Research

Publications

S. Horchidan, PH. Chen, E. Kritharakis, P. Carbone, and V. Kalavri. Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems. In Proceedings of the 27th International Conference on Extending Database Technology (EDBT) [link]

S. Horchidan. Query Optimization for Inference-Based Graph Databases. In Proceedings of the VLDB 2023 PhD Workshop, co-located with the 49th International Conference on Very Large Data Bases (VLDB 2023) [BEST PAPER AWARD] [link]

S. Horchidan, and P. Carbone. ORB: Empowering Graph Queries through Inference. In Proceedings of 1st International Workshop on Data Management for Knowledge Graphs (DMKG ‘23), co-located with ESWC 2023 [link]

S. Horchidan, E. Kritharakis, V. Kalavri, and P. Carbone. Evaluating Model Serving Strategies Over Streaming Data. In Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning (DEEM ‘22), co-located with ACM SIGMOD/PODS 2022. [BEST PAPER AWARD] [BEST PRESENTATION AWARD] [link]

M. Zwolak, Z. Abbas, S. Horchidan, P. Carbone, and V. Kalavri. 2022. GCNSplit: Bounding the State of Streaming Graph Partitioning. In Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM ‘22), co-located with ACM SIGMOD/PODS 2022 [link]

S. Imtiaz, S. F. Horchidan, Z. Abbas, M. Arsalan, H. N. Chaudhry and V. Vlassov, “Privacy Preserving Time-Series Forecasting of User Health Data Streams” 2020 IEEE International Conference on Big Data (Big Data) [link]

S. F. Horchidan, “Real-time forecasting of dietary habits and user health using Federated Learning with privacy guarantees”, KTH Royal Institute of Technology, Dissertation, 2020 [link]

Service

Program Committee member @ aiDM 2024, co-located with ACM SIGMOD/PODS.

Availability and Reproducibility Committee member @ ACM SIGMOD/PODS 2023.

Availability Committee member @ ACM SIGMOD/PODS 2022.