|Title:||Query Engine System for Probabilistic Preferences
|Currently accessibly only within the Technion network|
|Abstract:||Models of uncertain preferences, such as Mallows, have been extensively studied due to their plethora of application domains. In a recent work, a conceptual and theoretical framework has been proposed for supporting uncertain preferences as rst-class citizens in a relational database. The resulting database is probabilistic, and, consequently, query evaluation entails inference of marginal probabilities of query answers. In this thesis, we embark on the challenge of a practical realization of this framework. We rst describe an implementation of a query engine that supports querying prob- abilistic preferences alongside relational data. Our system accommodates preference distributions in the general form of the Repeated Insertion Model (RIM), which gen- eralizes Mallows and other models. We then devise a novel inference algorithm for conjunctive queries over RIM, and show that it signicantly outperforms the state of the art in terms of both asymptotic and empirical execution cost. We also develop performance optimizations that are based on sharing computations among dierent in- ference tasks in the workload. Finally, we conduct an extensive experimental evaluation and demonstrate that clear performance benets can be realized by a query engine with built-in probabilistic inference, as compared to a stand-alone implementation with a black-box inference solver.|
|Copyright||The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information|
Remark: Any link to this technical report should be to this page (http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-info.cgi/2018/MSC/MSC-2018-17), rather than to the URL of the PDF files directly. The latter URLs may change without notice.
To the list of the MSC technical reports of 2018
To the main CS technical reports page
Computer science department, Technion