aiDM 2024
Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)
 

 
Co-located with ACM SIGMOD/PODS 2024
Friday, June 14, 2024

 
 
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Workshop Overview

Recently, the field of Artificial Intelligence (AI) has been experiencing a resurgence. AI broadly covers a wide swath of techniques, which include logic-based approaches, probabilistic graphical models, machine learning approaches such as deep learning. Advances in specialized hardware capabilities (e.g., Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), etc.), software ecosystem (e.g., programming languages such as Python, Data Science frameworks, and accelerated ML libraries), and systems infrastructure (e.g., cloud servers with AI accelerators) have led to wide-spread adoption of AI techniques in a variety of domains. Examples of such domains include image classification, autonomous driving, automatic speech recognition, and conversational systems (e.g., chatbots). AI solutions not only support multiple data types (e.g., images, speech, or text), but also are available in various configurations and settings, from personal devices to large-scale distributed systems.

In spite of the wide-ranging techniques and applications of AI, their interactions with data management systems remain in infancy. Database management systems have been, for a long time, simply used as repositories for feeding inputs and storing results. Only very recently, we have started seeing some new efforts in using AI techniques in data management systems, e.g., enabling natural language interfaces to relational databases and applying machine learning techniques for query optimization. However, a lot more needs to be done to fully exploit the power of AI for data management systems and workloads.

aiDM'24 is a one-day workshop that will bring together people from academia and industry to discuss various ways of integrating AI techniques with data management systems. The primary goal of the proposed workshop is to explore opportunities for AI techniques for enhancing different components of data management systems, e.g., user interfaces, tooling, performance optimizations, new query types, and workloads. Special emphasis will be given to transparent exploitation of AI techniques (e.g., using Generative AI frameworks) for data management for enterprise class workloads. We hope this workshop will identify important areas of research and spur new efforts in this emerging field.

Topics of Interest

The goal of the workshop is to take a holistic view of various AI technologies and investigate how they can be applied to different component of an end-to-end data management pipeline. Special emphasis would be given to how AI techniques could be used for enhancing user experience by reducing complexity in tools, or providing newer insights, or providing better user interfaces. Topics of interest include, but are not restricted to:

  • AI-enabled improvements to foundational DB algorithms: sorting, searching, consensus
  • New AI-enabled business intelligence (BI) queries for relational databases
  • Integration of Large Language Models with databases and supporting services (e.g., Generative AI)
  • Natural language queries and conversational interfaces
  • AI-enabled database programming (e.g., natural language queries, SQL co-pilots, etc.)
  • Fairness of AI-based system components
  • Design and Implementation of Vector Databases for unstructured data
  • Ethics, governance, and societal implications of AI-enabled databases
  • Reasoning over knowledge bases
  • Self-tuning Databases using reinforced learning
  • Impact of model interpretability
  • Supporting multiple datatypes (e.g., images or time-series data)
  • Supporting semi-structured, streaming, and graph databases
  • Impact of AI on tooling, e.g., ETL or data cleaning
  • Performance implications of AI-enabled queries
  • Case studies of AI-accelerated workloads
  • AI-enabled databases for managing and supporting AI workloads
  • AI strategies for data provenence, access control, anomaly detection and cyber security


Organization

Workshop Steering Committee

Workshop Program Chairs

Program Committee

  • Madelon Hulsebos, UC Berkeley
  • Sonia-Florina Horchidan, KTH Royal Institute of Technology
  • Yuliang Li, Reality Lab Research, Meta
  • Yasuko Matsubara, Osaka University
  • Apoorva Nitsure, IBM Almaden Research Center
  • Amit Somech, Bar-Ilan University
  • Matthias Urban, TU Darmstadt
  • Jun Wan, Databricks
  • Wenlu Wang, Texas A&M University

Submission Instructions

Important Dates 

  • Paper Submission: Monday, 25th March 2024, 12 pm PST (EXTENDED)
  • Notification of Acceptance: Monday, 15th April, 2024
  • Camera-ready Submission: Monday, 6th May, 2024

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the SIGMOD/PODS'24 conferences (the ACM format). Like SIGMOD/PODS'24, the aiDM submission should be double-blind.

It is the authors' responsibility to ensure that their submissions adhere strictly to the ACM format. In particular, it is not allowed to modify the format with the objective of squeezing in more material. Submissions that do not comply with the formatting detailed here will be rejected without review. 

The paper length for a full paper is limited upto 12 pages, with unlimited pages of references. However, shorter papers (4 or 8 pages) are encouraged as well.  

All accepted papers will be indexed via the ACM digital library and available for download from the workshop webpage in the digital library.