Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
- Zoi Kaoudi
- Pınar Tözün
- Ehsan Yousefzadeh-Asl-Miandoab
- Martin Hentschel
- Yucheng Lu
- Veronika Cheplygina
- Sebastian Büttrich
- Julian Priest
- Ties Robroek
- Robert Bayer
- Amelia Jiménez-Sánchez
- Eike Petersen
- Niclas Hedam
- Philippe Bonnet
- Dovile Juodelyte
- Paul Rosero
- Maria Astefanoaei
- Björn Þór Jónsson
- Aaron Duane
- Niels Ørbæk Chemnitz
- Iman Elghandour
Supervisor: Zoi Kaoudi
PROPOSAL
Learning-to-rank methods for query optimization
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model (typically a regression model) that estimates the runtime of a query …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: machine learning, database, query optimization, ranking
PROPOSAL
Big data processing with Apache Wayang
Are you interested in working with a big data open source project?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For a general overview …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, database, cross-platform data processing, open source, Apache
PROPOSAL
Combine Knowledge Graph Embeddings and Reasoning
Knowledge graphs (KGs) are extensively used in many application domains, such as search engines, product recommendation, and bioinformatics. Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in the above applications. This project will investigate how to loosely-couple the data-driven power of knowledge …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: knowledge graph, LLMs, reasoning
PROPOSAL
MCP server for Apache Wayang
Are you interested in working with a big data open source project and AI?
You are welcome to conduct your thesis/project in the context of Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, AI, LLMs, cross-platform data processing, open source, Apache
PROPOSAL
Reduce energy consumption with Apache Wayang
Are you interested in working with a big data open source project and help the environment?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, database, cross-platform data processing, open source, Apache
PROPOSAL
Training data generation for data management systems
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model that estimates the runtime of a plan. However, to build such a model …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: machine learning, training data, query optimizer
Supervisor: Pınar Tözün
PROPOSAL
Analysis of Checkpointing during Deep Learning Model Training
Deep learning changed the landscape of many applications like computer vision, natural language processing, etc. On the other hand, deep learning require gigantic computing power offered by modern hardware. As a result data scientists rely on powerful hardware resources offered by shared high-performance computing (HPC) clusters or the cloud. Due to the long-running times of deep learning …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, checkpointing, scheduling, resource management
PROPOSAL
Analysis of Predicting GPU Utilization Metrics Using Deep Learning Models and infering number of required SMs
GPU offers massive computational power and parallelism through its Streaming Multiprocessors (SMs). Efficient GPU utilization is critical for maximizing performance and optimizing compute resource usage, which is measured using various metrics such as SMACT (SM Activity) and SMOCC (SM Occupancy), and DRAMA (DRAM Active). These metrics provide insight into how effectively the GPU’s SMs and …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, GPU Utilization, resource management, resource interference
PROPOSAL
BLOX for Deep Learning Task Scheduling with GPU Collocation
Workload collocation has been shown as an effective method to reduce the hardware requirements for certain deep learning (DL) training tasks. On the other hand, there hasn’t been many robust open-source implementations of schedulers that incorporate workload collocation on GPUs for DL.
BLOX is a framework that aims at standardizing the way we implement deep learning schedulers. In this …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, scheduling, resource management, workload collocation
PROPOSAL
Extending GPU Memory Profiling Dataset with Transformer-Based Models
This project focuses on extending an existing dataset for predicting GPU memory requirements during deep learning training by incorporating transformer-based models such as BERT, GPT, and their variants. The student will study the architecture of these models and develop training scripts to run them under controlled conditions.
During training, key GPU metrics—including memory usage, utilization, …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, GPU Memory Requirement, GPU Utilization, resource management
PROPOSAL
Exploiting Edge Devices for Data-Intensive Applications
The work on running data-intensive applications on very powerful, expensive, and power-hungry server hardware is very popular thanks to the growing size of data centers and high-performance computing (HPC) platforms. However, with the rise of new generation internet of things (IoT) applications, the lower-power and lower-budget hardware devices that specifically target IoT, the edge platforms, …
Supervisors:
Pınar Tözün
Semester: Fall 2024
Tags: edge, benchmarking, data-intensive applications, resource-constrained hardware
PROPOSAL
A Framework for Systematic Performance Experiments for Machine Learning
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
PROPOSAL
Data Attribution on Progressive Datasets for Deep Learning
Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data attribution, deep learning, machine learning, resource efficiency
PROPOSAL
Efficient Data Selection Methods for Machine Learning
Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency
PROPOSAL
Flexible Data Placement on SSDs for Database Systems
Traditionally solid-state drives (SSDs) does not give the users the ability to control the data placement on the SSD. This often leads to suboptimal performance and lowers SSD lifetime, since SSDs internally don’t allow in-place updates. The updated disk pages are written elsewhere and the old versions have to be garbage collected. This poses problems if data with different lifetimes and …
Supervisors:
Pınar Tözün
Semester: Fall 2024
Tags: SSDs, data management systems, modern storage
PROPOSAL
How much Data Movement is Being Saved?
