Aggregated Deletion Propagation for Counting Conjunctive Query Answers
Abstract
We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. This is a variant of the well-studied {\em deletion propagation} problem, the difference being that we are interested in removing the smallest subset of input tuples to remove a given number of output tuples} while deletion propagation focuses on removing a specific output tuple. We call this the {\em Aggregated Deletion Propagation} problem. We completely characterize the poly-time solvability of this problem for arbitrary conjunctive queries without self-joins. This includes a poly-time algorithm to decide solvability, as well as an exact structural characterization of NP-hard instances. We also provide a practical algorithm for this problem (a heuristic for NP-hard instances) and evaluate its experimental performance on real and synthetic datasets.
Cite
@article{arxiv.2010.08694,
title = {Aggregated Deletion Propagation for Counting Conjunctive Query Answers},
author = {Xiao Hu and Shouzhuo Sun and Shweta Patwa and Debmalya Panigrahi and Sudeepa Roy},
journal= {arXiv preprint arXiv:2010.08694},
year = {2020}
}