Improving constrained search results by data melioration

Ido Guy, Tova Milo, Slava Novgorodov, Brit Youngmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many search applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. However, in real-world databases, the data available on items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML)-based inference. However, such methods are naturally error-prone. More accurate data can be obtained by asking humans to complete missing information. But, as the number of items in the repository is vast, limiting human effort is crucial. To this end, we introduce the Probabilistic Constrained Search (PCS) problem, which identifies a bounded-size item-set whose data completion is likely to be highly beneficial, as these items are expected to belong to the result set of the constrained search queries in question. We prove PCS to be hard to approximate, and consequently propose a best-effort PTIME heuristic to solve it. We demonstrate the effectiveness and efficiency of our algorithm over real-world datasets and scenarios, showing that our algorithm significantly improves the result sets of constrained search queries, in terms of both utility and constraints satisfaction probability.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherInstitute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781728191843
StatePublished - 1 Apr 2021
Externally publishedYes
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference37th IEEE International Conference on Data Engineering, ICDE 2021
CityVirtual, Chania

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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