{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:39:55Z","timestamp":1743143995190,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031429200"},{"type":"electronic","value":"9783031429217"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42921-7_8","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T05:02:14Z","timestamp":1694754134000},"page":"115-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FPGA-Integrated Bag of\u00a0Little Bootstraps Accelerator for\u00a0Approximate Database Query Processing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0858-5384","authenticated-orcid":false,"given":"V.","family":"Burtsev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2713-2842","authenticated-orcid":false,"given":"M.","family":"Wilhelm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8322-6747","authenticated-orcid":false,"given":"A.","family":"Drewes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Gurumurthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Broneske","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Pionteck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Saake","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Acharya, S., Gibbons, P.B., Poosala, V., Ramaswamy, S., et al.: The aqua approximate query answering system. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pp 574\u2013576. New York, NY, USA (1999). https:\/\/doi.org\/10.1145\/304182.304581","DOI":"10.1145\/304182.304581"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Agarwal, S., Panda, A., Mozafari, B., Madden, S., Stoica, I.: Blinkdb: queries with bounded errors and bounded response times on very large data. EuroSys 2013 (2012). https:\/\/doi.org\/10.1145\/2465351.2465355","DOI":"10.1145\/2465351.2465355"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Alimohammad, A., Fard, S.F., Cockburn, B.F., Schlegel, C.: A compact and accurate gaussian variate generator. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 16(5), 517\u2013527 (2008). https:\/\/doi.org\/10.1109\/TVLSI.2008.917552","DOI":"10.1109\/TVLSI.2008.917552"},{"issue":"2","key":"8_CR4","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s13222-021-00384-w","volume":"21","author":"D Broneske","year":"2021","unstructured":"Broneske, D., Drewes, A., Gurumurthy, B., Hajjar, I., Pionteck, T., Saake, G.: In-depth analysis of OLAP query performance on heterogeneous hardware. Datenbank-Spektrum 21(2), 133\u2013143 (2021). https:\/\/doi.org\/10.1007\/s13222-021-00384-w","journal-title":"Datenbank-Spektrum"},{"issue":"1","key":"8_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1002\/cjs.5550340103","volume":"34","author":"AJ Canty","year":"2006","unstructured":"Canty, A.J., Davison, A.C., et al.: Bootstrap diagnostics and remedies. Can. J. Stat. 34(1), 5\u201327 (2006). https:\/\/doi.org\/10.1002\/cjs.5550340103","journal-title":"Can. J. Stat."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Cormode, G.: Sketch techniques for approximate query processing. Found. Trends Databases 4(1-3), 1-294 (2011)","DOI":"10.1561\/1900000004"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Dagum, L., Menon, R.: Openmp: an industry-standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46\u201355 (1998). https:\/\/doi.org\/10.1109\/99.660313","DOI":"10.1109\/99.660313"},{"issue":"1","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176344552","volume":"7","author":"B Efron","year":"1979","unstructured":"Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7(1), 1\u201326 (1979). https:\/\/doi.org\/10.1214\/aos\/1176344552","journal-title":"Ann. Stat."},{"issue":"1","key":"8_CR9","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s00778-019-00581-w","volume":"29","author":"J Fang","year":"2019","unstructured":"Fang, J., Mulder, Y.T.B., Hidders, J., Lee, J., Hofstee, H.P.: In-memory database acceleration on FPGAs: a survey. VLDB J. 29(1), 33\u201359 (2019). https:\/\/doi.org\/10.1007\/s00778-019-00581-w","journal-title":"VLDB J."},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Babu, G.J., Pathak, P.K., Rao, C.R.: Second-order correctness of the Poisson bootstrap. Ann. Stat. 27(5), 1666\u20131683 (1999). https:\/\/doi.org\/10.1214\/aos\/1017939146","DOI":"10.1214\/aos\/1017939146"},{"key":"8_CR11","unstructured":"Gough, B.: GNU scientific library reference manual. Network Theory Ltd. (2009)"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. SIGMOD Rec. 26(2), 171\u2013182 (1997). https:\/\/doi.org\/10.1145\/253262.253291","DOI":"10.1145\/253262.253291"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Hilprecht, B., et al.: Deepdb: learn from data, not from queries! Proc. VLDB Endow. 13(7), 992\u20131005 (2020). https:\/\/doi.org\/10.14778\/3384345.3384349","DOI":"10.14778\/3384345.3384349"},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Kleiner, A., Talwalkar, A., Agarwal, S., Stoica, I., Jordan, M.: A general bootstrap performance diagnostic, pp. 419\u2013427 (2013). https:\/\/doi.org\/10.1145\/2487575.2487650","DOI":"10.1145\/2487575.2487650"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Kleiner, Ariel, et al.: A scalable bootstrap for massive data. