王若愚
人工智能科研狗,万众创业打工人,猫狗双全铲屎官,文理双修键盘侠
主要期刊
- R. Wang, D. Sun, and R. K. Wong, "Symbolic minimization on relational data," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9307-9318, 2023.🔗
- R. Wang, D. Sun, R. K. Wong, and R. Ranjan, "SIB: Sorted-Integers-Based Index for Compact and Fast Caching in Top-Down Logic Rule Mining Targeting KB Compression," Software: Practice and Experience, 2025.🔗
- R. Wang, R. Wong, and D. Sun, "Estimation-based optimizations for the semantic compression of RDF knowledge bases," Information Processsing and Management, vol. 61, no. 5, p. 103799, 2024.🔗
- R. Wang, D. Sun, R. K. Wong, and R. Ranjan, "Horn rule discovery with batched caching and rule identifier for proficient compressor of knowledge data," Software: Practice and Experience, vol. 53, no. 3, pp. 682-703, 2023.🔗
- R. Wang, D. Sun, R. K. Wong, R. Ranjan, and A. Y. Zomaya, "Sinc: Semantic approach and enhancement for relational data compression," Knowledge-Based Systems, vol. 258, p. 110001, 2022.🔗
- R. Wang, D. Sun, G. Li, R. K. Wong, and S. Chen, "Pipeline provenance for cloud-based big data analytics," Software: Practice and Experience, vol. 50, no. 5, pp. 658-674, 2020.🔗
主要会议
- R. Wang, R. K. Wong, and D. Sun, "Efficient Pruning via Entailment Cardinality Estimation for Fast Top-down Logic Rule Mining," in the 2025 IEEE International Conference on Data Engineering, ICDE 25.🔗
- R. Wang, D. Sun, and R. K. Wong, "RDF knowledge base summarization by inducing first-order horn rules," in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD,Proceedings, Part II, ser. Lecture Notes in Computer Science, vol. 13714. Springer, 2022, pp. 188-204.🔗
- R. Wang X. Hu, D. Sun, G. Li, R. K. Wong, S. Chen, and J. Liu, "Statistical detection of collective data fraud," in IEEE International Conference on Multimedia and Expo, ICME. IEEE, 2020, pp. 1-6.🔗
- R. Wang, D. Sun, G. Li, M. Atif, and S. Nepal, "Logprov: Logging events as provenance of big data analytics pipelines with trustworthiness," in IEEE International Conference on Big Data. IEEE Computer Society, 2016, pp. 1402-1411.🔗