Program Overview

The program will consist of keynotes, research sessions, poster session, reception, and conference banquet. Each day will begin with a light breakfast and registration. The research sessions will include presentations of full and short research papers.

Day 1 (Monday, July 10) Overview

Time	Events
09:00 - 09:30	Registration with coffee and light breakfast
09:30 - 09:45	SSDBM Opening by Carl Kesselman
09:45 - 10:45	Keynote 1 by Helen M. Berman
10:45 - 11:00	Break
11:00 - 12:00	Research Session 1: Machine Learning & Applications Session Chair: Rob Schuler
12:00 - 13:30	Lunch Break
13:30 - 15:00	Research Session 2: Trust & Privacy Session Chair: Jia Zou
15:00 - 15:30	Break
15:30 - 16:45	Research Session 3: Data Science, Systems & Applications Session Chair: John Wu
16:45 - 17:00	Break
17:00 - 19:00	Poster Session and Reception

Day 2 (Tuesday, July 11) Overview

Time	Events
09:00 - 09:30	Registration with coffee and light breakfast
09:30 - 10:30	Keynote 2 by Chris Mattmann
10:30 - 10:45	Break
10:45 - 12:15	Research Session 4: Data Mining Session Chair: Alejandro Bugacov
12:15 - 13:30	Lunch Break
13:30 - 14:30	Keynote 3 by Andy Maltz
14:30 - 14:45	Break
14:45 - 16:45	Research Session 5: Indexing & Querying Session Chair: Qiang Zhu
16:45 - 17:00	Announcement of SSDBM 2024
17:00 - 19:00	Excursion
19:00 - 21:00	Conference Banquet

Day 3 (Wednesday, July 12) Overview

Time	Events
09:00 - 09:30	Registration with coffee and light breakfast
09:30 - 11:00	Research Session 6: Graphs & Data Mining (Hybrid I) Session Chair: Seokki Lee
11:00 - 11:15	Break
11:15 - 12:30	Research Session 7: Graphs & Data Mining (Hybrid II) Session Chair: Rob Schuler
12:30 - 14:00	Closing and Lunch

Research Sessions

See accepted papers to read the abstracts for the complete list of full, short, demonstration, and poster papers to appear at SSDBM 2023.

Day 1 (Monday, July 10)

Research Session 1: Machine Learning & Applications

• ST-CopulaGNN : A Multi-View Spatio-Temporal Graph Neural Network for Traffic Forecasting Pitikorn Khlaisamniang and Suronapee Phoomvuthisarn
• LearnedSort as a learning-augmented SampleSort: Analysis and Parallelization Ivan Carvalho and Ramon Lawrence

Research Session 2: Trust & Privacy

• Federated Learning on Personal Data Management Systems: Decentralized and Reliable Secure Aggregation Protocols Julien Mirval, Luc Bouganim and Iulian Sandu Popa
• Privacy-Preserving OLAP via Modeling and Analysis of Query Workloads: Innovative Theories and Theorems Alfredo Cuzzocrea [remote]
• ESM$^2$-Tree: An maintenance efficient authentication data structure in blockchain Yuzhou Fang, Weiwei Qiu, Fanglei Huang and Liang Cai [remote]

Research Session 3: Data Science, Systems & Applications

• Fast Algorithm for Embedded Order Dependency Validation Daichi Amagata, Alejandro Ramos, Ryo Shirai and Takahiro Hara (short)
• Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing? Jonathan Will, Lauritz Thamsen, Dominik Scheinert and Odej Kao (short)
• InfoMoD: Information-theoretic Model Diagnostics Armin Esmaeilzadeh, Lukasz Golab and Kazem Taghva (short)
• Early ICU Mortality Prediction with Deep Federated Learning: A Real-World Scenario Athanasios Georgoutsos, Paraskevas Kerasiotis and Verena Kantere (short)
• Privacy-Preserving Redaction of Diagnosis Data through Source Code Analysis Lixi Zhou, Lei Yu, Jia Zou and Hong Min (short)

Day 2 (Tuesday, July 11)

Research Session 4: Data Mining

• A Computer Vision Approach for Detecting Discrepancies in Map Textual Labels Abdulrahman Salama, Mahmoud Elkamhawy, Mohamed Ali, Ehab Al-Masri, Adel Sabour, Abdeltawab Hendawi, Ming Tan, Vashutosh Agrawal and Ravi Prakash
• Data Driven Dimensionality Reduction to Improve Modeling Performance Joshua Chung, Marcos Lopez de Prado, Horst Simon and Kesheng Wu
• Evaluating Autoencoders for Dimensionality Reduction of MRI-derived Radiomics and Classification of Malignant Brain Tumors Mikayla Biggs, Yaohua Wang, Neetu Soni, Sarv Priya, Girish Bathla and Guadalupe Canahuate

Research Session 5: Indexing & Query

• Less is More: How Fewer Results Improve Progressive Join Query Processing Xin Zhang and Ahmed Eldawy
• Indexing Temporal Relations for Range-Duration Queries Matteo Ceccarello, Anton Dignös, Johann Gamper and Christina Khnaisser
• Towards Efficient Discovery of Spatially Interesting Patterns in Geo-referenced Sequential Databases Uday Kiran Rage and Shota Suzuki
• Accelerating Machine Learning Queries with Linear Algebra Query Processing Wenbo Sun, Asterios Katsifodimos and Rihan Hai [remote]

Day 3 (Wednesday, July 12)

Research Session 6: Graphs & Data Mining (Hybrid I)

• Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection Bingbing Xie, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue and Hao Fan [remote]
• Multi-representations Space Separation based Graph-level Anomaly-aware Detection Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang and Xuexiong Luo [remote]
• A benchmark for Dynamic Graph version management Zeng Chenglin, Wang Huajin, Shen Zhihong and Hu Chuan [remote]

Research Session 7: Graphs & Data Mining (Hybrid II)

• A Long term Time Series Forecasting method with Multiple Decomposition Yang Wang, Shuyang Wang, Xu Chen and Yongjun Jing [remote]
• MSLS: Meta-graph Search with Learnable Supernet for Heterogeneous Graph Neural Networks Yili Wang, Chen Jiamin, Qiutong Li, Changlong He and Jianliang Gao (short) [remote]
• Decoupled Graph Neural Architecture Search with Variable Propagation Operation and Appropriate Depth Jianliang Gao, Changlong He, Jiamin Chen, Qiutong Li and Yili Wang (short) [remote]
• TGSLN: Time-aware Graph Structure Learning Network for Multi-variates Stock Sector Ranking Recommendation Quan Wan, Shuo Yin, Xiangyue Liu, Jianliang Gao and Yuhui Zhong (short) [remote]

The schedule of paper presentations may be subject to last minute changes, so please contact us if you have time-sensitive questions about the schedule.