{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T11:40:06Z","timestamp":1747309206608,"version":"3.40.5"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100025194","name":"Roskilde University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100025194","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["The VLDB Journal"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Approximate query processing (<jats:bold>AQP<\/jats:bold>) plays a critical role in modern data analytics. Although machine learning models are used for AQP, existing methods fail to uncover implicit relationships among the underlying data, the aggregate functions in queries, and the query predicates. In this work, we propose a <jats:underline>G<\/jats:underline>raph <jats:underline>RE<\/jats:underline>presentation <jats:underline>L<\/jats:underline>earning-based <jats:underline>A<\/jats:underline>QP model (<jats:bold>GRELA<\/jats:bold> for short) for answering queries with multiple aggregate functions. GRELA models the aggregate functions and the query predicates as task and clause nodes respectively in a graph and then learns appropriate node representations via its two modules. In particular, the <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\texttt {Encoder}$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>Encoder<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> module coalesces query predicates and underlying data into the representations of clause nodes. The <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\mathbf {\\texttt {Graph}}$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>Graph<\/mml:mi>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> module bridges task nodes and clause nodes such that each task node can aggregate the information from its neighborhood into its representation. Through the inner products of clause and task representations, GRELA is able to make accurate estimates for queries with multiple aggregate functions. Extensive experimental results verify that GRELA outperforms the state-of-the-art AQP methods on different kinds of datasets.<\/jats:p>","DOI":"10.1007\/s00778-025-00914-y","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T21:16:37Z","timestamp":1745529397000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GRELA: Exploiting graph representation learning in effective approximate query processing"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1484-0698","authenticated-orcid":false,"given":"Pengfei","family":"Li","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wenqing","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Rong","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Bolin","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Shuxian","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"914_CR1","unstructured":"https:\/\/github.com\/pfl-cs\/GRELA, online"},{"key":"914_CR2","unstructured":"https:\/\/relational.fit.cvut.cz\/dataset\/Stats, online"},{"key":"914_CR3","unstructured":"http:\/\/homepages.cwi.nl\/~boncz\/job\/imdb.tgz, online"},{"key":"914_CR4","unstructured":"https:\/\/www.tpc.org\/tpc_documents_current_versions\/current_specifications5.asp, online"},{"key":"914_CR5","doi-asserted-by":"crossref","unstructured":"Acharya, Swarup, Gibbons, Phillip\u00a0B., Poosala, Viswanath, Ramaswamy, Sridhar: The aqua approximate query answering system. In SIGMOD, pages 574\u2013576, (1999)","DOI":"10.1145\/304182.304581"},{"key":"914_CR6","doi-asserted-by":"crossref","unstructured":"Acharya, Swarup, Gibbons, Phillip\u00a0B., Poosala, Viswanath, Ramaswamy, Sridhar: Join synopses for approximate query answering. In SIGMOD, pages 275\u2013286, (1999)","DOI":"10.1145\/304182.304207"},{"key":"914_CR7","doi-asserted-by":"crossref","unstructured":"Agarwal, Sameer, Mozafari, Barzan, Panda, Aurojit, Milner, Henry, Madden, Samuel, Stoica, Ion: Blinkdb: queries with bounded errors and bounded response times on very large data. In EuroSys, pages 