{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:07:36Z","timestamp":1790676456237,"version":"4.1.0"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>Modern Internet applications often produce a large volume of user activity records. Data analysts are interested in cohort analysis, or finding unusual user behavioral trends, in these large tables of activity records. In a traditional database system, cohort analysis queries are both painful to specify and expensive to evaluate. We propose to extend database systems to support cohort analysis. We do so by extending SQL with three new operators. We devise three different evaluation schemes for cohort query processing. Two of them adopt a non-intrusive approach. The third approach employs a columnar based evaluation scheme with optimizations specifically designed for cohort query processing. Our experimental results confirm the performance benefits of our proposed columnar database system, compared against the two non-intrusive approaches that implement cohort queries on top of regular relational databases.<\/jats:p>","DOI":"10.14778\/3015270.3015271","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"1-12","source":"Crossref","is-referenced-by-count":20,"title":["Cohort query processing"],"prefix":"10.14778","volume":"10","author":[{"given":"Dawei","family":"Jiang","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingchao","family":"Cai","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. V.","family":"Jagadish","sequence":"additional","affiliation":[{"name":"University of Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony K. H.","family":"Tung","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Retention. https:\/\/mixpanel.com\/retention\/.  Retention. https:\/\/mixpanel.com\/retention\/."},{"key":"e_1_2_1_2_1","unstructured":"Rjmetrics. https:\/\/rjmetrics.com\/.  Rjmetrics. https:\/\/rjmetrics.com\/."},{"key":"e_1_2_1_3_1","unstructured":"Use the cohort analysis report. https:\/\/support.google.com\/analytics\/answer\/6074676?hl=en.  Use the cohort analysis report. https:\/\/support.google.com\/analytics\/answer\/6074676?hl=en."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142548"},{"key":"e_1_2_1_5_1","first-page":"506","volume-title":"VLDB","author":"Agarwal S.","year":"1996","unstructured":"S. Agarwal , R. Agrawal , P. Deshpande , A. Gupta , J. F. Naughton , R. Ramakrishnan , and S. Sarawagi . On the computation of multidimensional aggregates . In VLDB , pages 506 -- 521 , 1996 . S. Agarwal, R. Agrawal, P. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. In VLDB, pages 506--521, 1996."},{"key":"e_1_2_1_6_1","first-page":"329","volume-title":"VLDB","author":"Amer-Yahia S.","year":"2000","unstructured":"S. Amer-Yahia and T. Johnson . Optimizing queries on compressed bitmaps . In VLDB , pages 329 -- 338 , 2000 . S. Amer-Yahia and T. Johnson. Optimizing queries on compressed bitmaps. In VLDB, pages 329--338, 2000."},{"key":"e_1_2_1_7_1","first-page":"225","volume-title":"CIDR","author":"Boncz P. A.","year":"2005","unstructured":"P. A. Boncz , M. Zukowski , and N. Nes . Monetdb\/x100: Hyper-pipelining query execution . In CIDR , pages 225 -- 237 , 2005 . P. A. Boncz, M. Zukowski, and N. Nes. Monetdb\/x100: Hyper-pipelining query execution. 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