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aiDM'18: Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
ACM2018 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGMOD/PODS '18: International Conference on Management of Data Houston TX USA 10 June 2018
ISBN:
978-1-4503-5851-4
Published:
10 June 2018
Sponsors:
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Abstract

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research-article
GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning
Article No.: 1, Pages 1–7https://doi.org/10.1145/3211954.3211956

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, ...

research-article
Contextual Intelligence for Unified Data Governance
Article No.: 2, Pages 1–9https://doi.org/10.1145/3211954.3211955

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, ...

research-article
Deep Reinforcement Learning for Join Order Enumeration
Article No.: 3, Pages 1–4https://doi.org/10.1145/3211954.3211957

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 ...

research-article
Deep Reinforcement-Learning Framework for Exploratory Data Analysis
Article No.: 4, Pages 1–4https://doi.org/10.1145/3211954.3211958

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 ...

research-article
StreamScope: Automatic Pattern Discovery over Data Streams
Article No.: 5, Pages 1–8https://doi.org/10.1145/3211954.3211959

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 ...

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Acceptance Rates

aiDM'18 Paper Acceptance Rate 5 of 8 submissions, 63%;
Overall Acceptance Rate 19 of 26 submissions, 73%
YearSubmittedAcceptedRate
aiDM '2066100%
aiDM '1912867%
aiDM'188563%
Overall261973%