As a consequence of the popularity of big data, many users with a variety of backgrounds seek to extract high level information from datasets collected from various sources and combined using data integration techniques. A major challenge for research in data management is to develop tools to assist users in explaining observed query outputs. In this paper we introduce a principled approach to provide explanations for answers to SQL queries based on intervention: removal of tuples from the database that significantly affect the query answers. We provide a formal definition of intervention in the presence of multiple relations which can interact with each other through foreign keys. First we give a set of recursive rules to compute the intervention for any given explanation in polynomial time (data complexity). Then we give simple and efficient algorithms based on SQL queries that can compute the top-K explanations by using standard database management systems under certain conditions. We evaluate the quality and performance of our approach by experiments on real datasets.
Group-by and top-k are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data -such as unlabeled photos clustered by the same person ordered by age -are difficult to evaluate by machines. In contrast, these tasks are easy for humans to evaluate and are therefore natural candidates for being crowd-sourced.We study the problem of evaluating top-k and group-by queries using the crowd to answer either type or value questions. Given two data elements, the answer to a type question is "yes" if the elements have the same type and therefore belong to the same group or cluster; the answer to a value question orders the two data elements. The assumption here is that there is an underlying ground truth, but that the answers returned by the crowd may sometimes be erroneous. We formalize the problems of top-k and group-by in the crowd-sourced setting, and give efficient algorithms that are guaranteed to achieve good results with high probability. We analyze the crowd-sourced cost of these algorithms in terms of the total number of type and value questions, and show that they are essentially the best possible. We also show that fewer questions are needed when values and types are correlated, or when the error model is one in which the error decreases as the distance between the two elements in the sorted order increases.
With the surge in the availability of information, there is a great demand for tools that assist users in understanding their data. While today's exploration tools rely mostly on data visualization, users often want to go deeper and understand the underlying causes of a particular observation. This tutorial surveys research on causality and explanation for data-oriented applications. We will review and summarize the research thus far into causality and explanation in the database and AI communities, giving researchers a snapshot of the current state of the art on this topic, and propose a unified framework as well as directions for future research. We will cover both the theory of causality/explanation and some applications; we also discuss the connections with other topics in database research like provenance, deletion propagation, why-not queries, and OLAP techniques.
We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two types of repairs: an optimal subset repair (optimal S-repair) that is obtained by a minimum number of tuple deletions, and an optimal update repair (optimal U-repair) that is obtained by a minimum number of value (cell) updates. For computing an optimal S-repair, we present a polynomial-time algorithm that succeeds on certain sets of FDs and fails on others. We prove the following about the algorithm. When it succeeds, it can also incorporate weighted tuples and duplicate tuples. When it fails, the problem is NP-hard, and in fact, APX-complete (hence, cannot be approximated better than some constant). Thus, we establish a dichotomy in the complexity of computing an optimal Srepair. We present general analysis techniques for the complexity of computing an optimal U-repair, some based on the dichotomy for S-repairs. We also draw a connection to a past dichotomy in the complexity of finding a "most probable database" that satisfies a set of FDs with a single attribute on the left hand side; the case of general FDs was left open, and we show how our dichotomy provides the missing generalization and thereby settles the open problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.