https://dasya.itu.dk/supervisor/zoi-kaoudi/ Recent content in Zoi Kaoudi on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Mon, 24 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-xli-dataprepare4qo/ Mon, 24 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-xli-dataprepare4qo/ Traditional cost-based query optimizers are highly effective for many database workloads, especially for short and moderately complex queries. However, for queries involving many joins and large search spaces, optimization can become expensive and inaccurate. Recent learning-based query optimizers have shown promising results for complex queries, but they may introduce unnecessary overhead for simpler queries. This project aims to develop a hybrid query optimizer that combines the advantages of both approaches. The main idea is to analyze an incoming query and dynamically decide whether it should be handled by a traditional cost-based optimizer or a learning-based optimizer. https://dasya.itu.dk/for-students/proposals/archive/mh-zk-wayang-metadata-store/ Fri, 24 Jul 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mh-zk-wayang-metadata-store/ BSc / MSc Thesis. This project focuses on designing and implementing a metadata management store for Apache Wayang. Wayang lets users express a data processing task in a platform-agnostic way and then decides which underlying platform (e.g., Spark, Flink, Postgres, Java) should execute each part of the plan. Today, however, Wayang has no dedicated component for managing metadata: information such as schemas, statistics, or learned cost functions is handled implicitly by individual subsystems rather than stored and reused in one place. https://dasya.itu.dk/for-students/proposals/current/zk-lrt/ Mon, 09 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-lrt/ 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 plan. Interestingly though, it is well-known that what really matters in query optimization is the relative order of the query plan alternatives and not their actual cost or runtime. https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang/ 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 check this paper. Potential Intended Learning Outcomes depending on the concrete topic: https://dasya.itu.dk/for-students/proposals/current/zk-kges/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-kges/ 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 graph embeddings with domain-specific reasoning stemming from experts or reasoning systems. In this way, we not only enhance the prediction accuracy with domain knowledge that may not be included in the input knowledge graph but also allow users to plugin their own knowledge graph embedding and reasoning method. https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-mcp/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-mcp/ 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 a general overview check this paper. https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-green/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-apachewayang-green/ 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. For a general overview check this paper. Wayang currently optimises query execution time by selecting the right platforms. https://dasya.itu.dk/for-students/proposals/current/zk-datafarm/ Wed, 28 May 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/zk-datafarm/ 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 lots of training data are required. In this context, training data comprise of query plans and their runtimes.