{"id":160921,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fuzzy-prophet-parameter-exploration-in-uncertain-enterprise-scenarios\/"},"modified":"2018-10-16T20:50:08","modified_gmt":"2018-10-17T03:50:08","slug":"fuzzy-prophet-parameter-exploration-in-uncertain-enterprise-scenarios","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fuzzy-prophet-parameter-exploration-in-uncertain-enterprise-scenarios\/","title":{"rendered":"Fuzzy Prophet: Parameter exploration in uncertain enterprise scenarios"},"content":{"rendered":"
We present Fuzzy Prophet, a probabilistic database tool for constructing, simulating and analyzing business scenarios with uncertain data. Fuzzy Prophet takes externally de\ufb01ned probability distribution (so called VG-Functions) and a declarative description of a target scenario, and performs Monte Carlo simulation to compute probability distribution of the scenario\u2019s outcomes. In addition, Fuzzy Prophet supports parameter optimization,where probabilistic models are parameterized and a large parameter space must be explored to \ufb01nd parameters that optimize or achieve a desired goal. Fuzzy Prophet\u2019s key innovation is to use \ufb01ngerprints that can identify parameter values producing correlated outputs of a userprovided stochastic function and to reuse computations across such values. Fingerprints signi\ufb01cantly expedite the process of parameter exploration in of\ufb02ine optimization and interactive what-if exploration tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present Fuzzy Prophet, a probabilistic database tool for constructing, simulating and analyzing business scenarios with uncertain data. Fuzzy Prophet takes externally de\ufb01ned probability distribution (so called VG-Functions) and a declarative description of a target scenario, and performs Monte Carlo simulation to compute probability distribution of the scenario\u2019s outcomes. In addition, Fuzzy Prophet supports parameter […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160921","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"SIGMOD'11: Proceedings of the 2011 ACM SIGMOD international conference on management of 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