Wang, R., Wang, J., Idreos, S., Ozsu, T., & Aref, W. G. (2022). The Case for Distributed Shared-Memory Databases With RDMA-Enabled Memory Disaggregation Proceedings of the VLDB Endowment (PVLDB), 16, 15-22. Retrieved from https://www.vldb.org/pvldb/vol16/p15-wang.pdf
References
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Xia, K., Zhao, W., Jolfaei, A., & Ozsu, T. (2022). Introduction to the Special Section on Edge/Fog Computing for Infectious Disease Intelligence ACM Transactions on Internet Technology (TOIT), 22, 1-63. https://doi.org/10.1145/3494119
Devins, J., Tibshirani, J., & Lin, J. (2022). Aligning the Research and Practice of Building Search Applications: Elasticsearch and Pyserini Presented at the Aligning the Research and Practice of Building Search Applications: Elasticsearch and Pyserini conference. https://doi.org/10.1145/3488560.3502186
Ma, X., Pradeep, R., Nogueira, R., & Lin, J. (2022). Document Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 Presented at the Document Expansion Baselines and Learned Sparse Lexical Representations For MS MARCO V1 and V2 conference. https://doi.org/10.1145/3477495.3531749
Arabzadeh, N., Seifikar, M., & Clarke, C. (2022). Unsupervised Question Clarity Prediction Through Retrieved Item Coherency ArXiv, abs/2208.04882. https://doi.org/10.48550/arXiv.2208.04882
Jiang, Z., Yang, M. Y. R., Tsirlin, M., Tang, R., & Lin, J. (2022). Less Is More: Parameter-Free Text Classification With Gzip ArXiv, abs/2212.09410. https://doi.org/10.48550/arXiv.2212.09410
Li, H., Wang, S., Zhuang, S., Mourad, A., Ma, X., Lin, J., & Zuccon, G. (2022). To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers Presented at the To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers conference. https://doi.org/10.1145/3477495.3531884
Thakur, N., Reimers, N., & Lin, J. (2022). Domain Adaptation for Memory-Efficient Dense Retrieval ArXiv, abs/2205.11498. https://doi.org/10.48550/arXiv.2205.11498
Mazmudar, M., Humphries, T., Liu, J., Rafuse, M., & He, X. (2022). Cache Me if You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration Proceedings of the VLDB Endowment (PVLDB), 16, 574-586. Retrieved from https://www.vldb.org/pvldb/vol16/p574-mazmudar.pdf
Guo, R., Guo, V., Kim, A., Hildred, J., & Daudjee, K. (2022). Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers Presented at the Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers conference. Retrieved from https://proceedings.mlsys.org/paper/2022/hash/ea5d2f1c4608232e07d3aa3d998e5135-Abstract.html