Aggregated deletion propagation for counting conjunctive query answers

What is a minimal set of tuples to delete from a database in order to eliminate all query answers? This problem is called "the resilience of a query" and is one of the key algorithmic problems underlying various forms of reverse data management, such as view maintenance, deletion propagation and causal responsibility. A long-open question is determining the conjunctive queries (CQs) for which resilience can be solved in PTIME. We shed new light on this problem by proposing a unified Integer Linear Programming (ILP) formulation. It is unified in that it can solve both previously studied restrictions (e.g., self-join-free CQs under set semantics that allow a PTIME solution) and new cases (all CQs under set or bag semantics). It is also unified in that all queries and all database instances are treated with the same approach, yet the algorithm is guaranteed to terminate in PTIME for all known PTIME cases. In particular, we prove that for all known easy cases, the optimal solution to our ILP is identical to a simpler Linear Programming (LP) relaxation, which implies that standard ILP solvers return the optimal solution to the original ILP in PTIME. Our approach allows us to explore new variants and obtain new complexity results. 1) It works under bag semantics, for which we give the first dichotomy results in the problem space. 2) We extend our approach to the related problem of causal responsibility and give a more fine-grained analysis of its complexity. 3) We recover easy instances for generally hard queries, including instances with read-once provenance and instances that become easy because of Functional Dependencies in the data. 4) We solve an open conjecture about a unified hardness criterion from PODS 2020 and prove the hardness of several queries of previously unknown complexity. 5) Experiments confirm that our findings accurately predict the asymptotic running times, and that our universal ILP is at times even quicker than a previously proposed dedicated flow algorithm.

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Toward interpretable and actionable data analysis with explanations and causality

Roy

2022

Proc. VLDB Endow.

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We live in a world dominated by data, where users from different fields routinely collect, study, and make decisions supported by data. To aid these users, the current trend in data analysis is to design tools that allow large-scale analytics, sophisticated predictive models, and beautiful visualizations. At this exciting time when both data and analytics tools are widely accessible to users, treating analyses as magical black boxes can painfully mislead users and make troubleshooting frustratingly time-consuming. For instance, although the perils of interpreting correlations inferred by predictive models as causation are well-documented, making such a distinction can be tricky for many users who do not have formal training in computer science or statistics. In this paper, we give an overview of our research toward bridging this gap along two main thrusts of explanations and causality. Explanations support a primary goal of data analysis - empowering users to be able to interpret the results in data analysis and troubleshoot the process. Causality complements explanations by supporting prescriptive or actionable analytics with counterfactuals and interventions, thereby helping sound decision making. In these thrusts, we explore the symbiotic relationship between core database techniques and complementary techniques from machine learning and statistics via interdisciplinary collaborations, and employ them to applications in domains like computer science education, law, and health.

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Aggregated deletion propagation for counting conjunctive query answers

Cited by 3 publications

References 23 publications

Deletion Propagation Revisited for Multiple Key Preserving Views

Deletion Propagation Revisited for Multiple Key Preserving Views

A Unified Approach for Resilience and Causal Responsibility with Integer Linear Programming (ILP) and LP Relaxations

Toward interpretable and actionable data analysis with explanations and causality

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