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