{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T08:58:38Z","timestamp":1768121918024,"version":"3.49.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            A core requirement of database engine testing is the ability to create synthetic versions of the customer's data warehouse at the vendor site. Prior work on synthetic data regeneration suffers from critical limitations with regard to (a) scaling to large data volumes, (b) handling complex query workloads, and (c) producing data on demand. In this demo, we present\n            <jats:bold>HYDRA<\/jats:bold>\n            , a workload-dependent dynamic data regenerator, that materially addresses these limitations. It introduces the concept of dynamic regeneration by constructing a minuscule memory-resident database summary that can on-the-fly regenerate databases of arbitrary size during query execution. Further, since the data is generated in memory, the velocity of generation can be closely regulated. Finally, to complement dynamic regeneration, Hydra also ensures that the process of summary construction is data-scale-free.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236238","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1974-1977","source":"Crossref","is-referenced-by-count":6,"title":["HYDRA"],"prefix":"10.14778","volume":"11","author":[{"given":"Anupam","family":"Sanghi","sequence":"first","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghav","family":"Sood","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dharmendra","family":"Singh","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayant R.","family":"Haritsa","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srikanta","family":"Tirthapura","sequence":"additional","affiliation":[{"name":"Iowa State Univerity"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Big Data. en.wikipedia.org\/wiki\/Big_data  Big Data. en.wikipedia.org\/wiki\/Big_data"},{"key":"e_1_2_1_2_1","unstructured":"Hydra Database Regenerator. dsl.cds.iisc.ac.in\/projects\/HYDRA  Hydra Database Regenerator. dsl.cds.iisc.ac.in\/projects\/HYDRA"},{"key":"e_1_2_1_3_1","unstructured":"PostgreSQL. postgresql.org\/docs\/9.3  PostgreSQL. postgresql.org\/docs\/9.3"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989395"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824123"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1247480.1247520"},{"key":"e_1_2_1_7_1","volume-title":"Proc. of 31st VLDB Conf.","author":"Bruno N.","year":"2005","unstructured":"N. Bruno and S. Chaudhuri . Flexible Database Generators . In Proc. of 31st VLDB Conf. , 2005 , pgs. 1097--1107. N. Bruno and S. Chaudhuri. Flexible Database Generators. In Proc. of 31st VLDB Conf., 2005, pgs. 1097--1107."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/191839.191886"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-014-0354-1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/1792734.1792766"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2735378"},{"key":"e_1_2_1_12_1","volume-title":"Proc. of 21st EDBT Conf.","author":"Sanghi A.","year":"2018","unstructured":"A. Sanghi , R. Sood , J. R. Haritsa , and S. Tirthapura . Scalable and Dynamic Regeneration of Big Data Volumes . In Proc. of 21st EDBT Conf. , 2018 , pgs. 301--312. A. Sanghi, R. Sood, J. R. Haritsa, and S. Tirthapura. Scalable and Dynamic Regeneration of Big Data Volumes. In Proc. of 21st EDBT Conf., 2018, pgs. 301--312."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007328.3007333"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236238","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:09:13Z","timestamp":1672222153000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236238"}},"subtitle":["a dynamic big data regenerator"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236238"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236238","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}