https://dasya.itu.dk/tags/data-management/ Recent content in data management on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Fri, 18 Sep 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/osk-ecpfs-multirepresentation/ Fri, 18 Sep 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/osk-ecpfs-multirepresentation/ ecpfs stores one representation of each vector per index. This project adds the ability to store more than one, full vectors, PCA-reduced vectors, or both, and lets you choose which representation gets used at which point in the search: for routing, for final ranking, or both. Once that capability exists, the real question is what combinations are actually worth it: when does keeping a second representation around pay for itself, and when does it just add cost without helping? https://dasya.itu.dk/for-students/proposals/archive/pito-radt/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-radt/ 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 Data Systems Tracker. There is a variety of work to be done on this framework to improve it. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-resourcemanagement/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-resourcemanagement/ 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 powerful hardware device in the cloud for further data processing and training machine learning models. https://dasya.itu.dk/for-students/proposals/archive/mh-deconstr-counters/ Sat, 01 Feb 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mh-deconstr-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, reduces the cost of software development, and lowers the risk of errors by minimizing the number of components that need to be managed. https://dasya.itu.dk/for-students/proposals/archive/mh-deconstr-dictionary/ Mon, 26 Aug 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mh-deconstr-dictionary/ 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, reduces the cost of software development, and lowers the risk of errors by minimizing the number of components that need to be managed. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ 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 interested in resource-constrained hardware, benchmarking, and machine learning in general, this project would be a great fit for you. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ 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 to be deployed at the edge. Then, we would like to characterize the performance of some edge model deployments on a chosen edge device and building on the analysis in the first step. https://dasya.itu.dk/for-students/proposals/archive/pito-radtduckdb/ Thu, 23 Nov 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-radtduckdb/ 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 Data Systems Tracker. Currently, this framework uses Postgres data management system to keep the experimental data. https://dasya.itu.dk/for-students/proposals/archive/inferenceinkvs/ Fri, 16 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/inferenceinkvs/ (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 handled by an FPGA in order to provide small energy footprint and guarantee high access bandwidth at the same time (see details below). https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ Mon, 15 Mar 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mpc-databases/ (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), which allow for both queries and data to be handled privately. In these hybrid systems, not only the data is protected but the queries made by legitimate users are kept private even from the database engine itself (and consequently from the systems executing this engine). https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Tue, 08 Dec 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/privacypreserving/ Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T on the result of the query F or on the data itself before computing F”. https://dasya.itu.dk/for-students/proposals/archive/blockchainbenchmarking/ Tue, 18 Aug 2020 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/blockchainbenchmarking/ Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as key-value pairs, on the ledger that smarts contracts can access and modify. In several use-cases, permissioned ledgers are being considered as an alternative to traditional databases [2].