Answering Queries using Views over Probabilistic XML: Complexity and Tractability

We study the complexity of query answering using views in a probabilistic XML setting, identifying large classes of XPath queries -- with child and descendant navigation and predicates -- for which there are efficient (PTime) algorithms. We consider this problem under the two possible semantics for XML query results: with persistent node identifiers and in their absence. Accordingly, we consider rewritings that can exploit a single view, by means of compensation, and rewritings that can use multiple views, by means of intersection. Since in a probabilistic setting queries return answers with probabilities, the problem of rewriting goes beyond the classic one of retrieving XML answers from views. For both semantics of XML queries, we show that, even when XML answers can be retrieved from views, their probabilities may not be computable. For rewritings that use only compensation, we describe a PTime decision procedure, based on easily verifiable criteria that distinguish between the feasible cases -- when probabilistic XML results are computable -- and the unfeasible ones. For rewritings that can use multiple views, with compensation and intersection, we identify the most permissive conditions that make probabilistic rewriting feasible, and we describe an algorithm that is sound in general, and becomes complete under fairly permissive restrictions, running in PTime modulo worst-case exponential time equivalence tests. This is the best we can hope for since intersection makes query equivalence intractable already over deterministic data. Our algorithm runs in PTime whenever deterministic rewritings can be found in PTime.Comment: VLDB201

Graph-driven federated data management (extended abstract)

Modern data analysis applications require the ability to provide on-demand integration of data sources while offering a user-friendly query interface. Traditional methods for answering queries using views, focused on a rather static setting, fail to address such requirements. To overcome these issues, we propose a full fledged, GLAV-based data integration approach based on graph-based constructs. The extensibility of graphs allows us to extend the traditional framework for data integration with view definitions. Furthermore, we also propose a query language based on subgraphs. We tackle query answering via a query rewriting algorithm based on well-known algorithms for answering queries using views. We experimentally show that our method yields good performance with no significant overhead.Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovacion, as well as the European Union - NextGenerationEU, under project FJC2020-045809-I / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

A top-down approach to answering queries using views

The problem of answering queries using views is concerned with finding answers to a query using only answers to a set of views. In the context of data integration with LAV approach, this problem translates to finding maximally contained rewriting for a query using a set of views. When both query and views are in conjunctive form, rewritings generated by existing bottom-up algorithms in this context are generally expensive to evaluate. As a result, they often require costly post-processing to improve efficiency of computing the answer tuples. In this dissertation, we propose a top-down approach to the rewriting problem of conjunctive queries. We first present a graph-based analysis of the problem and identify conditions that must be satisfied to ensure maximal containment of rewriting. We then present TreeWise, a novel algorithm that uses our top-down approach to efficiently generate maximally contained rewritings that are generally less expensive to evaluate. Our experiments confirm that TreeWise generally produces better quality rewritings, with a performance comparable to the most efficient of previously proposed algorithm

Nutzung von ChaTEAU für das Answering-Queries-using-Views-Problem (AQuV)

Die vorliegende Arbeit beschäftigt sich mit dem Ziel, Anfragen auf Datenbanken nur über Sichten zu beantworten. Das Finden von äquivalenten Anfragen auf Sichten ist nicht von einfacher Natur. Es gibt eine handvoll Algorithmen, mit denen diese Anfragen umgeschrieben werden können. Ein Verfahren ist der CHASE&BACKCHASE. Diese Arbeit beschäftigt sich mit diesem Verfahren und seinen Optimierungen. Das Ziel dieser Arbeit ist es, den CHASE&BACKCHASE und den Provenance-Directed CHASE&BACKCHASE in ChaTEAU zu implementieren. Das Hauptaugenmerk liegt hierbei auf der provenance-basierten Variante

A Logic-Based Approach to Data Integration

An important aspect of data integration involves answering queries using various resources rather than by accessing database relations. The process of transforming a query from the database relations to the resources is often referred to as query folding or answering queries using views, where the views are the resources. We present a uniform approach that includes as special cases much of the previous work on this subject. Our approach is logic-based using resolution. We deal with integrity constraints, negation, and recursion also within this framework. (Also cross-listed as UMOIACS-TR-2000-61

Composition and Inversion of Schema Mappings

In the recent years, a lot of attention has been paid to the development of solid foundations for the composition and inversion of schema mappings. In this paper, we review the proposals for the semantics of these crucial operators. For each of these proposals, we concentrate on the three following problems: the definition of the semantics of the operator, the language needed to express the operator, and the algorithmic issues associated to the problem of computing the operator. It should be pointed out that we primarily consider the formalization of schema mappings introduced in the work on data exchange. In particular, when studying the problem of computing the composition and inverse of a schema mapping, we will be mostly interested in computing these operators for mappings specified by source-to-target tuple-generating dependencies