---------------------------------------------------------------------------------
Call For Participation
--------------------------------------------------------------------------------
4th International Workshop on Exploiting Artificial
Intelligence Techniques for Data Management (AIDM 2021)
http://www.aidm-conf.org/
In conjunction with SIGMOD/PODS 2021, Friday, June 25, 2021,
Xi'an, China (Online Conference)
--------------------------------------------------------------------------------
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 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
workshop is to explore opportunities for using AI techniques in
enhancing various components of data management systems, such as user
interfaces, tooling, performance optimization, support for new query
types and workloads. Special emphasis will be given to transparent
exploitation of AI techniques using existing data management
infrastructures 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:
- Characterizing different AI approaches:
Logic-based, probabilistic graphical models, and machine learning/deep learning approaches
- Evaluation of different learning approaches:
unsupervised, self-supervised, supervised or reinforced learning,
transfer learning, zero-shot learning, adversarial networks, and deep probabilistic models
- New AI-enabled business intelligence (BI) queries for relational databases
- Natural language enablement (e.g., queries, result summarization, chatbot interfaces, etc.)
- Explainability and interpretability
- Fairness of AI-based system components
- Integration with Data Science and Deep Learning toolkits (e.g., sklearn, TensorFlow, PyTorch, etc.)
- Evaluating quality of approximate results from AI-enabled queries
- Supporting multiple datatypes (e.g., images, time-series data, etc.)
- Supporting semi-structured, streaming, and graph databases
- Reasoning over knowledge bases
- Data exploration and visualization
- Integrating structured and unstructured data sources
- AI-enabled data integration strategies (e.g., entity resolution, schema matching, etc.)
- Reinforcement learning for Database tuning
- Impact of AI on tooling, e.g., ETL or data cleaning
- Performance implications of AI-enabled queries
- Case studies of AI-accelerated workloads
- Social Implications of AI-enabled databases (e.g., detection and elimination of bias)
- Learned data structures, database algorithms or systems components
- Examples of AI-enabled customer usecases
--------------------------------------------------------------------------------
Organization and PC
--------------------------------------------------------------------------------
Workshop Co-Chairs
- Rajesh Bordawekar, IBM T.J. Watson Research Center
- Yael Amsterdamer Department of Computer Science, Bar-Ilan University
- Oded Shmueli, Computer Science Department, Technion - Israel Institute of Technology
- Nesime Tatbul, Intel Labs and MIT
Program Committee
- Dana Van Aken, CMU
- Joy Arulraj, Georgia Tech
- Carsten Binnig, TU Darmstadt
- Thomas Heinis, Imperial College
- Andreas Kipf, MIT
- Nick Koudas, University of Toronto
- Amelie Marian, Rutgers University
- Yuval Moskovitch, University of Michigan
- Vivek Narasayya, Microsoft Research
- Apoorva Nitsure, IBM Research
- Sunita Sarawagi, IIT Bombay
- Seema Sundara, Oracle Labs
- Saravanan Thirumuruganathan, QCRI, HBKU
- Zongheng Yang, University of California, Berkeley
--------------------------------------------------------------------------------
Submission Instructions
--------------------------------------------------------------------------------
* Important Dates:
Paper Submission: March 22, 2021, 12 pm PST
Notification of Acceptance: April 23, 2021
Camera-ready Submission: May 3, 2021
Workshop Date: Friday, June 15, 2021
* Submission Site:
All submissions will be handled electronically via EasyChair
(https://www.easychair.org/conferences/?conf=aidm21).
* Formatting Guidelines:
We will use the same document templates as the SIGMOD/PODS'21 conferences (the
ACM format provided at: https://www.acm.org/publications/proceedings-template/).
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 guidelines will be rejected without review.
The paper length for a full paper is limited upto 8 pages. However, shorter
papers (4 pages) are encouraged as well.
All accepted papers will be indexed via the ACM digital library and will be
available for download from the workshop webpage in the digital library.