Co-located with ACM SIGMOD/PODS 2023
Friday, June 23, 2023

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, ONNX, 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
• AI-enabled databases for managing and supporting AI workloads
• AI strategies for data provenence, access control, anomaly detection and cyber security
• Experiences with database systems employing AI-enhanced components and interaction among AI-enhanced components

• Introductory Remarks Yael Amsterdamer, Department of Computer Science, Bar-Ilan University

• (Keynote 1) Dan Roth, VP/Distinguished Scientist, AWS AI Labs and the Eduardo D. Glandt Distinguished Professor, CIS, University of Pennsylvania

• AutoCure: Automated Tabular Data Curation Technique for ML Pipelines Mohamed Abdelaal, Software AG; Rashmi Koparde, Otto von Guericke University Magdebur; and Harald Schoening, Software AG

• Tuple Bubble: Learned Tuple Representation for Tunable Approximate Query Processing Damjan Gjurovski and Sebastian Michel, RPTU Kaiserslautern-Landau
• Adversarial and Clean Data Are Not Twins Zhitao Gong, Auburn University and Wenlu Wang, Texas A & M University-Corpus Christi
• Zero-Shot Cost Models for Parallel Stream Processing Pratyush Agnihotri, Technical University of Darmstadt; Boris Koldehofe, Technical University of Ilmenau; Carsten Binnig and Manisha Luthra, Technical University of Darmstadt and DFKI
• OmniscientDB: A Large Language Model-Augmented DBMS That Knows What Other DBMSs Do Not Know Matthias Urban, Duc Dat, and Carsten Binnig, Technical University of Darmstadt

• (Keynote 2) Jun Wan, Databricks
• Learned Spatial Data Partitioning Keizo Hori, Yuya Sasaki, Daichi Amagata, Yuki Murosaki, and Makoto Onizuka, Osaka University

• (Panel) Foundation Models and Databases: Opportunities and Challenges, Moderator: Rajesh Bordawekar
  • Arvind Arasu, Microsoft Research
  • Alekh Jindal, Smart Apps
  • Tim Kraska, MIT
  • Laurel Orr, Stanford
  • Jun Wan, Databricks

Organization

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

• Yael Amsterdamer, Department of Computer Science, Bar-Ilan University
• Donatella Firmani, Department of Statistical Sciences, Sapienza University of Rome
• Andreas Kipf, Amazon Web Services

Program Committee

• Zainab Abbas, KTH
• Laure Berti-Equille, IRD
• Cansu Kaynak Kocberber, Oracle
• Nick Koudas, University of Toronto
• Manisha Luthra, Tu Darmstadt
• Umar Farooq Minhas, Apple
• Felix Naumann, HPI
• Rekha Singhal, Tata Consultancy Services
• Anthony Tomasic, CMU
• Brit Youngmann, MIT

Important Dates 

• Paper Submission: Friday, 17th March 2023, 12 pm PST
• Notification of Acceptance: Monday, 17th April, 2023
• Camera-ready Submission: Monday, 8th May, 2023

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the SIGMOD/PODS'23 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 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.