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7 | 7 |
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8 | 8 | <div class="col-md-12 margin-top-3"> |
9 | 9 | <div class="pr-4 pl-5"> |
10 | | - <p> |
11 | | - With the increased adoption of machine learning (ML) across applications and disciplines, a strong synergy |
12 | | - between the database (DB) systems and ML communities has emerged. Steps involved in ML pipelines—such as |
13 | | - data preparation and cleaning, feature engineering, and management of the ML lifecycle—can benefit |
14 | | - significantly from advances in data management. For example, managing the ML lifecycle requires mechanisms |
15 | | - for modeling, storing, and querying ML artifacts in a robust, scalable, and auditable manner. |
16 | | - </p> |
17 | | - |
18 | | - <p> |
19 | | - More recently, the advent of large language models (LLMs) and Retrieval-Augmented Generation (RAG) has |
20 | | - further intensified the need for high-performance data management infrastructures. Modern AI systems |
21 | | - increasingly rely on vector databases, efficient vector search, and scalable model serving. At the same |
22 | | - time, the rise of multimodal AI introduces demanding requirements for storing and querying images, audio, |
23 | | - video, and other complex data types, all while maintaining low latency and high throughput for end users. |
24 | | - </p> |
25 | | - |
26 | | - <p> |
27 | | - In the opposite direction, ML techniques are now explored in core components of database systems, including |
28 | | - query optimization, indexing, storage layout, and self-tuning. Long-standing challenges in databases—such |
29 | | - as cardinality estimation, operator and plan selection, resource management, and other tasks traditionally |
30 | | - handled with extensive human expertise or rigid heuristics—increasingly benefit from learned models and |
31 | | - data-driven approaches. |
32 | | - </p> |
33 | | - |
34 | | - <p> |
35 | | - DBML 2026 aims to bring together researchers and practitioners working at this intersection, providing a dedicated forum for DB-inspired and ML-inspired approaches that address challenges in either or both communities. We welcome work that combines the strengths of DB and ML, ranging from foundational techniques and system designs to practical applications and real-world deployments, including ML for scientific data and other data-intensive domains. |
36 | | - </p> |
| 10 | + <p> |
| 11 | + DBML 2026 Workshop, held in conjunction with ICDE 2026 in Montréal, Canada, explores the growing synergy |
| 12 | + between databases and machine learning. |
| 13 | + </p> |
| 14 | + <p> |
| 15 | + Advances in data management techniques have become essential for building robust, scalable ML systems. |
| 16 | + Applications range from data preparation and cleaning to feature engineering and managing the ML lifecycle. |
| 17 | + The recent rise of LLMs and RAG has only intensified demand for high-performance data infrastructure. |
| 18 | + Modern AI systems increasingly rely on vector databases and scalable model serving. |
| 19 | + Multimodal AI adds further requirements for storing and querying images, audio, and video. |
| 20 | + </p> |
| 21 | + <p> |
| 22 | + In the opposite direction, ML techniques are now incorporated as core components of database systems: |
| 23 | + query optimization, indexing, storage layout, and self-tuning. |
| 24 | + Long-standing challenges like cardinality estimation, operator and plan selection, and resource management |
| 25 | + - traditionally handled via human knowledge or heuristics - increasingly benefit from learned models. |
| 26 | + </p> |
| 27 | + <p> |
| 28 | + DBML 2026 brings together researchers and practitioners working at this intersection. |
| 29 | + We welcome work combining DB and ML strengths, ranging from foundational techniques and system design to |
| 30 | + practical applications and real-world deployments, including ML for scientific and data-intensive domains. |
| 31 | + </p> |
37 | 32 |
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38 | 33 | <p> |
39 | 34 | Information about previous editions can be found at |
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