{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:01:37Z","timestamp":1774540897613,"version":"3.50.1"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p>\n            Frequent item mining, which deals with finding items that occur frequently in a given data stream over a period of time, is one of the heavily studied problems in data stream mining. A generalized version of frequent item mining is the persistent item mining, where a persistent item, unlike a frequent item, does not necessarily occur more frequently compared to other items over a short period of time, rather\n            <jats:italic>persists<\/jats:italic>\n            and occurs more frequently over a long period of time. To the best of our knowledge, there is no prior work on mining persistent items in a data stream. In this paper, we address the fundamental problem of finding persistent items in a given data stream during a given period of time at any given observation point. We propose a novel scheme, PIE, that can accurately identify each persistent item with a probability greater than any desired false negative rate (FNR) while using a very small amount of memory. The key idea of PIE is that it uses Raptor codes to encode the ID of each item that appears at the observation point during a measurement period and stores only a few bits of the encoded ID in the memory of that observation point during that measurement period. The item that is persistent occurs in enough measurement periods that enough encoded bits for the ID can be retrieved from the observation point to decode them correctly and get the ID of the persistent item. We implemented and extensively evaluated PIE using three real network traffic traces and compared its performance with two prior adapted schemes. Our results show that not only PIE achieves the desired FNR in every scenario, its FNR, on average, is 19.5 times smaller than the FNR of the best adapted prior art.\n          <\/jats:p>","DOI":"10.14778\/3025111.3025112","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"289-300","source":"Crossref","is-referenced-by-count":86,"title":["Finding persistent items in data streams"],"prefix":"10.14778","volume":"10","author":[{"given":"Haipeng","family":"Dai","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, Jiangsu, CHINA"}]},{"given":"Muhammad","family":"Shahzad","sequence":"additional","affiliation":[{"name":"North Carolina State University"}]},{"given":"Alex X.","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, Jiangsu, CHINA"}]},{"given":"Yuankun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, Jiangsu, CHINA"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"The caida ucsd anonymized 2011 internet traces. http:\/\/www.caida.org\/data\/overview.  The caida ucsd anonymized 2011 internet traces. http:\/\/www.caida.org\/data\/overview."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6309-2_1"},{"issue":"10","key":"e_1_2_1_3_1","first-page":"87","article-title":"Resource adaptive technique for frequent itemset mining in transactional data streams","volume":"12","author":"Bangalore M. H. S.","year":"2012","journal-title":"IJCSNS"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879175"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/15427951.2004.10129096"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/3-540-45465-9_59","volume-title":"Automata, Languages and Programming","author":"Charikar M.","year":"2002"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1562764.1562789"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalgor.2003.12.001"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2018436.2018462"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2674005.2674994"},{"key":"e_1_2_1_11_1","first-page":"759","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"Gaber M. M.","year":"2010"},{"key":"e_1_2_1_12_1","unstructured":"A. C. Gilbert Y. Kotidis S. Muthukrishnan and M. Strauss. Quicksand: Quick summary and analysis of network data. Technical report.  A. C. Gilbert Y. Kotidis S. Muthukrishnan and M. Strauss. Quicksand: Quick summary and analysis of network data. Technical report."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04342-0_17"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2011.6120248"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/11600930_5"},{"key":"e_1_2_1_16_1","volume-title":"Proc. INFOCOM.","author":"Li Y."},{"key":"e_1_2_1_17_1","volume-title":"Methods for mining frequent items in data streams: an overview. Knowledge and information systems, 26(1):1--30","author":"Liu H.","year":"2011"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/1287369.1287400"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30570-5_27"},{"key":"e_1_2_1_20_1","first-page":"117","volume-title":"Proc. ESM","author":"Moro S.","year":"2011"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/1251086.1251088"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1155\/2005\/962135"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2004.826277"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.874390"},{"key":"e_1_2_1_25_1","volume-title":"IETF Internet Draft","author":"Transport R. M.","year":"2011"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2014.33"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3025111.3025112","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:18:34Z","timestamp":1672222714000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3025111.3025112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["10.14778\/3025111.3025112"],"URL":"https:\/\/doi.org\/10.14778\/3025111.3025112","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2016,11]]}}}