have recently built a framework to aid the management of such monitoring data efficiently, called Resource-Aware …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: benchmarking, data management, data visualization

In this project, we would specifically like to quantify the data movement savings of applying techniques like compression and model-based data filtering in the context of resource-constrained hardware and edge/IoT applications. Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Processing the data on …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML

One of the key challenges with enabling efficient machine learning on resource-constrained devices is keeping the machine learning models deployed on these devices up-to-date without frequent retraining. This requires exploring the impact of different model update mechanisms at the edge. This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024. If you are …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML

To enable efficient data processing and machine learning on resource-constrained devices has many challenges. One is fitting the models into the restrictive memory and compute resources of these devices. In this project, first, we would like to explore the landscape of foundational, generative-AI, language, etc. models with respect to their size and compute needs to understand what could be a fit …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, ML model updates, tinyML

Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more …
Supervisors: Pınar Tözün, Robert Bayer
Semester: Fall 2024
Tags: resource-constrained hardware, data management, resource management, tinyML

Observing how well data-intensive systems utilize hardware resources is a crucial preliminary step to improve system performance and reduce hardware waste. To do such observations, one has to collect a lot of monitoring data on hardware behavior through experiments. In our group, we have recently built a framework to aid the management of such monitoring data efficiently, called Resource-Aware …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2023
Tags: benchmarking, data management, data visualization

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy