E
Ester Livshits
Researcher at Technion – Israel Institute of Technology
Publications - 26
Citations - 255
Ester Livshits is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Tuple & Shapley value. The author has an hindex of 8, co-authored 24 publications receiving 153 citations.
Papers
More filters
Proceedings ArticleDOI
Computing Optimal Repairs for Functional Dependencies
TL;DR: In this paper, the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are functional dependencies (FDs), was investigated. And the authors established a dichotomy in complexity of finding a "most probable database" that satisfies a set of FDs with a single attribute on the left hand side.
Proceedings ArticleDOI
The Shapley Value of Tuples in Query Answering.
TL;DR: This work investigates the application of the Shapley value to quantifying the contribution of a tuple to a query answer and provides algorithmic and complexity-theoretic results on the computation of Shapley-based contributions to query answers.
Proceedings ArticleDOI
Counting and Enumerating (Preferred) Database Repairs
Ester Livshits,Benny Kimelfeld +1 more
TL;DR: This paper establishes dichotomies in data complexity for the entire space of (sets of) functional dependencies and devise enumeration algorithms with efficiency guarantees on the delay between generated repairs, even for constraints represented as general conflict graphs or hypergraphs.
Posted Content
Approximate Denial Constraints
TL;DR: ADCMiner as discussed by the authors is an algorithm for mining approximate Denial Constraints (DCs that are "almost" satisfied) from data, which takes the semantics as input.
Journal ArticleDOI
Approximate denial constraints
TL;DR: This paper introduces the algorithm ADCMiner for mining approximate DCs, a general family of approximation functions that satisfies some natural axioms defined in this paper and captures commonly used definitions of approximate constraints.