BLEND: A Unified Data Discovery System
Mahdi Esmailoghli, Christoph Schnell, Renée J. Miller, Ziawasch Abedjan
Abstract
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- and cost-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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7bd06b4e-4564-48b0-a69b-51cef12ef2adCited by top-tier papers4
- Qualitative Join Discovery in Data Lakes using ExamplesMir Mahathir Mohammad, El Kindi RezigSIGMOD 2026 · 6 citations
- Rethinking Dataset Discovery with DataScoutRachel Lin, Bhavya Chopra, Wenjing Lin, Shreya Shankar et al.UIST 2025 · 2 citations
- FedAugment: Table Augmentation Search over Decentralized Data RepositoriesLennart Behme, Emil Badura, Leonard Geißler, Matthias Böhm et al.VLDB 2026
- Matryoshka: Uncovering Relevant Features in Data Lakes to Enhance Machine Learning ApplicationsFedor Turchenko, Runjie Zhang, Binger Chen, Matthias Boehm et al.VLDB 2026
Builds on16
- Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation LearningGrace Fan, Jin Wang, Yuliang Li, Dan Zhang et al.VLDB 2023 · 139 citations
- Dataset Discovery in Data LakesAlex Bogatu, Alvaro A. A. Fernandes, Norman W. Paton, Nikolaos KonstantinouICDE 2020 · 118 citations
- Finding Related Tables in Data Lakes for Interactive Data ScienceYi Zhang, Zachary G. IvesSIGMOD 2020 · 98 citations
- Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based ApproachYuyang Dong, Kunihiro Takeoka, Chuan Xiao, Masafumi OyamadaICDE 2021 · 78 citations
- SANTOS: Relationship-based Semantic Table Union SearchAamod Khatiwada, Grace Fan, Roee Shraga, Zixuan Chen et al.SIGMOD 2023 · 61 citations
Related papers
- Predicate Pushdown for Data Science PipelinesCong Yan, Yin Lin, Yeye HeSIGMOD 2023 · 15 citations
- Machine Learning Inference Pipeline Execution Using Pure SQL Based on Operator FusionQingfeng Pan, Jiahe Zhi, Chenyang Zhang, Chen Xu et al.ICDE 2025 · 2 citations
- Cross Modal Data Discovery over Structured and Unstructured Data LakesMohamed Y. Eltabakh, Mayuresh Kunjir, Ahmed K. Elmagarmid, Mohammad Shahmeer AhmadVLDB 2023 · 11 citations
- Integrating Data Lake TablesAamod Khatiwada, Roee Shraga, Wolfgang Gatterbauer, Renée J. MillerVLDB 2023 · 59 citations
- Generalized Sub-Query Fusion for Eliminating Redundant I/O from Big-Data QueriesPartho Sarthi, Kaushik Rajan, Akash Lal, Abhishek Modi et al.OSDI 2020 · 5 citations
