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A probabilistic optimization framework for the empty-answer problem
| Content Provider | CiteSeerX |
|---|---|
| Author | Mottin, Davide Roy, Senjuti Basu Das, Gautam Palpanas, Themis Velegrakis, Yannis |
| Abstract | We propose a principled optimization-based interactive query re-laxation framework for queries that return no answers. Given an initial query that returns an empty answer set, our framework dy-namically computes and suggests alternative queries with less con-ditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggest-ing query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objec-tive functions. We describe optimal and approximate solutions of different optimization problems using the framework. We analyze these solutions, experimentally verify their efficiency and effective-ness, and illustrate their advantage over the existing approaches. 1. |
| File Format | |
| Journal | PVLDB |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
