{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T10:08:24Z","timestamp":1758190104379,"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>Everyone needs to make decisions under uncertainty and with limited resources, e.g., an investor who is building a stock portfolio subject to an investment budget and a bounded risk tolerance. Doing this with current technology is hard. There is a disconnect between software tools for data management, stochastic predictive modeling (e.g., simulation of future stock prices), and optimization; this leads to cumbersome analytical workflows. Moreover, current methods do not scale. To handle a broad class of uncertainty models, analysts approximate the original stochastic optimization problem by a large deterministic optimization problem that incorporates many \"scenarios\", i.e., sample realizations of the uncertain data values. For large problems, a huge number of scenarios is required, often causing the solver to fail. We demonstrate sPaQLTooLs, a system for in-database specification and scalable solution of constrained optimization problems. The key ingredients are (i) a database-oriented specification of constrained stochastic optimization problems as \"stochastic package queries\" (SPQs), (ii) use of a Monte Carlo database to incorporate stochastic predictive models, and (iii) a new SummarySearch algorithm for scalably solving SPQs with approximation guarantees. In this demonstration, the attendees will experience first-hand the difficulty of manually constructing feasible and high-quality portfolios, using real-world stock market data. We will then demonstrate how SummarySearch can easily and efficiently help them find very good portfolios, while being orders of magnitude faster than prior methods.<\/jats:p>","DOI":"10.14778\/3415478.3415499","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2881-2884","source":"Crossref","is-referenced-by-count":2,"title":["sPaQLTooLs"],"prefix":"10.14778","volume":"13","author":[{"given":"Matteo","family":"Brucato","sequence":"first","affiliation":[{"name":"University of Massachusetts"}]},{"given":"Miro","family":"Mannino","sequence":"additional","affiliation":[{"name":"NYU Abu Dhabi"}]},{"given":"Azza","family":"Abouzied","sequence":"additional","affiliation":[{"name":"NYU Abu Dhabi"}]},{"given":"Peter J.","family":"Haas","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}]},{"given":"Alexandra","family":"Meliou","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1287\/educ.1080.0048"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0483-4"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389765"},{"key":"e_1_2_1_4_1","first-page":"20","volume-title":"Proc. 2nd Intl. 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