Synthesizing Linked Data under Cardinality and Integrity Constraints

Amir Gilad, Shweta Patwa, Ashwin MacHanavajjhala

Research output: Contribution to journalConference articlepeer-review

Abstract

The generation of synthetic data is useful in multiple aspects, from testing applications to benchmarking to privacy preservation. Generating thelinks between relations, subject tocardinality constraints (CCs) andintegrity constraints (ICs) is an important aspect of this problem. Given instances of two relations, where one has a foreign key dependence on the other and is missing its foreign key ($FK$) values, and two types of constraints: (1) CCs that apply to the join view and (2) ICs that apply to the table with missing $FK$ values, our goal is to impute the missing $FK$ values such that the constraints are satisfied. We provide a novel framework for the problem based on declarative CCs and ICs. We further show that the problem is NP-hard and propose a novel two-phase solution that guarantees the satisfaction of the ICs. Phase I yields an intermediate solution accounting for the CCs alone, and relies on a hybrid approach based on CC types. For one type, the problem is modeled as an Integer Linear Program. For the others, we describe an efficient and accurate solution. We then combine the two solutions. Phase II augments this solution by incorporating the ICs and uses a coloring of the conflict hypergraph to infer the values of the $FK$ column. Our extensive experimental study shows that our solution scales well when the data and number of constraints increases. We further show that our solution maintains low error rates for the CCs.

Original languageEnglish
Pages (from-to)619-631
Number of pages13
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China
Duration: 20 Jun 202125 Jun 2021

Keywords

  • data generation
  • database constraints
  • linked data

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