{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:22:00Z","timestamp":1672291320602},"reference-count":9,"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":"\n Researchers and developers use benchmarks to compare their algorithms and products. For database systems, a benchmark must have a dataset\n D.<\/jats:italic>\n To be application-specific, this dataset\n D<\/jats:italic>\n should be empirical. However, a real\n D<\/jats:italic>\n may be too small, or too large, for the benchmarking experiments. Therefore,\n D<\/jats:italic>\n must first be scaled to the desired size.\n <\/jats:p>\n \n Previous related work typically extracts a set of properties \u03a0 = {\n \u03c0<\/jats:italic>\n 1<\/jats:sub>\n , . . . , \u03c0\n \n n<\/jats:italic>\n <\/jats:sub>\n } from\n D,<\/jats:italic>\n then use \u03a0 to generate the synthetic D~. \u03a0 may thus ensure D~ is similar to\n D.<\/jats:italic>\n This approach of having some monolithic software enforce properties \u03c0\n 1<\/jats:sub>\n , . . . , \u03c0\n \n n<\/jats:italic>\n <\/jats:sub>\n becomes increasingly intractable as\n n<\/jats:italic>\n increases. Our demonstration will present ASPECT, a framework that takes a different approach.\n <\/jats:p>\n \n With ASPECT, there is a tool\n So<\/jats:italic>\n to first scale the dataset size. The resulting D~ can then be tweaked by tools\n T<\/jats:italic>\n 1<\/jats:sub>\n , . . . ,\n \n T\n n<\/jats:sub>\n <\/jats:italic>\n , where\n \n T\n k<\/jats:sub>\n <\/jats:italic>\n enforces\n \n \u03c0\n k<\/jats:sub>\n <\/jats:italic>\n in D~.\n <\/jats:p>\n \n At the demonstration, a visitor has a choice of (i)\n D<\/jats:italic>\n , (ii) size scaler\n S<\/jats:italic>\n 0<\/jats:sub>\n ,<\/jats:italic>\n (iii) the subset of properties to enforce, and (iv) the order of applying the tools for the chosen properties. The visitor can then see the enforcement error for each\n \n \u03c0\n k<\/jats:sub>\n <\/jats:italic>\n and the running time for each\n \n T\n k<\/jats:sub>\n .\n <\/jats:italic>\n <\/jats:p>\n A video of the demonstration is presented here: http:\/\/scaler.d2.comp.nus.edu.sg\/<\/jats:p>","DOI":"10.14778\/3229863.3236247","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2010-2013","source":"Crossref","is-referenced-by-count":0,"title":["A collaborative framework for tweaking properties in a synthetic dataset"],"prefix":"10.14778","volume":"11","author":[{"given":"J. W.","family":"Zhang","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y. C.","family":"Tay","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"95","volume-title":"Data Science, LNCS 9147","author":"Buda T.","year":"2015","unstructured":"T. Buda , T. Cerqueus , ReX: Extrapolating relational data in a representative way . In Data Science, LNCS 9147 , pages 95 -- 107 . Springer , 2015 . T. Buda, T. Cerqueus, et al. ReX: Extrapolating relational data in a representative way. In Data Science, LNCS 9147, pages 95--107. Springer, 2015."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661845"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2015.7113276"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.49"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10424-4_2"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402755.3402798"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2013.07.004"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007328.3007333"},{"key":"e_1_2_1_9_1","volume-title":"A tool framework for tweaking features in synthetic datasets. https:\/\/arxiv.org\/abs\/1801.03645","author":"Zhang J. W.","year":"2018","unstructured":"J. W. Zhang and Y. C. Tay . A tool framework for tweaking features in synthetic datasets. https:\/\/arxiv.org\/abs\/1801.03645 , 2018 . J. W. Zhang and Y. C. Tay. A tool framework for tweaking features in synthetic datasets. https:\/\/arxiv.org\/abs\/1801.03645, 2018."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236247","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:06:21Z","timestamp":1672221981000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":9,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236247"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236247","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}