29\u201342, (2013)","DOI":"10.1145\/2465351.2465355"},{"key":"914_CR8","doi-asserted-by":"crossref","unstructured":"Babcock, Brian, Chaudhuri, Surajit, Das, Gautam: Dynamic sample selection for approximate query processing. In SIGMOD, pages 539\u2013550, (2003)","DOI":"10.1145\/872757.872822"},{"key":"914_CR9","unstructured":"Cao, Kaidi, You, Jiaxuan, Leskovec, Jure: Modeling relations between data and tasks. In ICLR, Relational multi-task learning (2022)"},{"issue":"4","key":"914_CR10","doi-asserted-by":"publisher","first-page":"401","DOI":"10.14778\/2735496.2735503","volume":"8","author":"Badrish Chandramouli","year":"2014","unstructured":"Chandramouli, Badrish, Goldstein, Jonathan, Barnett, Mike, DeLine, Robert, Platt, John C., Terwilliger, James F., Wernsing, John: Trill: A high-performance incremental query processor for diverse analytics. Proc. VLDB Endow. 8(4), 401\u2013412 (2014)","journal-title":"Proc. VLDB Endow."},{"issue":"2","key":"914_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1145\/1242524.1242526","volume":"32","author":"Surajit Chaudhuri","year":"2007","unstructured":"Chaudhuri, Surajit, Das, Gautam, Narasayya, Vivek R.: Optimized stratified sampling for approximate query processing. ACM Trans. Database Syst. 32(2), 9 (2007)","journal-title":"ACM Trans. Database Syst."},{"key":"914_CR12","doi-asserted-by":"crossref","unstructured":"Chaudhuri, Surajit, Ding, Bolin, Kandula, Srikanth: Approximate query processing: No silver bullet. In SIGMOD, pages 511\u2013519, (2017)","DOI":"10.1145\/3035918.3056097"},{"key":"914_CR13","unstructured":"Condie, Tyson, Conway, Neil, Alvaro, Peter, Hellerstein, Joseph\u00a0M., Elmeleegy, Khaled, Sears, Russell: Mapreduce online. In NSDI, pages 313\u2013328, (2010)"},{"key":"914_CR14","doi-asserted-by":"crossref","unstructured":"Considine, Jeffrey, Li, Feifei, Kollios, George, Byers, John\u00a0W.: Approximate aggregation techniques for sensor databases. In ICDE, pages 449\u2013460, (2004)","DOI":"10.1109\/ICDE.2004.1320018"},{"key":"914_CR15","unstructured":"Cormode, Graham: Sketch techniques for approximate query processing. Foundations and Trends in Databases. NOW publishers, page\u00a015, (2011)"},{"issue":"1\u20133","key":"914_CR16","first-page":"1","volume":"4","author":"Graham Cormode","year":"2012","unstructured":"Cormode, Graham, Garofalakis, Minos N., Haas, Peter J., Jermaine, Chris: Synopses for massive data: Samples, histograms, wavelets, sketches. Found. Trends Databases 4(1\u20133), 1\u2013294 (2012)","journal-title":"Found. Trends Databases"},{"issue":"1","key":"914_CR17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jalgor.2003.12.001","volume":"55","author":"Graham Cormode","year":"2005","unstructured":"Cormode, Graham, Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58\u201375 (2005)","journal-title":"J. Algorithms"},{"key":"914_CR18","unstructured":"Oracle Corporation. Mysql. (1995). Accessed: 2023-12-31"},{"key":"914_CR19","doi-asserted-by":"crossref","unstructured":"Durrett, Rick: Probability: theory and examples, volume\u00a049. (2019)","DOI":"10.1017\/9781108591034"},{"key":"914_CR20","unstructured":"Friedman, Avner: Foundations of modern analysis. Courier Corporation, (1982)"},{"issue":"10","key":"914_CR21","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.14778\/3115404.3115418","volume":"10","author":"Alex Galakatos","year":"2017","unstructured":"Galakatos, Alex, Crotty, Andrew, Zgraggen, Emanuel, Binnig, Carsten, Kraska, Tim: Revisiting reuse for approximate query processing. Proc. VLDB Endow. 