|
Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (4): 721-740.doi: 10.1007/s11390-021-1348-2
Special Issue: Data Management and Data Mining
• Special Section on AI4DB and DB4AI • Previous Articles Next Articles
Zhou Zhang1,2, Pei-Quan Jin1,2,*, Senior Member, CCF, Member, ACM, IEEE, Xiao-Liang Wang1,2, Yan-Qi Lv1,2, Shou-Hong Wan1,2, Member, ACM, IEEE, and Xi-Ke Xie1, Member, ACM, IEEE
[1] Kraska T, Beutel A, Chi E H, Dean J, Polyzotis N. The case for learned index structures. In Proc. the 2018 International Conference on Management of Data, Jun. 2018, pp.489-504. DOI:10.1145/3183713.3196909. [2] Galakatos A, Markovitch M, Binnig C, Fonseca R, Kraska T. FITing-Tree:A data-aware index structure. In Proc. the 2019 International Conference on Management of Data, Jun. 2019, pp.1189-1206. DOI:10.1145/3299869.3319860. [3] Ferragina P, Vinciguerra G. The PGM-index:A fullydynamic compressed learned index with provable worst-case bounds. Proceedings of the VLDB Endowment, 2020, 13(8):1162-1175. DOI:10.14778/3389133.3389135. [4] Ding J, Minhas U F, Yu J et al. ALEX:An updatable adaptive learned index. In Proc. the 2020 ACM International Conference on Management of Data, Jun. 2020, pp.969-984. DOI:10.1145/3318464.3389711. [5] Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q V, Hinton G E, Dean J. Outrageously large neural networks:The sparsely-gated mixture-of-experts layer. In Proc. the 5th International Conference on Learning Representations, April 2017. [6] Liu X, Lin Z, Wang H. Novel online methods for time series segmentation. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(12):1616-1626. DOI:10.1109/TKDE.2008.29. [7] Xu Z, Zhang R, Ramamohanarao K, Parampalli U. An adaptive algorithm for online time series segmentation with error bound guarantee. In Proc. the 15th International Conference on Extending Database Technology, Mar. 2012, pp.192-203. DOI:10.1145/2247596.2247620. [8] Xie Q, Pang C, Zhou X, Zhang X, Deng K. Maximum errorbounded piecewise linear representation for online stream approximation. The VLDB Journal, 2014, 23(6):915-937. DOI:10.1007/s00778-014-0355-0. [9] Bentley J L, Yao A C. An almost optimal algorithm for unbounded searching. Information Processing Letters, 1976, 5(3):82-87. DOI:10.1016/0020-0190(76)90071-5. [10] Hadian A, Heinis T. Considerations for handling updates in learned index structures. In Proc. the 2nd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, Jul. 2019, Article No. 3. DOI:10.1145/3329859.3329874. [11] Li X, Li J, Wang X. ASLM:Adaptive single layer model for learned index. In Proc. the 2019 International Conference on Database Systems for Advanced Applications, Apr. 2019, pp.80-95. DOI:10.1007/978-3-030-18590-96. [12] O'Neil P, Cheng E Y, Gawlick D, Oneil E. The logstructured merge-tree (LSM-tree). Acta Informatica, 1996, 33(4):351-385. DOI:10.1007/s002360050048. [13] Bender M A, Hu H. An adaptive packed-memory array. ACM Transactions on Database Systems, 2007, 32(4):Article No. 26. DOI:10.1145/1292609.1292616. [14] Ailamaki A, DeWitt D, Hill M, Wood D. DBMSs on a modern processor:Where does time go? In Proc. the 25th International Conference on Very Large Data Bases, Sept. 1999, pp.266-277. [15] Hadian A, Heinis T. Shift-Table:A low-latency learned index for range queries using model correction. In Proc. the 24th International Conference on Extending Database Technology, Mar. 2021, pp.253-264. DOI:10.5441/002/edbt.2021.23. [16] Tang C, Wang Y, Hu G et al. XIndex:A scalable learned index for multicore data storage. In Proc. the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Feb. 2020, pp.308-320. DOI:10.1145/33-32466.3374547. [17] Kipf A, Marcus R, van Renen A, Stoian M, Kemper A, Kraska T, Neumann T. RadixSpline:A single-pass learned index. In Proc. the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, Jun. 2020, Article No. 5. DOI:10.1145/34010-71.3401659. [18] Neumann T, Michel S. Smooth interpolating