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
PROPOSAL
Incremental Model Updates on Tiny Hardware
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
PROPOSAL
Machine Learning Models for the Edge
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
PROPOSAL
Resource Management on Tiny Hardware
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
PROPOSAL
Efficient OS-level Context-Switching for Thread Migration
Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019
PROPOSAL
Leveraging Heterogeneous Hardware
The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019
PROPOSAL
Micro-architectural Analysis of SystemML
Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019
PROPOSAL
What is HTAP?
The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019
PROPOSAL
Workload Characterization for Big Data Management
The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019
Supervisor: Ehsan Yousefzadeh-Asl-Miandoab
PROPOSAL
Analysis of Checkpointing during Deep Learning Model Training
Deep learning changed the landscape of many applications like computer vision, natural language processing, etc. On the other hand, deep learning require gigantic computing power offered by modern hardware. As a result data scientists rely on powerful hardware resources offered by shared high-performance computing (HPC) clusters or the cloud. Due to the long-running times of deep learning …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, checkpointing, scheduling, resource management
PROPOSAL
Analysis of Predicting GPU Utilization Metrics Using Deep Learning Models and infering number of required SMs
GPU offers massive computational power and parallelism through its Streaming Multiprocessors (SMs). Efficient GPU utilization is critical for maximizing performance and optimizing compute resource usage, which is measured using various metrics such as SMACT (SM Activity) and SMOCC (SM Occupancy), and DRAMA (DRAM Active). These metrics provide insight into how effectively the GPU’s SMs and …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, GPU Utilization, resource management, resource interference
PROPOSAL
BLOX for Deep Learning Task Scheduling with GPU Collocation
Workload collocation has been shown as an effective method to reduce the hardware requirements for certain deep learning (DL) training tasks. On the other hand, there hasn’t been many robust open-source implementations of schedulers that incorporate workload collocation on GPUs for DL.
BLOX is a framework that aims at standardizing the way we implement deep learning schedulers. In this …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, scheduling, resource management, workload collocation
PROPOSAL
Extending GPU Memory Profiling Dataset with Transformer-Based Models
This project focuses on extending an existing dataset for predicting GPU memory requirements during deep learning training by incorporating transformer-based models such as BERT, GPT, and their variants. The student will study the architecture of these models and develop training scripts to run them under controlled conditions.
During training, key GPU metrics—including memory usage, utilization, …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2025
Tags: machine learning systems, GPU Memory Requirement, GPU Utilization, resource management
Supervisor: Martin Hentschel
PROPOSAL
Deconstructed Cloud Databases: Atomic Counters
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: Srping 2025
Tags: data management, performance, benchmarking, hacking
Supervisor: Yucheng Lu
PROPOSAL
Predicting Heart Rate from a Face Webcam
This student project aims to develop a method for predicting health metrics, such as heart rate, in real-time using a conventional webcam capturing the user’s face. The project will integrate face detection, image and video signal processing, and spatial-temporal neural networks to estimate heart rate by analyzing subtle color variations in the face caused by blood flow. The final …
Supervisors:
Yucheng Lu
Semester: Spring 2025
Tags: Heart Rate Estimation, Video Analysis, Machin Learning
PROPOSAL
Exploring connections of spectral learning priority and learning task
Spectral learning priority is a useful tool in analyzing a model’s focus during training, it describes how a model may understand a given image from the spectrum perspective. For example, to distinguish cats and tortoises, learning to recognize their shapes would be enough, such embedding will result in higher learning priority at low frequencies representing shapes; while learning to …
Supervisors:
Yucheng Lu, Veronika Cheplygina
Semester: Fall 2024
Tags: Spectral analysis, Image classification, Medical imaging
PROPOSAL
Learning-based image quality enhancement on CubeSat
The DISCO-2 project is driven by students and aims to develop and deploy a 3-unit CubeSat into low Earth orbit. Its mission focuses on conducting Earth observations over Greenland and supporting various research objectives. The satellite has three cameras onboard: infrared, wide-angle, and standard (main camera). Due to the limitations of the imaging hardware and the challenging conditions on the …
Supervisors:
Yucheng Lu, Julian Priest
Semester: Fall 2024
Tags: Image enhancement, Image processing, Machine learning
Supervisor: Veronika Cheplygina
PROPOSAL
Data representativity, similarity, and diversity
Machine learning methods are often evaluated on benchmark datasets, in computer vision, medical imaging, NLP and other fields. In such evaluation, researchers often describe the data as being:
representative, for example based on the distribution of ages of the patients mirroring the world population, similar, for example because both dataset contain pictures of animals diverse, for example …
Supervisors:
Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, meta-research
PROPOSAL
Exploring connections of spectral learning priority and learning task
