{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:51:36Z","timestamp":1742950296639,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608393"},{"type":"electronic","value":"9789819608409"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0840-9_22","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:28:07Z","timestamp":1734024487000},"page":"311-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PiqSketch: An Efficient Sketching Algorithm for\u00a0Per-Key Tail Quantile Estimation in\u00a0Large-Scale Data Streams"],"prefix":"10.1007","author":[{"given":"Yuheng","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Guoju","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Yu-E","family":"Sun","sequence":"additional","affiliation":[]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Yihuai","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"issue":"2","key":"22_CR1","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TNSM.2019.2896710","volume":"16","author":"AS Asrese","year":"2019","unstructured":"Asrese, A.S., Eravuchira, S.J., Bajpai, V., Sarolahti, P., Ott, J.: Measuring web latency and rendering performance: Method, tools, and longitudinal dataset. IEEE Trans. Netw. Serv. Manage. 16(2), 535\u2013549 (2019)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Cappos, J., Beschastnikh, I., Krishnamurthy, A., Anderson, T.: Seattle: a platform for educational cloud computing. In: Proceedings of the 40th ACM technical symposium on Computer science education. pp. 111\u2013115 (2009)","DOI":"10.1145\/1508865.1508905"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Corbett, J.C., Dean, J., Epstein, M., Fikes, A., Frost, C., Furman, J.J., Ghemawat, S., Gubarev, et\u00a0al.: Spanner: Google\u2019s globally distributed database. ACM Transactions on Computer Systems (TOCS) 31(3), 1\u201322 (2013)","DOI":"10.1145\/2518037.2491245"},{"issue":"1","key":"22_CR4","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jalgor.2003.12.001","volume":"55","author":"G Cormode","year":"2005","unstructured":"Cormode, G., 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":"22_CR5","doi-asserted-by":"crossref","unstructured":"Ding, R., Yang, S., Chen, X., Huang, Q.: Bitsense: Universal and nearly zero-error optimization for sketch counters with compressive sensing. In: Proceedings of the ACM SIGCOMM 2023 Conference. pp. 220\u2013238 (2023)","DOI":"10.1145\/3603269.3604865"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Fan, Z., Zhang, Y., Yang, T., Yan, M., Wen, G., Wu, Y., Li, H., Cui, B.: Periodicsketch: Finding periodic items in data streams. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE). pp. 96\u2013109. IEEE (2022)","DOI":"10.1109\/ICDE53745.2022.00012"},{"issue":"1","key":"22_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4086\/toc.2017.v013a014","volume":"13","author":"D Felber","year":"2017","unstructured":"Felber, D., Ostrovsky, R.: A randomized online quantile summary in $$o((1\/{\\varepsilon }) log(1\/{\\varepsilon }))$$ words. Theory of Computing 13(1), 1\u201317 (2017)","journal-title":"Theory of Computing"},{"issue":"2","key":"22_CR8","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/376284.375670","volume":"30","author":"M Greenwald","year":"2001","unstructured":"Greenwald, M., Khanna, S.: Space-efficient online computation of quantile summaries. ACM SIGMOD Rec. 30(2), 58\u201366 (2001)","journal-title":"ACM SIGMOD Rec."},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Guo, J., Hong, Y., Wu, Y., Liu, Y., Yang, T., Cui, B.: Sketchpolymer: Estimate per-item tail quantile using one sketch. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 590\u2013601 (2023)","DOI":"10.1145\/3580305.3599505"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"He, J., Zhu, J., Huang, Q.: Histsketch: A compact data structure for accurate per-key distribution monitoring. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE). pp. 2008\u20132021. IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00156"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Karnin, Z., Lang, K., Liberty, E.: Optimal quantile approximation in streams. In: 2016 ieee 57th annual symposium on foundations of computer science (focs). pp. 71\u201378. IEEE (2016)","DOI":"10.1109\/FOCS.2016.17"},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1109\/TIT.2011.2173713","volume":"58","author":"J Liebeherr","year":"2012","unstructured":"Liebeherr, J., Burchard, A., Ciucu, F.: Delay bounds in communication networks with heavy-tailed and self-similar traffic. IEEE Trans. Inf. Theory 58(2), 1010\u20131024 (2012)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"2","key":"22_CR13","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1145\/304181.304204","volume":"28","author":"GS Manku","year":"1999","unstructured":"Manku, G.S., Rajagopalan, S., Lindsay, B.G.: Random sampling techniques for space efficient online computation of order statistics of large datasets. ACM SIGMOD Rec. 28(2), 251\u2013262 (1999)","journal-title":"ACM SIGMOD Rec."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Masson, C., Rim, J.E., Lee, H.K.: Ddsketch: A fast and fully-mergeable quantile sketch with relative-error guarantees. arXiv preprint arXiv:1908.10693 (2019)","DOI":"10.14778\/3352063.3352135"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Roy, P., Khan, A., Alonso, G.: Augmented sketch: Faster and more accurate stream processing. In: Proceedings of the 2016 International Conference on Management of Data. pp. 1449\u20131463 (2016)","DOI":"10.1145\/2882903.2882948"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Shahout, R., Friedman, R., Basat, R.B.: Squad: Combining sketching and sampling is better than either for per-item quantile estimation. arXiv preprint arXiv:2201.01958 (2022)","DOI":"10.1145\/3534056.3535009"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Tang, L., Xiao, Y., Huang, Q.: A high-performance invertible sketch for network-wide superspreader detection. IEEE\/ACM Transactions on Networking (2022)","DOI":"10.1109\/TNET.2022.3198738"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Yang, T., Jiang, J., Liu, P., Huang, Q., Gong, J., Zhou, Y., Miao, R., Li, X., Uhlig, S.: Elastic sketch: Adaptive and fast network-wide measurements. In: Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication. pp. 561\u2013575 (2018)","DOI":"10.1145\/3230543.3230544"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0840-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:09:29Z","timestamp":1734026969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0840-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608393","9789819608409"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0840-9_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}