Databases Theory and Applications

This book constitutes the refereed proceedings of the 34th Australasian Database Conference on Databases Theory and Applications, ADC 2023, held in Melbourne, VIC, Australia, during November 1-3, 2023.

The 26 full papers presented in this volume are carefully reviewed and selected from 41 submissions. They were organized in topical sections named: Mining Complex Types of Data, Natural Language Processing and Text Analysis, Machine Learning and Computer Vision, Database Systems and Data Storage, Data Quality and Fairness for Graphs and Graph Mining and Graph Algorithms.

Inhalt
Mining Complex Types of Data .- kNN Join for Dynamic High-dimensional Data: A Parallel Approach.- Take a Close Look at the Optimization of Deep Kernels forNon-parametric Two-sample Tests.- Probabilistic Reverse Top-k Query on Probabilistic Data.- Optimizing Taxi Route Planning Based on Taxi Trajectory Data Analysis.- Natural Language Processing and Text Analysis .- Learning Implicit Sentiment for Explainable Review-Based Recommendation.- Balanced and Explainable Social Media Analysis for Public Health with Large Language Models.- Prompt-based Effective Input Reformulation for Legal Case Retrieval.- Surveying the Landscape: Compound Methods for Aspect-Based Sentiment Analysis.- Machine Learning and Computer Vision .- Towards Reliable and Efficient Vegetation Segmentation for Australian Wheat Data Analysis.- SMST: A Saliency Map to Scanpath Transformer.- Multi-level Storage Optimization for Intermediate Data in AI Model Training.- Enhancing Night-to-Day Image Translation with Semantic Prior and Reference Image Guidance.- Batch Level Distributed Training of LSTM with Infinity Norm Gradient Flow.- Database Systems and Data Storage .- Health Status Assessment for HDDs based on Bi-LSTM and N-dimensional Similarity Metric.- Why Query Plans are Different: An Automatic Detection and Inference System.- An Empirical Analysis of Just-in-Time Compilation in Modern Databases.- Relational Expressions for Data Transformation and Computation.- Data Quality and Fairness for Graphs .- Discovering Graph Differential Dependencies.- Maximum Fairness-aware (k, r)-Core Identification in Large Graphs.- IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs.- Efficient Maximum Relative Fair Clique Computation in Attributed Graphs.- Graph Mining and Graph Algorithms .- Balanced Hop-Constrained Path Enumeration in Signed Directed Graphs.- An Experimental Evaluation of Two Methods on Shortest Distance Queries over Small-world Graphs.- Discovering Densest Subgraph over Heterogeneous Information Networks.- Influence Maximization Revisited.- On Directed Densest Subgraph Detection.