histograms with error guarantees. In Proc. the 25th British National Conference on Databases, July 2008, pp.126-138. DOI:10.1007/978-3-540-70504-812. [19] Bilgram R. Cost models for learned index with insertions[Master Thesis]. Department of Computer Science, Aalborg University, 2019. [20] Wang Y, Tang C, Wang Z, Chen H. SIndex:A scalable learned index for string keys. In Proc. the 11th ACM SIGOPS Asia-Pacific Workshop on Systems, Aug. 2020, pp.17-24. DOI:10.1145/3409963.3410496. [21] Llaveshi A, Sirin U, Ailamaki A, West R. Accelerating B+-tree search by using simple machine learning techniques. In Proc. the 1st International Workshop on Applied AI for Database Systems and Applications, Aug. 2019. [22] Hadian A, Heinis T. Interpolation-friendly B-trees:Bridging the gap between algorithmic and learned indexes. In Proc. the 22nd International Conference on Extending Database Technology, Mar. 2019, pp.710-713. DOI:10.5441/002/edbt.2019.93. [23] Hadian A, Heinis T. MADEX:Learning-augmented algorithmic index structures. In Proc. the 2nd International Workshop on Applied AI for Database Systems and Applications, Aug. 2020. [24] Li P, Lu H, Zheng Q, Yang L, Pan G. LISA:A learned index structure for spatial data. In Proc. the 2020 International Conference on Management of Data, Jun. 2020, pp.2119-2133. DOI:10.1145/3318464.3389703. [25] Qi J, Liu G, Jensen C S, Kulik L. Effectively learning spatial indices. Proceedings of the VLDB Endowment, 2020, 13(11):2341-2354. DOI:10.14778/3407790.3407829. [26] Nathan V, Ding J, Alizadeh M, Kraska T. Learning multidimensional indexes. In Proc. the 2020 International Conference on Management of Data, Jun. 2020, pp.985-1000. DOI:10.1145/3318464.3380579. [27] Ding J, Nathan V, Alizadeh M, Kraska T. Tsunami:A learned multi-dimensional index for correlated data and skewed workloads. Proceedings of the VLDB Endowment, 2020, 14(2):74-86. DOI:10.14778/3425879.3425880. [28] Zhou X, Chai C, Li G, Sun J. Database meets artificial intelligence:A survey. IEEE Transactions on Knowledge and Data Engineering. DOI:10.1109/TKDE.2020.2994641. [29] Sun J, Li G. An end-to-end learning-based cost estimator. Proceedings of the VLDB Endowment, 2019, 13(3):307-319. DOI:10.14778/3368289.3368296. [30] Rodriguez L V, Yusuf F, Lyons S, Paz E, Rangaswami R, Liu J, Zhao M, Narasimhan G. Learning cache replacement with CACHEUS. In Proc. the 19th USENIX Conference on File and Storage Technologies, Feb. 2021, pp.341-354. [31] Zhou X, Sun J, Li G, Feng J. Query performance prediction for concurrent queries using graph embedding. Proceedings of the VLDB Endowment, 2020, 13(9):1416-1428. DOI:10.14778/3397230.3397238. [32] Fan J, Liu T, Li G, Chen J, Shen Y, Du X. Relational data synthesis using generative adversarial networks:A design space exploration. Proceedings of the VLDB Endowment, 2020, 13(11):1962-1975. DOI:10.14778/3407790.3407802. [33] Cooper B F, Silberstein A, Tam E, Ramakrishnan R, Sears R. Benchmarking cloud serving systems with YCSB. In Proc. the 1st ACM Symposium on Cloud Computing, Jun. 2010, pp.143-154. DOI:10.1145/1807128.1807152. [34] Jin P, Ou Y, Härder T, Li Z. AD-LRU:An efficient buffer replacement algorithm for flash-based databases. Data & Knowledge Engineering, 2012, 72:83-102. DOI:10.1016/j.datak.2011.09.007. |
[1] | Hua Luan, Student Member, CCF, Xiao-Yong Du, Member, CCF, and Shan Wang, Senior Member, CCF, Member, ACM. Cache-Conscious Data Cube Computation on a Modern Processor [J]. , 2009, 24(4): 708-722. |
[2] | Yi-Ci Cai, Xin Zhao, Qiang Zhou, and Xian-Long Hong. Shielding Area Optimization Under the Solution of Interconnect Crosstalk [J]. , 2005, 20(6): 901-906 . |
[3] | Dian Zhou[1,2] and Rui-Ming Li[1]. Design and Verification of High-Speed VLSI Physical Design [J]. , 2005, 20(2): 147-165. |
[4] | Abdil Rashid Mohamed, Zebo Peng, and Petru Eles. A Wiring-Aware Approach to Minimizing Built-In Self-Test Overhead [J]. , 2005, 20(2): 216-223. |
|
|