Abstract
The proceedings contain 29 papers. The special focus in this conference is on Big Data Analytics and Knowledge Discovery. The topics include: Leveraging Machine Learning Techniques for Customer Data Deduplication - Hard-Won Lessons from a Real-World Project in the Financial Industry; FairFES - Fast Exact Sampling for Fair Classification; autism Detection by Analyzing Handwriting Characteristics of Chinese Characters via Deep Learning Models; FNoDe: Faulty Node Detection in Microservices Architecture; An Enhanced FP-Growth Algorithm with Hybrid Adaptive Support Threshold for Association Rule Mining; entity Resolution for Streaming Data with Embeddings; cross-Modal Sequential Point-of-Interest Recommendation with Lightweight Hybrid Fusion Strategy; alternatives to Shallow Autoencoders for Collaborative Filtering; accurate Concept Drift Detection Without Updating Autoencoders; Parallel and Distributed SQL/PGQ Query Processing for Property Graphs; graph Constraint Language for Industrial Knowledge Graphs and Machine Learning; SemViSG: Semantic Enrichment and Visualization of Software Graphs; certainty Attacks Using Explainability Preprocessing; integrating Bitcoin Transactions into Relational Databases for IoT: Challenges and Solutions; effects of Response Length on User Search Experience in Spoken Conversational Search; fair Proportional Top-k Ranking; PAID: Power-Efficient AI-Optimized Databases; on the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data; a Bayesian Reinforcement Learning Framework for Online Index Tuning; explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-Wise Multiview Clustering and Large Language Models; Parameter Drift as a Signal for Membership Inference in Overfit-Tuned LLMs; microSuggest: Kernel-Aware Microservice Decomposition; TraceTune: Targeted Fine-Tuning of Attention Heads for Text-to-SQL; ONNYX : Optimized Neural Networks Yielding eXplainable Insights from ECG Signals-Based Data Streams; spaPool: Soft Partition Assignment Pooling for Graph Neural Networks; prediction of Iterative Solvers’ Convergence Using Pretraining by Natural Images; local-Aware Convolutional Modulation for Short-Term Sequential Recommendation.