arXiv:2608. 08639v1 Announce Type: new Abstract: Open lakehouse table formats accumulate small data files over time, which degrades query performance.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 03946v1 Announce Type: cross Abstract: Database vendors recently released AI functions that can be used in filter predicates.
arXiv:2607. 28880v1 Announce Type: cross Abstract: Modern multimedia machine learning workloads increasingly store large-scale datasets in cloud object storage services such as AWS S3.
arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.
arXiv:2608. 16309v1 Announce Type: cross Abstract: Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms.
arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.