Titlebook: Big Data Analytics and Knowledge Discovery; 26th International C Robert Wrembel,Silvia Chiusano,Ismail Khalil Conference proceedings 2024 T

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IDAGEmb: An Incremental Data Alignment Based on?Graph Embedding and integration complexities. These challenges impact on decision-making and data integration processes. We define data alignment as the process of aligning columns from different tabular sources using their schema and instances. Data alignment is emerging as an essential solution, ensuring data co

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MultiMatch: Low-Resource Generalized Entity Matching Using Task-Conditioned Hyperadapters in?Multitaeous data formats refer to the same real-world entity. State-of-the-art single-task fine-tuning approaches have shown limitations in handling scenarios with entity distribution shifts, particularly in low-resource settings, and can also require significant amounts of computationally expensive fine-t

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Evaluation of?High Sparsity Strategies for?Efficient Binary Classificatione-constrained environments, the strategic sparsification of neural networks takes center stage. In this work, we investigate creating, training, and evaluating Convolutional Neural Network (CNN), DenseNet, and ResNet models taking advantage of sparse neural networks with the help of the Sparse Evolu

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Exploring Evaluation Metrics for?Binary Classification in?Data Analysis: the?Worthiness Benchmark Cocation models, making it essential to analyze and compare these metrics to select the most appropriate one. Despite significant research, a comprehensive comparison of these metrics has not been adequately addressed. The effectiveness of classification models is typically represented by a confusion