10(10), 1142\u20131153 (2017)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR22","unstructured":"Ganti, Venkatesh, Lee, Mong-Li, Ramakrishnan, Raghu: ICICLES: self-tuning samples for approximate query answering. In VLDB, pages 176\u2013187, (2000)"},{"key":"914_CR23","unstructured":"Gibbons, Phillip\u00a0B., Poosala, Viswanath, Acharya, Swarup, Bartal, Yair, Matias, Yossi, Muthukrishnan, S., Ramaswamy, Sridhar, Suel, Torsten: Aqua: System and techniques for approximate query answering. Technical report, Technical report, Bell Labs, (1998)"},{"key":"914_CR24","first-page":"1263","volume":"70","author":"Justin Gilmer","year":"2017","unstructured":"Gilmer, Justin, Schoenholz, Samuel S., Riley, Patrick F., Vinyals, Oriol, Dahl, George E.: Neural message passing for quantum chemistry. In ICML 70, 1263\u20131272 (2017)","journal-title":"In ICML"},{"key":"914_CR25","first-page":"729","volume":"2","author":"Marco Gori","year":"2005","unstructured":"Gori, Marco, Monfardini, Gabriele, Scarselli, Franco: A new model for learning in graph domains. In IJCNN 2, 729\u2013734 (2005)","journal-title":"In IJCNN"},{"issue":"1","key":"914_CR26","doi-asserted-by":"publisher","first-page":"201","DOI":"10.14778\/1453856.1453883","volume":"1","author":"Marios Hadjieleftheriou","year":"2008","unstructured":"Hadjieleftheriou, Marios, Xiaohui, Yu., Koudas, Nick, Srivastava, Divesh: Hashed samples: selectivity estimators for set similarity selection queries. Proc. VLDB Endow. 1(1), 201\u2013212 (2008)","journal-title":"Proc. VLDB Endow."},{"issue":"4","key":"914_CR27","doi-asserted-by":"publisher","first-page":"752","DOI":"10.14778\/3503585.3503586","volume":"15","author":"Yuxing Han","year":"2021","unstructured":"Han, Yuxing, Ziniu, Wu., Peizhi, Wu., Zhu, Rong, Yang, Jingyi, Tan, Liang Wei, Zeng, Kai, Cong, Gao, Qin, Yanzhao, Pfadler, Andreas, Qian, Zhengping, Zhou, Jingren, Li, Jiangneng, Cui, Bin: Cardinality estimation in DBMS: A comprehensive benchmark evaluation. Proc. VLDB Endow. 15(4), 752\u2013765 (2021)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR28","doi-asserted-by":"crossref","unstructured":"Hasan, Shohedul, Thirumuruganathan, Saravanan, Augustine, Jees, Koudas, Nick, Das, Gautam: Deep learning models for selectivity estimation of multi-attribute queries. In SIGMOD, pages 1035\u20131050, (2020)","DOI":"10.1145\/3318464.3389741"},{"key":"914_CR29","doi-asserted-by":"crossref","unstructured":"He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian: Deep residual learning for image recognition. In CVPR, pages 770\u2013778, (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"914_CR30","doi-asserted-by":"crossref","unstructured":"Hellerstein, Joseph\u00a0M., Haas, Peter\u00a0J., Wang, Helen\u00a0J.: Online aggregation. In SIGMOD, pages 171\u2013182, (1997)","DOI":"10.1145\/253262.253291"},{"key":"914_CR31","unstructured":"Hermann, Karl\u00a0Moritz, Kocisk\u00fd, Tom\u00e1s, Grefenstette, Edward, Espeholt, Lasse, Kay, Will, Suleyman, Mustafa, Blunsom, Phil: Teaching machines to read and comprehend. In NeurIPS, pages 1693\u20131701, (2015)"},{"issue":"7","key":"914_CR32","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3384345.3384349","volume":"13","author":"Benjamin Hilprecht","year":"2020","unstructured":"Hilprecht, Benjamin, Schmidt, Andreas, Kulessa, Moritz, Molina, Alejandro, Kersting, Kristian, Binnig, Carsten: Deepdb: Learn from data, not from queries! Proc. VLDB Endow. 13(7), 992\u20131005 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR33","unstructured":"Inan, Hakan, Khosravi, Khashayar, Socher, Richard: A loss framework for language modeling. In ICLR, Tying word vectors and word classifiers (2017)"},{"key":"914_CR34","doi-asserted-by":"crossref","unstructured":"Kandula, Srikanth, Shanbhag, Anil, Vitorovic, Aleksandar, Olma, Matthaios, Grandl, Robert, Chaudhuri, Surajit, Ding, Bolin: Quickr: Lazily approximating complex adhoc queries in bigdata clusters. In SIGMOD, pages 631\u2013646, (2016)","DOI":"10.1145\/2882903.2882940"},{"key":"914_CR35","unstructured":"Kingma, Diederik, P., Ba, Jimmy: A method for stochastic optimization. In ICLR, Adam (2015)"},{"key":"914_CR36","unstructured":"Kingma, Diederik P., Welling, Max: Auto-encoding variational bayes, In ICLR (2014)"},{"key":"914_CR37","doi-asserted-by":"crossref","unstructured":"Kraska, Tim: Approximate query processing for interactive data science. In SIGMOD, page 525, (2017)","DOI":"10.1145\/3035918.3056099"},{"key":"914_CR38","doi-asserted-by":"crossref","unstructured":"Lee, Taewhi, Nam, Kihyuk, Park, Choon\u00a0Seo, Kim, Sung-Soo: Exploiting machine learning