A Probabilistic Optimization Framework for the Empty-Answer Problem

Davide Mottin, Alice Marascu, Senjuti Basu Roy, Gautam Das, Themis Palpanas, Yannis Velegrakis

PVLDB 2013, 6(14)

How can we guide the user towards the answer?


Interactive framework for suggesting changes on user queries, includes

  • Multiple objectives (e.g, user effort, revenue)
  • Exhaustive, fast, and approximate solutions
  • Probabilistic, interactive formulations with black-box preference and knowledge functions
  • User-study and extensive experiments on real data


We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty answer set, our framework dynamically computes and suggests alternative queries with less conditions 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 suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. We analyze these solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantage over the existing approaches.

Cite us

  title={A Probabilistic Optimization Framework for the Empty-Answer Problem},
  author={Mottin, Davide and Marascu, Alice and Basu Roy, Senjuti and Das, Gautam and Palpanas, Themis and Velegrakis, Yannis},