We study the problem of efficiently computing rankings of joinable attributes in data lakes. Traditional set-overlap measures produce numerous false positives in this scenario, while modern, more accurate Table Representation Learning (TRL) techniques incur prohibitive computational costs. In contrast to the state-of-the-art, we adopt a novel notion of join quality tailored to data lakes relying on a metric that combines multiset Jaccard and cardinality proportion. The proposed metric merges the best of both worlds by leveraging syntactic measures while achieving accuracy scores comparable to those of TRL approaches. Generating rankings of joinable pairs is highly scalable at both preparation and query time, since we train a general-purpose predictive model. Predictions are based on data profiles, succinct and efficiently computed representations of dataset characteristics. Our experiments show that our system, Freyja, matches and improves upon, the results obtained by the state-of-the-art while reducing execution costs by orders of magnitude.

Recently, transformers have gained prominence in video action recognition due to their ability to capture spatio-temporal dependencies. Despite their effectiveness, the interpretability of their self-attention mechanisms remains limited, posing obstacles in understanding model decisions, impacting transparency and bias identification. Additionally, the computational demands of transformer architectures, particularly the self-attention mechanism, present practical difficulties. To tackle both challenges, we adapt existing interpretability techniques and introduce a layer pruning method guided by importance metrics. In the context of driver action recognition, our findings highlight the efficacy of the applied head importance metrics in pinpointing crucial attention heads and identifying key visual cues essential for recognizing driver behavior. Experimental results, conducted on three mainstream video transformers, demonstrate the effectiveness of the proposed pruning technique with significantly reduced computational costs and only slight performance degradation by removing low-relevance layers. Specifically, on our DriverActionInsight (DAI) dataset, we achieve a 23.5% FLOPs saving in compressing Video Swin with less than a 1 % decrease in Top-1 accuracy.