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Blend: A Unified Data Discovery System

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arxiv 2310.02656 v2 pith:U2T3PT5S submitted 2023-10-04 cs.DB

classification cs.DB
keywords discoveryoperatorsblenddatasystempipelinesproposetasks
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Most research on data discovery has so far focused on improving individual discovery operators such as join, correlation, or union discovery. However, in practice, a combination of these techniques and their corresponding indexes may be necessary to support arbitrary discovery tasks. We propose BLEND, a comprehensive data discovery system that supports existing operators and enables their flexible pipelining. BLEND is based on a set of lower-level operators that serve as fundamental building blocks for more complex and sophisticated user tasks. To reduce the execution runtime of discovery pipelines, we propose a unified index structure and a rule-based optimizer that rewrites SQL statements into low-level operators when possible. We show the superior flexibility and efficiency of our system compared to ad-hoc discovery pipelines and stand-alone solutions.

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Cited by 1 Pith paper

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  1. Rethinking Dataset Discovery with DataScout

    cs.HC 2025-07 conditional novelty 6.0 of 10

    A dataset search interface with LLM-generated query reformulations, semantic column and granularity filters, and task-specific relevance indicators that helped 12 study participants explore and make sense of dataset s...

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