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.

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

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

       For questions regarding the workshop please send email to bordaw AT us DOT ibm DOT com.

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

Important Dates 

• Paper Submission: Monday, 22th March 2021, 12 pm PST
• Notification of Acceptance: Friday, 23th April, 2021
• Camera-ready Submission: Monday, 3rd May, 2021
• Workshop Date: Friday, 25th June, 2021

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the SIGMOD/PODS'21 conferences (the ACM format).

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 8 pages. However, shorter papers (4 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.