| Vamsa: Automated provenance tracking in data science scripts MH Namaki, A Floratou, F Psallidas, S Krishnan, A Agrawal, Y Wu, Y Zhu, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 65 | 2020 |
| Discovering Graph Temporal Association Rules MH Namaki, Y Wu, Q Song, P Lin, T Ge 26th International Conference on Information and Knowledge Management (CIKM), 2017 | 57 | 2017 |
| Answering why-questions for subgraph queries in multi-attributed graphs Q Song, MH Namaki, Y Wu 2019 IEEE 35th International Conference on Data Engineering (ICDE), 40-51, 2019 | 28 | 2019 |
| Minimizing maximum tardiness and delivery costs in a batched delivery system MM Mazdeh, M Rostami, MH Namaki Computers & Industrial Engineering 66 (4), 675-682, 2013 | 27 | 2013 |
| Answering why-questions by exemplars in attributed graphs MH Namaki, Q Song, Y Wu, S Yang Proceedings of the 2019 International Conference on Management of Data, 1481 …, 2019 | 24 | 2019 |
| Learning to Speed Up Query Planning in Graph Databases MH Namaki, R Rahman Chuwdhury, R Islam, JR Doppa, Y Wu Proceedings of the Twenty-Seventh International Conference on Automated …, 2017 | 19 | 2017 |
| Gexp: Cost-aware graph exploration with keywords MH Namaki, Y Wu, X Zhang Proceedings of the 2018 International Conference on Management of Data, 1729 …, 2018 | 17 | 2018 |
| Event Pattern Discovery by Keywords in Graph Streams MH Namaki, P Lin, Y Wu 2017 IEEE International Conference on Big Data (Big Data), 982-987, 2017 | 17 | 2017 |
| Vamsa: Tracking Provenance in Data Science Scripts MH Namaki, A Floratou, F Psallidas, S Krishnan, A Agrawal, Y Wu arXiv preprint, 2020 | 14 | 2020 |
| Select-and-evaluate: A learning framework for large-scale knowledge graph search FARR Chowdhury, C Ma, MR Islam, MH Namaki, MO Faruk, JR Doppa Asian conference on machine learning, 129-144, 2017 | 13 | 2017 |
| BEAMS: Bounded Event Detection in Graph Streams MH Namaki, K Sasani, Y Wu, T Ge 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017 | 13 | 2017 |
| Performance Prediction for Graph Queries MH Namaki, K Sasani, Y Wu, A Gebremedhin Proceedings of the 2nd ACM SIGMOD Workshop on Network Data Analytics, 2017 | 11 | 2017 |
| Spatio-temporal deep graph network for event detection, localization, and classification in cyber-physical electric distribution system A Ahmed, S Basumallik, A Gholami, SK Sadanandan, MHN Namaki, ... IEEE Transactions on Industrial Informatics 20 (2), 2397-2407, 2023 | 9 | 2023 |
| Multi-metric graph query performance prediction K Sasani, MH Namaki, Y Wu, AH Gebremedhin Database Systems for Advanced Applications: 23rd International Conference …, 2018 | 7 | 2018 |
| Answering why-questions for subgraph queries Q Song, MH Namaki, P Lin, Y Wu IEEE Transactions on Knowledge and Data Engineering 34 (10), 4636-4649, 2020 | 6 | 2020 |
| Quality-aware keyword query suggestion and evaluation MH Namaki, Y Wu, X Zhang, Y Ding US Patent 11,514,049, 2022 | 5 | 2022 |
| Navigate: Explainable visual graph exploration by examples MH Namaki, Q Song, Y Wu Proceedings of the 2019 International Conference on Management of Data, 1965 …, 2019 | 5 | 2019 |
| Network Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach K Sasani, MH Namaki, A Gebremedhin GraML 2018 Workshop, 2018 | 3 | 2018 |
| Tracking provenance in data science scripts A Floratou, A Agrawal, MH Namaki, SV Krishnan, F Psallidas, Y Wu US Patent 11,775,862, 2023 | 2 | 2023 |
| Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts F Psallidas, ME Leszczynski, MH Namaki, A Floratou, A Agrawal, ... Companion of the 2023 International Conference on Management of Data, 123-126, 2023 | 2 | 2023 |
Yinghui WuCase Western Reserve UniversityBestätigte E-Mail-Adresse bei case.edu