# Towards Anticipatory Databases Through Shared Data and Workload Semantics

arXiv: https://arxiv.org/abs/2609.14255 · HTML: https://mlsanity.com/paper/2609.14255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [Towards Dynamic and Safe Configuration Tuning for Cloud Databases](https://mlsanity.com/paper/2203.14473/) (2022)
- [FactorJoin: A New Cardinality Estimation Framework for Join Queries](https://mlsanity.com/paper/2212.05526/) (2022)
- [SGDP: A Stream-Graph Neural Network Based Data Prefetcher](https://mlsanity.com/paper/2304.03864/) (2023)
- [Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices](https://mlsanity.com/paper/2305.01237/) (2023)
- [Sibyl: Forecasting Time-Evolving Query Workloads](https://mlsanity.com/paper/2401.03723/) (2024)
- [SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads](https://mlsanity.com/paper/2310.14666/) (2023)
- [QVCache: A Query-Aware Vector Cache](https://mlsanity.com/paper/2602.02057/) (2026)
- [Learning and Anticipating Future Actions During Exploratory Data Analysis](https://mlsanity.com/paper/1809.09664/) (2018)

## Abstract (arXiv)

Database management systems increasingly serve dynamic and exploratory workloads, yet many of their decisions still rely on low-level signals such as recency, frequency, and address locality. These signals capture how data was accessed, but not what is being examined or how an analytical focus evolves. We argue for treating workload semantics as a first-class control signal for anticipatory decision making. Central to this view, we introduce semantic locality and semantic trajectories, which capture relationships among nearby queries and how those relationships evolve across a session. We propose a framework that represents semantic context at the data, query, and session levels, models its evolution over time, and translates it into task-specific utility estimates. We instantiate this framework in semantic prefetching and semantic cache eviction, which share a semantic layer to make two separate decisions. Prefetching uses semantic trajectories to anticipate future accesses beyond what address-based locality can capture, while eviction uses semantic relevance to inform block replacement. These systems provide initial evidence that shared semantic context can support multiple DBMS components. We further outline how this principle can extend to other decisions and data systems, and discuss key challenges in representation, cost, adaptation, and evaluation.