Maximizing conjunctive views in deletion propagation

Abstract

In deletion propagation, tuples from the database are deleted in order to reflect the deletion of a tuple from the view. Such an operation may result in the (often necessary) deletion of additional tuples from the view, besides the intentionally deleted one. The complexity of deletion propagation is studied, where the view is defined by a conjunctive query (CQ), and the goal is to maximize the number of tuples that remain in the view. Buneman et al. showed that for some simple CQs, this problem can be solved by a trivial algorithm. This paper identifies additional cases of CQs where the trivial algorithm succeeds, and in contrast, it proves that for some other CQs the problem is NP-hard to approximate better than some constant ratio. In fact, this paper shows that among the CQs without self joins, the hard CQs are exactly the ones that the trivial algorithm fails on. In other words, for every CQ without self joins, deletion propagation is either APX-hard or solvable by the trivial algorithm.The paper then presents approximation algorithms for certain CQs where deletion propagation is APX-hard. Specifically, two constant-ratio (and polynomial-time) approximation algorithms are given for the class of star CQs without self joins. The first algorithm is a greedy algorithm, and the second is based on randomized rounding of a linear program. While the first algorithm is more efficient, the second one has a better approximation ratio. Furthermore, the second algorithm can be extended to a significant generalization of star CQs. Finally, the paper shows that self joins can have a major negative effect on the approximability of the problem.

Provenance-based dictionary refinement in information extraction

“…Also related are recent studies on causality [29] and deletion propagation [6,23] in general relational setting. In [29], the input consists of source tuples, an output boolean vector and a ground truth boolean vector, and the goal is to compute the responsibility of every source tuple using modifications such that the output is identical to the ground truth.…”

“…In other words, the modifications should remove all incorrect results, while keeping all correct results. On the other hand, [6,23] dictionary entries correspond to source tuples and extraction results correspond to outputs. As such, our work can be seen as an alternative objective for these related problems that tries to balance between the number of incorrect tuples retained and the number of correct tuples deleted.…”

Programmable view update strategies on relations

“…The view update problem is a classical problem that has a long history in database research [22,20,21,11,34,48,33,40,29,16,36,44,45,46,42,41]. It was realized very early that a database update that reflects a view update may not always exist, and even if it does exist, it may not be unique [20,21].…”

“…Some other works use auxiliary tables to store the updates, which cannot be applied to the underlying database [40,29]. The authors of [16,36] studied approximation algorithms to minimize the side effects for propagating deletion from the view to the source database. However, these existing approaches can only solve a very restricted class of view updates.…”

“…Because the query get is generally not injective, there may be many update translations on the source database that can be used to reflect view update [20,21]. This ambiguity makes view update an open challenging problem that has a long history in database research [22,20,21,11,34,33,40,36,45,42,41]. The existing approaches either impose too many syntactic restrictions on the view definition get that allow for limited unambiguous update propagation [21,15,11,35,43,41,44,45,46] or provide dialogue mechanisms for users to manually choose update translations with users' interaction [34,42].…”