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GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning
- Gabriel Campero Durand,
- Marcus Pinnecke,
- Rufat Piriyev,
- Mahmoud Mohsen,
- David Broneske,
- Gunter Saake,
- Maya S. Sekeran,
- Fabián Rodriguez,
- Laxmi Balami
In this paper we define a research agenda to develop a general framework supporting online autonomous tuning of data partitioning and layouts with a reinforcement learning formulation. We establish the core elements of our approach: agent, environment, ...
Contextual Intelligence for Unified Data Governance
Current data governance techniques are very labor-intensive, as teams of data stewards typically rely on best practices to transform business policies into governance rules. As data plays an increasingly key role in today's data-driven enterprises, ...
Deep Reinforcement Learning for Join Order Enumeration
Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join order enumeration algorithms that do not incorporate feedback about the quality of the resulting plan. Hence, optimizers ...
Deep Reinforcement-Learning Framework for Exploratory Data Analysis
Deep Reinforcement Learning (DRL) is unanimously considered as a breakthrough technology, used in solving a growing number of AI challenges previously considered to be intractable. In this work, we aim to set the ground for employing DRL techniques in ...
StreamScope: Automatic Pattern Discovery over Data Streams
Given a large volume of multi-dimensional data streams, such as that produced by IoT applications, finance and online web-click logs, how can we discover typical patterns and compress them into compact models? In addition, how can we incrementally ...
Index Terms
- Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
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