models for approximate query processing. In ICDE, pages 6752\u20136754, (2022)","DOI":"10.1109\/BigData55660.2022.10020252"},{"key":"914_CR39","first-page":"6437","volume":"139","author":"Guohao Li","year":"2021","unstructured":"Li, Guohao, M\u00fcller, Matthias, Ghanem, Bernard, Koltun, Vladlen: Training graph neural networks with 1000 layers. In ICML 139, 6437\u20136449 (2021)","journal-title":"In ICML"},{"issue":"4","key":"914_CR40","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s41019-018-0074-4","volume":"3","author":"Kaiyu Li","year":"2018","unstructured":"Li, Kaiyu, Li, Guoliang: Approximate query processing: What is new and where to go? - A survey on approximate query processing. Data Sci. Eng. 3(4), 379\u2013397 (2018)","journal-title":"Data Sci. Eng."},{"issue":"2","key":"914_CR41","doi-asserted-by":"publisher","first-page":"197","DOI":"10.14778\/3626292.3626302","volume":"17","author":"Pengfei Li","year":"2023","unstructured":"Li, Pengfei, Wei, Wenqing, Zhu, Rong, Ding, Bolin, Zhou, Jingren, Hua, Lu.: ALECE: an attention-based learned cardinality estimator for SPJ queries on dynamic workloads. Proc. VLDB Endow. 17(2), 197\u2013210 (2023)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR42","doi-asserted-by":"crossref","unstructured":"Liang, Xi, Sintos, Stavros, Krishnan, Sanjay: Janusaqp: Efficient partition tree maintenance for dynamic approximate query processing. In ICDE, pages 572\u2013584, (2023)","DOI":"10.1109\/ICDE55515.2023.00050"},{"key":"914_CR43","doi-asserted-by":"crossref","unstructured":"Liang, Xi, Sintos, Stavros, Shang, Zechao, Krishnan, Sanjay: Combining aggregation and sampling (nearly) optimally for approximate query processing. In SIGMOD, pages 1129\u20131141, (2021)","DOI":"10.1145\/3448016.3457277"},{"key":"914_CR44","unstructured":"Ma, Qingzhi, Shanghooshabad, Ali Mohammadi, Almasi, Mehrdad, Kurmanji, Meghdad, Triantafillou, Peter: Learned approximate query processing: Make it light, accurate and fast. In CIDR, (2021)"},{"key":"914_CR45","doi-asserted-by":"crossref","unstructured":"Ma, Qingzhi, Triantafillou, Peter: Dbest: Revisiting approximate query processing engines with machine learning models. In SIGMOD, pages 1553\u20131570, (2019)","DOI":"10.1145\/3299869.3324958"},{"key":"914_CR46","doi-asserted-by":"crossref","unstructured":"Park, Yongjoo, Mozafari, Barzan, Sorenson, Joseph, Wang, Junhao: Verdictdb: Universalizing approximate query processing. In SIGMOD, pages 1461\u20131476, (2018)","DOI":"10.1145\/3183713.3196905"},{"key":"914_CR47","doi-asserted-by":"crossref","unstructured":"Park, Yongjoo, Tajik, Ahmad\u00a0Shahab, Cafarella, Michael\u00a0J., Mozafari, Barzan: Database learning: Toward a database that becomes smarter every time. In SIGMOD, pages 587\u2013602, (2017)","DOI":"10.1145\/3035918.3064013"},{"key":"914_CR48","doi-asserted-by":"crossref","unstructured":"Peng, Jinglin, Zhang, Dongxiang, Wang, Jiannan, Pei, Jian: AQP++: connecting approximate query processing with aggregate precomputation for interactive analytics. In SIGMOD, pages 1477\u20131492, (2018)","DOI":"10.1145\/3183713.3183747"},{"key":"914_CR49","doi-asserted-by":"crossref","unstructured":"Poon, Hoifung, Domingos, Pedro\u00a0M.: Sum-product networks: A new deep architecture. In UAI, pages 337\u2013346, (2011)","DOI":"10.1109\/ICCVW.2011.6130310"},{"issue":"9","key":"914_CR50","doi-asserted-by":"publisher","first-page":"898","DOI":"10.14778\/2777598.2777599","volume":"8","author":"Navneet Potti","year":"2015","unstructured":"Potti, Navneet, Patel, Jignesh M.: DAQ: A new paradigm for approximate query processing. Proc. VLDB Endow. 