arXiv subjects

Angjela Davitkova

Publications and source records attributed to Angjela Davitkova.

4 recordsLinked to original sources

arXiv2025

Automated Training of Learned Database Components with Generative AI

The use of deep learning for database optimization has gained significant traction, offering improvements in indexing, cardinality estimation, and query optimization. However, acquiring high-quality training data remains a significant challenge. This paper explores the possibility of using generative models, such as GPT, to synthesize training data for learned database components. We present an initial feasibility study investigating their ability to produce realistic query distributions and execution plans for database workloads. Additionally, we discuss key challenges, such as data scalability and labeling, along with potential solutions. The initial results suggest that generative models can effectively augment training datasets, improving the adaptability of learned database techniques.

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arXiv2024

Grid-AR: A Grid-based Booster for Learned Cardinality Estimation and Range Joins

We propose an advancement in cardinality estimation by augmenting autoregressive models with a traditional grid structure. The novel hybrid estimator addresses the limitations of autoregressive models by creating a smaller representation of continuous columns and by incorporating a batch execution for queries with range predicates, as opposed to an iterative sampling approach. The suggested modification markedly improves the execution time of the model for both training and prediction, reduces memory consumption, and does so with minimal decline in accuracy. We further present an algorithm that enables the estimator to calculate cardinality estimates for range join queries efficiently. To validate the effectiveness of our cardinality estimator, we conduct and present a comprehensive evaluation considering state-of-the-art competitors using three benchmark datasets -- demonstrating vast improvements in execution times and resource utilization.

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arXiv2022

Compressing (Multidimensional) Learned Bloom Filters

Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing can be modeled as a binary classification problem and solved through deep learning models, leading to what is called learned Bloom filters. We have identified that the benefits of learned Bloom filters are apparent only when considering a vast amount of data, and even then, there is a possibility to further reduce their memory consumption. For that reason, we introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy. We evaluate our approach and show significant memory consumption improvements over learned Bloom filters.

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arXiv2021

LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated predicates, and various types of queries involving multiple joins, render cardinality estimation a particularly challenging task. In this paper, we develop a framework, termed LMKG, that adopts deep learning approaches for effectively estimating the cardinality of queries over RDF graphs. We employ both supervised (i.e., deep neural networks) and unsupervised (i.e., autoregressive models) approaches that adapt to the subgraph patterns and produce more accurate cardinality estimates. To feed the underlying data to the models, we put forward a novel encoding that represents the queries as subgraph patterns. Through extensive experiments on both real-world and synthetic datasets, we evaluate our models and show that they overall outperform the state-of-the-art approaches in terms of accuracy and execution time.

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