{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:00:17Z","timestamp":1776445217186,"version":"3.51.2"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>Data pipelines (i.e., converting raw data to features) are critical for machine learning (ML) models, yet their development and management is time-consuming. Feature stores have recently emerged as a new \"DBMS-for-ML\" with the premise of enabling data scientists and engineers to define and manage their data pipelines. While current feature stores fulfill their promise from a functionality perspective, they are resource-hungry---with ample opportunities for implementing database-style optimizations to enhance their performance. In this paper, we propose a novel set of optimizations specifically targeted for point-in-time join, which is a critical operation in data pipelines. We implement these optimizations on top of Feathr: a widely-used feature store, and evaluate them on use cases from both the TPCx-AI benchmark and real-world online retail scenarios. Our thorough experimental analysis shows that our optimizations can accelerate data pipelines by up to 3\u00d7 over state-of-the-art baselines.<\/jats:p>","DOI":"10.14778\/3625054.3625060","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T17:09:42Z","timestamp":1701709782000},"page":"4230-4239","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimizing Data Pipelines for Machine Learning in Feature Stores"],"prefix":"10.14778","volume":"16","author":[{"given":"Rui","family":"Liu","sequence":"first","affiliation":[{"name":"University of Chicago"}]},{"given":"Kwanghyun","family":"Park","sequence":"additional","affiliation":[{"name":"Yonsei University"}]},{"given":"Fotis","family":"Psallidas","sequence":"additional","affiliation":[{"name":"Microsoft"}]},{"given":"Xiaoyong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Microsoft"}]},{"given":"Jinghui","family":"Mo","sequence":"additional","affiliation":[{"name":"LinkedIn"}]},{"given":"Rathijit","family":"Sen","sequence":"additional","affiliation":[{"name":"Microsoft"}]},{"given":"Matteo","family":"Interlandi","sequence":"additional","affiliation":[{"name":"Microsoft"}]},{"given":"Konstantinos","family":"Karanasos","sequence":"additional","affiliation":[{"name":"Meta"}]},{"given":"Yuanyuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Microsoft"}]},{"given":"Jes\u00fas","family":"Camacho-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Microsoft"}]}],"member":"320","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. 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