aiDM 2020
Third International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)
 
Friday, June 19, 2020
 
SIGMOD/PODS 2020 Online Access with Free Registration  
 
 
  Links
  Overview
  Topics of Interest
  Important Dates
  Submission Instructions
  Organization
 
 
  AIDM'19
  AIDM'19 ACM DL Page
  AIDM'19 DBLP Page
 
  AIDM'18
  AIDM'18 ACM DL Page
  AIDM'18 DBLP Page
 
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 components (e.g., accelerated libraries, programming frameworks), and systems infrastructures (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 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, result summarization, and chatbot interfaces
  • Issues with explainability/interpretability
  • Evaluating quality of approximate results from AI-enabled queries
  • Supporting multiple datatypes (e.g., images, 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
  • 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., De-Biasing)
  • Learned data structures, database algorithms or systems components

Online Schedule (9 am - 5 pm PST)

Session 1: Keynote Presentation (9-10.30 am)
  • Data Discovery and Schema Inference Over Data Lakes, Prof. Renee Miller, University Distinguished Professor of Computer Science, Northeastern University

Coffee Break (10.30-11 am)

Session 2: Paper Presentations (11 am - 12.30 pm
  • Best of Both Worlds: Combining Traditional and Machine Learning Models for Cardinality Estimation, Lucas Woltmann, Claudio Hartmann, Dirk Habich, and Wolfgang Lehner, TU Dresden

  • Automated Tuning of Query Degree of Parallelism via Machine Learning, Zhiwei Fan, University of Wisconsin, Madison, Rathijit Sen, Microsoft, Paraschos Koutris, and Aws Albarghouthi, University of Wisconsin, Madison

  • Research Challenges in Deep Reinforcement Learning-based Join Query Optimization, Runsheng Guo and Khuzaima Daudjee, University of Waterloo

    • Lunch Break (12.30 - 1.30 pm)

    Session 3: Keynote Presentation (1.30-3 pm)
    • Detonating Enterprise Applications with AI, Griffin Chronis, Chief Algorithms Officer of Einstein Analytics, Salesforce

    Coffee Break (3- 3.30pm)

    Session 4: Paper Presentations (3.30-5 pm)
    • BanditJoin: Learning to Join What Matters, Vahid Ghadakchi, Mian Xie, and Arash Termehchy, Oregon State University

    • RadixSpline: A Single-Pass Learned Index, Andreas Kipf, Ryan Marcus, MIT CSAIL; Alexander van Renen, Mihail Stoian, Alfons Kemper, TU Munich; Tim Kraska, MIT CSAIL, and Thomas Neumann, TU Munich

    • PartLy: Learning Data Partitioning for Distributed Data Stream Processing, Ahmed Abdelhamid and Walid Aref, Purdue University

    Organization

    Workshop Co-Chairs

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

    Program Committee

    • Uri Alon, Technion
    • Yael Amsterdamer, Bar Ilan University
    • Bortik Bandyopadhyay, Ohio State University
    • Carsten Binnig, TU Darmstadt
    • Donatella Firmani, Roma Tre University
    • Johannes Gehrke, Microsoft
    • Justin Gottschlich, Intel Labs
    • Ryan Marcus, MIT
    • Shaikh Quader, IBM Cloud and Cognitive Software
    • Iryna Skrypnyk, Pfizer
    • Kavitha Srinivas, IBM Research
    • Seema Sundara, Oracle Labs
    • Lucas Woltmann, TU Dresden
    • Liqi Xu, University of Illinois

    Submission Instructions

    Important Dates 

    • Paper Submission: Wednesday, 25th March 2020, 12 pm PST
    • Notification of Acceptance: Friday, 24th April, 2020
    • Camera-ready Submission: Friday, 1st May, 2020
    • Workshop Date: Friday, 19th June, 2020

    Submission Site 

    All submissions will be handled electronically via EasyChair.

    Formatting Guidelines 

    We will use the same document templates as the SIGMOD/PODS'20 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.