Recently, the Artificial Intelligence (AI) field has been experiencing a resurgence. AI broadly covers a wide swath of techniques which include logic-based approaches, probabilistic graphical models, and machine learning/deep learning approaches. Advances in hardware capabilities, such as Graphics Processing Units (GPUs), software components (e.g., accelerated libraries, programming frameworks), and systems infrastructures (e.g., GPU-enabled cloud providers) has led to a wide-spread adaptation of AI techniques to a variety of domains. Examples of such domains include image classification, autonomous driving, automatic speech recognition (ASR) and conversational systems (chatbots). AI solutions not only support multiple datatypes (e.g., free text, images, or speech), but are also available in various configurations, from personal devices to large-scale distributed systems.

In spite of the wide ranging applications of AI techniques, its interactions with the data management systems remains in infancy. At present, a majority of database management systems (DBMS) are being used primarily as a repository for feeding input data and storing results. Recently, there has been some activity in using AI techniques in data management systems, e.g., enabling natural language interfaces to relational databases and applying machine learning techniques for query optimizations. However, a lot more needs to done to fully exploit the power of AI for data management workloads.

We propose to organize 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 the data management systems, e.g., user interfaces, tooling, performance optimizations, new query types, and workloads. Special emphasis would be given to transparent exploitation of AI techniques using existing 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:

• Characterizing different AI approaches: Logic-based, Probabilistic Graphical models, and machine learning/deep learning approaches
• Evaluation of different learning approaches: unsupervised learning, 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 queries and chatbot interfaces
• Natural language result summarization
• Issues with explainability/interpretability
• Evaluating quality of approximate results from AI-enabled queries
• Supporting multiple datatypes (e.g., images or time-series data)
• 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
• Re-inforcement 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 database (e.g., De-Biasing)

Organization

Workshop Co-Chairs

• Rajesh Bordawekar, IBM T.J. Watson Research Center
• Oded Shmueli, Computer Science Department, Technion

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

Program Committee

• Uri Alon, Technion
• Bortik Bandyopadhyay, Ohio State University
• Raul Castro Fernandez, MIT
• Bugra Gedik, Bilkent University
• Tin Kam Ho, IBM Watson
• Lipyeow Lim, University of Hawaii
• Sharad Mehrotra, University of California, Irvine
• Ryan Markus, Brandeis University
• Subhabrata Mukherjee, Amazon
• Sebastian Schelter, NYU
• Jennifer Sleeman, University of Maryland, Baltimore County
• Kavitha Srinivas, IBM Research
• Seema Sundara, Oracle Labs

Important Dates

• Paper Submission: Monday, 18th March 2019
• Notification of Acceptance: Friday, 19th April, 2019
• Camera-ready Submission: Friday, 3rd May, 2019
• Workshop Date: Friday, 5th July, 2019

Submission Instructions

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the SIGMOD/PODS'19 conferences (using the 2017 ACM format).

It is the authors' responsibility to ensure that their submissions adhere strictly to the 2017 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.