Time: August 26, 2019 (Monday)

Location: San Jose Room

09:00 - 10:30 | Session 1

Keynote 1 (45 min)

• Self-Driving Databases: The Hard Parts. Andy Pavlo (CMU) [slides] [screen capture] [video]

Research Talks 1 & 2 (22.5 min each)

• Estimating Filtered Group-By Queries is Hard: Deep Learning to the Rescue. Andreas Kipf (TUM); Michael J Freitag (TUM); Dimitri Vorona (TUM); Peter Boncz (Centrum Wiskunde & Informatica); Thomas Neumann (TUM); Alfons Kemper (TUM) [pdf] [slides] [video]
• Accelerating B+tree Search by Using Simple Machine Learning Techniques. Anisa LLaveshi (TUM); Utku Sirin (EPFL); Robert West (EPFL); Anastasia Ailamaki (EPFL) [pdf] [slides] [video]

10:30 - 11:00 | Coffee Break

11:00 - 12:30 | Session 2

Keynote 2 (45 min)

• Journey to SDDP: Towards Building a Self-Driving Database Platform. Feifei Li (Alibaba & University of Utah) [video]

Research Talks 3 & 4 (22.5 min each)

• Demonstrating Semantic SQL Queries over Relational Data using the AI-Powered Database. Jose Neves (IBM Systems); Rajesh Bordawekar (IBM T. J. Watson Research Center); Elpida Tzortzatos (IBM Systems) [pdf] [slides][video]
• "Amnesia" - Towards Machine Learning Models That Can Forget User Data Very Fast. Sebastian Schelter (New York University) [pdf] [slides] [video]

12:30 - 14:00 | Lunch Break

14:00 - 15:30 | Session 3

Keynote 3 (45 min)

• AI-Powered Database Systems. Johannes Gehrke (Microsoft)

Research Talks 5 & 6 (22.5 min each)

• Towards Model-based Approximate Query Processing. Moritz Kulessa (TU Darmstadt); Benjamin Hilprecht (TU Darmstadt); Alejandro Molina (TU Darmstadt); Carsten Binnig (TU Darmstadt); Kristian Kersting (TU Darmstadt) [pdf] [video]
• 3 Lessons Learned from Implementing a Deep Reinforcement Learning Framework for Data Exploration. Amit Somech (Tel Aviv University); Tova Milo (Tel Aviv University); Ori Barel (Tel Aviv University) [pdf] [video]

15:30 - 16:00 | Coffee Break

16:00 - 17:30 | Session 3

Innovative Talks (17.5 min each)

• Query Optimization and Deep Learning: Derailing the Hype Train. Ryan Marcus (MIT) [slides] [video]
• Relational Query Optimization Meets Machine Learning. Yao Lu (Microsoft Research) [slides] [video]
• How do Database Technologies Help Machine Learning Systems? Zeke Wang (ETH Zurich) [slides] [video]
• ThunderML: Machine Learning Systems on Heterogeneous Architectures. Zeyi Wen (National University of Singapore) [slides] [video]

Lightning talks (10 min each)

• Learned Operator Cost Models. Jan Kossmann (Hasso Plattner Institute) [slides] [video]
• Learning from Query vs Learning from Data. Chenggang Wu (UC Berkeley) [slides] [video]