Spectral learning priority is a useful tool in analyzing a model’s focus during training, it describes how a model may understand a given image from the spectrum perspective. For example, to distinguish cats and tortoises, learning to recognize their shapes would be enough, such embedding will result in higher learning priority at low frequencies representing shapes; while learning to …
Supervisors:
Yucheng Lu, Veronika Cheplygina
Semester: Fall 2024
Tags: Spectral analysis, Image classification, Medical imaging
PROPOSAL
Debiasing medical image datasets to improve machine learning robustness and fairness
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
Supervisor: Sebastian Büttrich
PROPOSAL
Extreme networking
This is not a single project, but rather a larger cluster of potential projects in the field of what could be summarized as extreme networking.
The networks we are interested in are typically wireless, and can be extreme in different senses of the word:
distance - hundreds of kilometers terrestrial, 10,000s of km to satellite latency - sub-ms latencies autonomy - off-grid quality - extreme remote …
Supervisors:
Sebastian Büttrich
Semester: Fall 2025
Tags: network, IoT, LoRa, LoRaWAN, satellites
PROPOSAL
Cubesat LoRa module
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embedded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Sebastian Büttrich
Semester: Fall 2024
Tags: satellites, LoRa, cubesat, IoT, embedded, electronics
PROPOSAL
Innovative Satellite LoRa use cases
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embedded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Sebastian Büttrich
Semester: Fall 2025
Tags: IoT, LoRa, LoRaWAN, satellites
PROPOSAL
Machine learning on optical fiber sensor data
Optical fiber is the backbone of the internet’s communication, e.g. in the form of submarine fiber cables. It can also be employed as a sensor device, by means of combined opto-acoustic methods such as Distributed acoustic sensing (DAS) or State of Polarisation (SoP) sensing. Fiber is cabapble of sensing all kinds of vibrational/acoustic events, from animal sounds over seismic activity to …
Supervisors:
Sebastian Büttrich
Semester: Fall 2025
Tags: fiber, acoustics, audio, machine learning, DAS, SOP
PROPOSAL
Danish Student Cubesat
The Danish Student Cubesat Program is an inter university collaboration that will launch 3 cubesats into Low Earth Orbit over the next 4 years. The satellites will be designed, operated, programmed and built by students and the project offers an opportunity for Master’s students to take part in a live satellite project. ITU is partnering with Aarhus University on DISCOSAT2 which will be an …
Supervisors:
Sebastian Büttrich, Julian Priest
Semester: Fall 2021
Tags: Satellite, Cubesat, Image processing, Machine Learning, edge, constrained computing
PROPOSAL
A GUI for The Things Network LoRaWAN stack v3
The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Mobile Air Quality Monitoring
New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Satellite Data
Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Small Smart City
Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Sound-based Predictive Maintenance
Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk
PROPOSAL
Temperature-based Predictive Maintenance
Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk
PROPOSAL
The ms IoT gateway
Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
The TrekkTracker
In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Wind turbine data publication
Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication
PROPOSAL
Wind turbine electricity production analysis
Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis
PROPOSAL
Wind turbine electricity production prediction
The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning
PROPOSAL
Wind turbine non-invasive instrumentation
The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors
Supervisor: Julian Priest
PROPOSAL
Learning-based image quality enhancement on CubeSat
The DISCO-2 project is driven by students and aims to develop and deploy a 3-unit CubeSat into low Earth orbit. Its mission focuses on conducting Earth observations over Greenland and supporting various research objectives. The satellite has three cameras onboard: infrared, wide-angle, and standard (main camera). Due to the limitations of the imaging hardware and the challenging conditions on the …
Supervisors:
Yucheng Lu, Julian Priest
Semester: Fall 2024
Tags: Image enhancement, Image processing, Machine learning
PROPOSAL
Danish Student Cubesat
The Danish Student Cubesat Program is an inter university collaboration that will launch 3 cubesats into Low Earth Orbit over the next 4 years. The satellites will be designed, operated, programmed and built by students and the project offers an opportunity for Master’s students to take part in a live satellite project. ITU is partnering with Aarhus University on DISCOSAT2 which will be an …
Supervisors:
Sebastian Büttrich, Julian Priest
Semester: Fall 2021
Tags: Satellite, Cubesat, Image processing, Machine Learning, edge, constrained computing
Supervisor: Ties Robroek
PROPOSAL
A Framework for Systematic Performance Experiments for Machine Learning
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
PROPOSAL
Data Attribution on Progressive Datasets for Deep Learning
Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data attribution, deep learning, machine learning, resource efficiency
PROPOSAL
Efficient Data Selection Methods for Machine Learning
Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, we would like to investigate various state-of-the-art data selection mechanisms from a hardware requirements and …
Supervisors:
Pınar Tözün, Ties Robroek
Semester: Fall 2024
Tags: data selection, deep learning, machine learning, resource efficiency
Supervisor: Robert Bayer
PROPOSAL
How much Data Movement is Being Saved?
