{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:19:03Z","timestamp":1763810343681,"version":"3.44.0"},"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":[[2020,8]]},"abstract":"<jats:p>\n            Big data applications increasingly involve diverse datasets, conforming to different data models. Such datasets are routinely hosted in heterogeneous stores, each capable of handling one or a few data models, and each efficient for some, but not all, kinds of data processing. Systems capable of exploiting disparate data in this fashion are usually termed\n            <jats:italic toggle=\"yes\">polystores.<\/jats:italic>\n            A current limitation of polystores is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of (\n            <jats:italic toggle=\"yes\">i<\/jats:italic>\n            ) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; (\n            <jats:italic toggle=\"yes\">ii<\/jats:italic>\n            ) previous query results (in the style of materialized views), which may be available in the stores.\n          <\/jats:p>\n          <jats:p>We propose to demonstrate ESTOCADA [4], a novel approach that can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. The system leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data.<\/jats:p>","DOI":"10.14778\/3415478.3415516","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2949-2952","source":"Crossref","is-referenced-by-count":11,"title":["ESTOCADA"],"prefix":"10.14778","volume":"13","author":[{"given":"R.","family":"Alotaibi","sequence":"first","affiliation":[{"name":"UC San Diego"}]},{"given":"B.","family":"Cautis","sequence":"additional","affiliation":[{"name":"Univ. Paris-Saclay"}]},{"given":"A.","family":"Deutsch","sequence":"additional","affiliation":[{"name":"UC San Diego"}]},{"given":"M.","family":"Latrache","sequence":"additional","affiliation":[{"name":"Inria &amp; Institut Polytechnique de Paris"}]},{"given":"I.","family":"Manolescu","sequence":"additional","affiliation":[{"name":"Inria &amp; Institut Polytechnique de Paris"}]},{"given":"Y.","family":"Yang","sequence":"additional","affiliation":[{"name":"UC San Diego"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"unstructured":"AsterixDB. https:\/\/asterixdb.apache.org\/.","key":"e_1_2_1_1_1"},{"unstructured":"GDELT.https:\/\/www.gdeltproject.org\/data.html.","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","volume-title":"RHEEM: enabling cross-platform data processing - may the big data be with you! PVLDB, 11(11):1414--1427","author":"Agrawal D.","year":"2018","unstructured":"D. Agrawal et al. RHEEM: enabling cross-platform data processing - may the big data be with you! PVLDB, 11(11):1414--1427, 2018."},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1145\/3299869.3319895"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.14778\/3007263.3007297"},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.1109\/ICDE.2016.7498353"},{"key":"e_1_2_1_7_1","first-page":"201","volume-title":"Proc. of VLDB","author":"Deutsch A.","year":"2003","unstructured":"A. Deutsch et al. MARS: A system for publishing XML from mixed and redundant storage. In Proc. of VLDB, pages 201--212, 2003."},{"key":"e_1_2_1_8_1","volume-title":"SIGMOD","author":"Duggan J.","year":"2015","unstructured":"J. Duggan et al. The BigDAWG polystore system. In SIGMOD, 2015."},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1007\/s007780100054"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.1145\/2588555.2593683"},{"key":"e_1_2_1_11_1","volume-title":"MIMIC-III. Available at: http:\/\/www.nature.com\/articles\/sdata201635","author":"Johnson A.","year":"2016","unstructured":"A. Johnson et al. MIMIC-III. Available at: http:\/\/www.nature.com\/articles\/sdata201635, 2016."},{"key":"e_1_2_1_12_1","first-page":"241","volume-title":"Proc. of VLDB","author":"Manolescu I.","year":"2001","unstructured":"I. Manolescu et al. Answering XML queries on heterogeneous data sources. In Proc. of VLDB, pages 241--250, 2001."},{"key":"e_1_2_1_13_1","volume-title":"SIGMOD","author":"Taft R.","year":"2014","unstructured":"R. Taft et al. Genbase: A complex analytics genomics benchmark. In SIGMOD, 2014."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3415478.3415516","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T02:36:23Z","timestamp":1758076583000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3415478.3415516"}},"subtitle":["towards scalable polystore systems"],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.14778\/3415478.3415516"],"URL":"https:\/\/doi.org\/10.14778\/3415478.3415516","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2020,8]]}}}