Synthesizing Linked Data Under Cardinality and Integrity Constraints

Gilad, Amir; Patwa, Shweta; Machanavajjhala, Ashwin

doi:10.1145/3448016.3457242

Cited by 5 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arasu et al (2011) exploited the declarative characteristic of cardinality constraints for the design of a linear programming based algorithm which instantiates tables that satisfy those constraints. More recently, Gilad et al (2021) have drawn on (Arasu et al, 2011) for the design of a generation algorithm which allows for the satisfaction of integrity constraints also, enabling the generation of foreign keys for existing database relations. HYDRA (Sanghi et al, 2018) uses a declarative approach that allows for the generation of a database summary that can be used for dynamically generating data for query execution purposes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning methods for generating high dimensional discrete datasets

Manco

Ritacco

Rullo

et al. 2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real‐life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two‐step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X′ that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint‐based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Algorithmic Development > Structure Discovery

show abstract

Section: Introductionmentioning

confidence: 99%