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 76 (2011). https:\/\/doi.org\/10.1111\/rssb.12050","DOI":"10.1111\/rssb.12050"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Li, Y., Chow, P., et al.:: Software\/hardware framework for generating parallel gaussian random numbers based on the monty python method. In: 2012 International Conference on Field-Programmable Technology, pp. 190\u2013197 (2012). https:\/\/doi.org\/10.1109\/FPT.2012.6412133","DOI":"10.1109\/FPT.2012.6412133"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Liu, Z., Zhang, A.: A survey on sampling and profiling over big data (technical report) (2020). https:\/\/doi.org\/10.48550\/ARXIV.2005.05079","DOI":"10.48550\/ARXIV.2005.05079"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Ma, Q., Triantafillou, P.: Dbest: revisiting approximate query processing engines with machine learning models, pp. 1553\u20131570 (2019). https:\/\/doi.org\/10.1145\/3299869.3324958","DOI":"10.1145\/3299869.3324958"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Mahmud, M.S., Huang, J.Z., et al.: A survey of data partitioning and sampling methods to support big data analysis. Big Data Min. Anal. 3(2), 85\u2013101 (2020). https:\/\/doi.org\/10.26599\/BDMA.2019.9020015","DOI":"10.26599\/BDMA.2019.9020015"},{"key":"8_CR20","doi-asserted-by":"publisher","unstructured":"Malik, J.S., Hemani, A.: Gaussian random number generation: a survey on hardware architectures. ACM Comput. Surv. 49(3) (2016). https:\/\/doi.org\/10.1145\/2980052","DOI":"10.1145\/2980052"},{"key":"8_CR21","doi-asserted-by":"publisher","unstructured":"Nair, L.B.G., et al.: The reprovide query-sequence optimization in a hardware-accelerated DBMs. In: Proceedings of the 16th International Workshop on Data Management on New Hardware. DaMoN 2020, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3399666.3399926","DOI":"10.1145\/3399666.3399926"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Park, Y., et al.: Verdictdb: universalizing approximate query processing, SIGMOD 2018, pp. 1461\u20131476. New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3183713.3196905","DOI":"10.1145\/3183713.3196905"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Parsons, V.L.: Stratified Sampling, pp. 1\u201311. Wiley, Hoboken (2017). https:\/\/doi.org\/10.1002\/9781118445112.stat05999.pub2","DOI":"10.1002\/9781118445112.stat05999.pub2"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Peng, J., et al.: AQP++: Connecting approximate query processing with aggregate precomputation for interactive analytics, pp. 1477\u20131492. SIGMOD 2018, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3183713.3183747","DOI":"10.1145\/3183713.3183747"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Pol, A., Jermaine, C.: Relational confidence bounds are easy with the bootstrap, pp. 587\u2013598 (2005). https:\/\/doi.org\/10.1145\/1066157.1066224","DOI":"10.1145\/1066157.1066224"},{"issue":"2","key":"8_CR26","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0378-3758(97)00041-4","volume":"64","author":"C Rao","year":"1997","unstructured":"Rao, C., Pathak, P., Koltchinskii, V.: Bootstrap by sequential resampling. J. Stat. Plan. Infer. 64(2), 257\u2013281 (1997). https:\/\/doi.org\/10.1016\/S0378-3758(97)00041-4","journal-title":"J. Stat. Plan. Infer."},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Salami, B., Gorker, et al.: Axledb: a novel programmable query processing platform on FPGA. Microprocess. Microsyst. 51, 142\u2013164 (2017). https:\/\/doi.org\/10.1016\/j.micpro.2017.04.018","DOI":"10.1016\/j.micpro.2017.04.018"},{"issue":"8\u20139","key":"8_CR28","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1081\/STA-100105691","volume":"30","author":"OJ Shoemaker","year":"2001","unstructured":"Shoemaker, O.J., Pathak, P.K.: The sequential bootstrap: a comparison with regular bootstrap. Commun. Stat. Theor. Methods 30(8\u20139), 1661\u20131674 (2001). https:\/\/doi.org\/10.1081\/STA-100105691","journal-title":"Commun. Stat. Theor. Methods"},{"key":"8_CR29","doi-asserted-by":"publisher","unstructured":"Thomas, D.B.: The table-hadamard GRNG: an area-efficient FPGA gaussian random number generator. ACM Trans. Reconfigurable Technol. Syst. 8(4) (2015). https:\/\/doi.org\/10.1145\/2629607","DOI":"10.1145\/2629607"},{"key":"8_CR30","unstructured":"TPC: Tpc-h decision support benchmark. https:\/\/www.tpc.org\/tpch. Accessed 05 Aug 2022"},{"key":"8_CR31","unstructured":"Xilinx: Logicore IP product guide. https:\/\/docs.xilinx.com\/v\/u\/en-US\/pg060-floating-point (2020)"},{"key":"8_CR32","doi-asserted-by":"publisher","unstructured":"Zhao, H., Zhang, H., Jing, Y., Zhang, K., He, Z., Wang, X.S.: Revisiting approximate query processing and bootstrap error estimation on GPU. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. LNCS, vol. 13245, pp. 72\u201387. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-00123-9_5","DOI":"10.1007\/978-3-031-00123-9_5"},{"key":"8_CR33","doi-asserted-by":"publisher","unstructured":"Ziener, D., Bauer, F., et al.: FPGA-based dynamically reconfigurable SQL query processing. ACM Trans. Reconfigurable Technol. Syst. 9(4) (2016). https:\/\/doi.org\/10.1145\/2845087","DOI":"10.1145\/2845087"}],"container-title":["Lecture Notes in Computer Science","Applied Reconfigurable Computing. Architectures, Tools, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42921-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T05:03:08Z","timestamp":1694754188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42921-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031429200","9783031429217"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42921-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Applied Reconfigurable Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cottbus","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arc2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}