8(9), 898\u2013909 (2015)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR51","doi-asserted-by":"crossref","unstructured":"Press, Ofir, Wolf, Lior: Using the output embedding to improve language models. In EACL, pages 157\u2013163, (2017)","DOI":"10.18653\/v1\/E17-2025"},{"key":"914_CR52","doi-asserted-by":"crossref","unstructured":"Sanca, Viktor, Ailamaki, Anastasia: Sampling-based AQP in modern analytical engines. In DaMoN, pages 4:1\u20134:8, (2022)","DOI":"10.1145\/3533737.3535095"},{"issue":"1","key":"914_CR53","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"Franco Scarselli","year":"2009","unstructured":"Scarselli, Franco, Marco Gori, Ah., Tsoi, Chung, Hagenbuchner, Markus, Monfardini, Gabriele: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Networks"},{"key":"914_CR54","unstructured":"Sukhbaatar, Sainbayar, Szlam, Arthur, Weston, Jason, Fergus, Rob: End-to-end memory networks. In NeurIPS, pages 2440\u20132448, (2015)"},{"key":"914_CR55","doi-asserted-by":"crossref","unstructured":"Thirumuruganathan, Saravanan, Hasan, Shohedul, Koudas, Nick, Das, Gautam: Approximate query processing for data exploration using deep generative models. In ICDE, pages 1309\u20131320, (2020)","DOI":"10.1109\/ICDE48307.2020.00117"},{"key":"914_CR56","unstructured":"Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan\u00a0N., Kaiser, Lukasz, Polosukhin, Illia: Attention is all you need. In NeurIPS, pages 5998\u20136008, (2017)"},{"key":"914_CR57","unstructured":"Velickovic, Petar, Cucurull, Guillem: Arantxa Casanova. Pietro Li\u00f2, and Yoshua Bengio. Graph attention networks. In ICLR, Adriana Romero (2018)"},{"key":"914_CR58","doi-asserted-by":"crossref","unstructured":"Wu, Sai, Ooi, Beng\u00a0Chin, Tan, Kian-Lee: Continuous sampling for online aggregation over multiple queries. In Ahmed\u00a0K. Elmagarmid and Divyakant Agrawal, editors, Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6-10, 2010, pages 651\u2013662. ACM, (2010)","DOI":"10.1145\/1807167.1807238"},{"issue":"13","key":"914_CR59","doi-asserted-by":"publisher","first-page":"4709","DOI":"10.14778\/3704965.3704977","volume":"17","author":"Bobbi W Yogatama","year":"2024","unstructured":"Yogatama, Bobbi W., Gong, Weiwei, Xiangyao, Yu.: Scaling your hybrid CPU-GPU DBMS to multiple gpus. Proc. VLDB Endow. 17(13), 4709\u20134722 (2024)","journal-title":"Proc. VLDB Endow."},{"key":"914_CR60","doi-asserted-by":"crossref","unstructured":"Zeng, Tianjing, Lan, Junwei, Ma, Jiahong, Wei, Wenqing, Zhu, Rong, Li, Pengfei, Ding, Bolin, Lian, Defu, Wei, Zhewei: and Jingren Zhou. A pretrained model for cross-database cardinality estimation. Proc. VLDB Endow, PRICE (2025)","DOI":"10.14778\/3712221.3712231"},{"key":"914_CR61","doi-asserted-by":"crossref","unstructured":"Zhao, Kangfei, Yu, Jeffrey\u00a0Xu, He, Zongyan, Li, Rui, Zhang, Hao: Lightweight and accurate cardinality estimation by neural network gaussian process. In SIGMOD, pages 973\u2013987, (2022)","DOI":"10.1145\/3514221.3526156"},{"key":"914_CR62","doi-asserted-by":"crossref","unstructured":"Zhao, Zhuoyue, Christensen, Robert, Li, Feifei, Hu, Xiao, Yi, Ke: Random sampling over joins revisited. In SIGMOD, pages 1525\u20131539, (2018)","DOI":"10.1145\/3183713.3183739"},{"issue":"9","key":"914_CR63","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.14778\/3461535.3461539","volume":"14","author":"Rong Zhu","year":"2021","unstructured":"Zhu, Rong, Ziniu, Wu., Han, Yuxing, Zeng, Kai, Pfadler, Andreas, Qian, Zhengping, Zhou, Jingren, Cui, Bin: FLAT: fast, lightweight and accurate method for cardinality estimation. Proc. VLDB Endow. 14(9), 1489\u20131502 (2021)","journal-title":"Proc. VLDB Endow."}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-025-00914-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-025-00914-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-025-00914-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T11:29:12Z","timestamp":1747308552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-025-00914-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,24]]},"references-count":63,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["914"],"URL":"https:\/\/doi.org\/10.1007\/s00778-025-00914-y","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"type":"print","value":"1066-8888"},{"type":"electronic","value":"0949-877X"}],"subject":[],"published":{"date-parts":[[2025,4,24]]},"assertion":[{"value":"2 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"35"}}