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.
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
Workshop Steering Committee
- Rajesh Bordawekar, IBM T.J. Watson Research Center
- Oded Shmueli, Hirundo Ltd., and Emeritus Professor at Technion - Israel Institute of Technology
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
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.
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