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
PROPOSAL
Incremental Model Updates on Tiny Hardware
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
PROPOSAL
Machine Learning Models for the Edge
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
PROPOSAL
Resource Management on Tiny Hardware
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
Supervisor: Amelia Jiménez-Sánchez
PROPOSAL
Debiasing medical image datasets to improve machine learning robustness and fairness
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
PROPOSAL
Concept Bottleneck Models to detect hidden features or avoid memorization
Concept Bottleneck Models [1] are designed to leverage high-level concepts. They revisit the classic idea of first predicting concepts that are providing at training time, and then using these concepts to predict the label. By construction, it is possible to intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Spring 2025
Tags: machine learning, data science, medical imaging
PROPOSAL
Medical Visual Question Answering
A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning
Supervisor: Eike Petersen
PROPOSAL
Debiasing medical image datasets to improve machine learning robustness and fairness
It has been observed that deep learning models are able to identify patient characteristics such as age, sex, and self-reported race with high accuracy from medical images such as chest x-ray recordings, even when medical doctors cannot. This raises the potential for such models to learn to (falsely) diagnose patients of different demographics differently, even if they present with the same …
Supervisors:
Amelia Jiménez-Sánchez, Eike Petersen, Veronika Cheplygina
Semester: Fall 2024
Tags: machine learning, data science, medical imaging
Supervisor: Niclas Hedam
PROPOSAL
Reproducibility and Testing of Computational Storage
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
Supervisor: Philippe Bonnet
PROPOSAL
Reproducibility and Testing of Computational Storage
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
PROPOSAL
A GUI for The Things Network LoRaWAN stack v3
The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Computational Storage
Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019
PROPOSAL
Database Performance Characteristics
Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019
PROPOSAL
Decentralized Cloud Management
In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019
PROPOSAL
Mobile Air Quality Monitoring
New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Satellite Data
Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Small Smart City
Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Sound-based Predictive Maintenance
Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk
PROPOSAL
Storage System Performance Characteristics
New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019
PROPOSAL
Temperature-based Predictive Maintenance
Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk
PROPOSAL
The ms IoT gateway
Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
The TrekkTracker
In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
PROPOSAL
Wind turbine data publication
Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication
PROPOSAL
Wind turbine electricity production analysis
Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis
PROPOSAL
Wind turbine electricity production prediction
The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning
PROPOSAL
Wind turbine non-invasive instrumentation
The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors:
Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors
PROPOSAL
FPGA-based Hardware Acceleration
Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD
Supervisor: Dovile Juodelyte
PROPOSAL
Comparing different pre-training strategies for transfer learning in medical imaging
Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such …
Supervisors:
Dovile Juodelyte
Semester: Fall 2023
Tags: transfer learning, deep learning, medical imaging
Supervisor: Paul Rosero
PROPOSAL
Outlier detection in IoT devices
Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems
Supervisor: Maria Astefanoaei
PROPOSAL
15-minutes cities visualisation
The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, visualisation, Python, OSM data
PROPOSAL
Algorithms for data-aware cycling network expansion
As a response to increased traffic congestion and the need to reduce carbon emissions, cities consider ways to modernise, build and extend transit systems. Transit network design solutions can benefit from analysing the large amount of crowd-sourced location data available, which provides valuable insights into population mobility needs. Designing efficient metro lines, bicycle paths, or bus …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, network design, Python, OSM data
PROPOSAL
Graph summaries of accessibility maps
The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph summaries, Python, OSM data
PROPOSAL
Music genre embeddings
Musical genres are inherently ambiguous and difficult to define. Even more so is the task of establishing how genres relate to one another. Yet, genre is perhaps the most common and effective way of describing musical experience. The number of possible genre classifications (e.g. Spotify has over 4000 genre tags, LastFM over 500,000 tags) has made the idea of manually creating music taxonomies …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: scalable algorithms, hyperbolic embeddings, Python, Spotify data
PROPOSAL
Spatiotemporal dependencies in wind energy production
The integration of wind power in the energy grid is dependent on accurate production forecasts. The power output curves between neighbouring wind farms are often correlated temporally and spatially, but currently, these spatiotemporal dependencies are under-utilised in prediction models. Graph neural networks allow for modelling these dependencies. In this project the student will implement a …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph neural networks, Python, timeseries data
Supervisor: Björn Þór Jónsson
PROPOSAL
Diversity of Relevance Feedback
In relevance feedback, the choice of images to present to the user is a difficult problem, as a naïve approach may present too many similar images. The challenge addressed in this project is to ensure diversity (aka “one of each”) as well as relevance. A particularly interesting project for students interested in efficient algorithms.
Read more…
Supervisors:
Björn Þór Jónsson
Semester: Fall 2021
Tags: multimedia analytics, scalability, diversity
PROPOSAL
Hash-Based Indexing for Relevance Feedback
In interactive learning systems, such as Exquisitor, the system presents potentially relevant images to users who label them as either relevant or irrelevant. Currently, Exquisitor uses a cluster-based index, which allows it to return results from a collection of 100 million images in 0.3 seconds. The goal of this project is to study the application of hash-based indexing to interactive learning …
Supervisors:
Björn Þór Jónsson
Semester: Fall 2021
Tags: multimedia analytics, diversity
PROPOSAL
Video Browser Showdown
The goal of this project is to enhance PhotoCube as a competior for the Video Browser Showdown, an international video retrieval competition where competing systems are judged based on speed, accuracy and recall. We propose to develop new versions of the C++-based media server and JS-based media browser, to expand the data model to videos and improve the performance sufficiently to take part in …
Supervisors:
Björn Þór Jónsson
Semester: Fall 2021
Tags: video search, multimedia analytics, photocube
PROPOSAL
Virtual Reality Analytics
We are actively developing a new prototype for analysing large multimedia collections in virtual reality, based on the ObjectCube data model. There are many ways in which students can contribute to the project, including work on the user interface and the back-end, and later on running large-scale user experiments.
Read more…
Supervisors:
Aaron Duane, Björn Þór Jónsson
Semester: Fall 2021
Tags: virtual reality, multimedia analytics
PROPOSAL
Competitor for the Video Browser Showdown
The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students.
The Video Browser Showdown (VBS) is a live competition for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019
PROPOSAL
Diversity of Relevance Feedback
The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience.
The project is suitable for 1-3 well-qualified MSc students.
In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019
PROPOSAL
Exploring Image Collections via Eye Tracking
The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor.
The project is suitable for 1-3 well-qualified MSc students.
Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019
PROPOSAL
Mobile Version of Exquisitor
The goal of this project is build a prototype of the Exquisitor system for mobile devices.
The project is suitable for 1-3 well-qualified MSc students.
Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019
Supervisor: Aaron Duane
PROPOSAL
Virtual Reality Analytics
We are actively developing a new prototype for analysing large multimedia collections in virtual reality, based on the ObjectCube data model. There are many ways in which students can contribute to the project, including work on the user interface and the back-end, and later on running large-scale user experiments.
Read more…
Supervisors:
Aaron Duane, Björn Þór Jónsson
Semester: Fall 2021
Tags: virtual reality, multimedia analytics
Supervisor: Niels Ørbæk Chemnitz
PROPOSAL
Evaluating AutoML for Forecasting Domestic Electricity Data
With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors:
Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data
Supervisor: Iman Elghandour
PROPOSAL
Accelerated Distributed Platform for Spatial Queries
It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019
PROPOSAL
Extending Spark Scheduler for Heterogeneous Clusters
Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019
PROPOSAL
Multi-query Optimization in Spark
Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019
PROPOSAL
Predicting Execution times Queries Executed on Accelerated Distributed Platforms
In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019