Real-life query languages feature external predicates such as user-defined functions or built-in arithmetic and string operations. These predicates are often infinite, potentially leading to unsafe or non-computable queries. To overcome this, traditional languages such as SQL, put significant syntactic restrictions on the use of external predicates. These restrictions have been relaxed in a number of modern query languages, each doing it in their own way. Our goal therefore is to provide a theoretical basis for querying with external predicates. To this end, we formalize queries with external predicates based on the notion of access patterns. We develop a suitable evaluation model, based on Turing machines with oracles, and tailor the classical notion of query safety to it. Since query safety is undecidable in general, we can only produce sufficient conditions for guaranteeing safety. We do so by developing an inference system to derive safety and computability for relational algebra, first-order logic, as well as for a language that combines them both.

We consider the dichotomy conjecture for consistent query answering under primary key constraints. It states that, for every fixed Boolean conjunctive query q, testing whether q is certain (i.e. whether it evaluates to true over all repairs of a given inconsistent database) is either polynomial time or coNP-complete. This conjecture has been verified for self-join-free and path queries. We propose a simple inflationary fixpoint algorithm for consistent query answering which, for a given database, naively computes a set $\Delta$ of subsets of facts of the database of size at most k, where k is the size of the query q. The algorithm runs in polynomial time and can be formally defined as: (1) Initialize $\Delta$ with all sets $S$ of at most $k$ facts such that $S\models q$. (2) Add any set $S$ of at most k facts to $\Delta$ if there exists a block $B$ (i.e., a maximal set of facts sharing the same key) such that for every fact $a \in B$ there is a set $S' \subseteq S \cup \{a\}$ such that $S'\in \Delta$. For an input database $D$, the algorithm answers "q is certain" iff $\Delta$ eventually contains the empty set. The algorithm correctly computes certainty when the query q falls in the polynomial time cases of the known dichotomies for self-join-free queries and path queries. For arbitrary Boolean conjunctive queries, the algorithm is an under-approximation: the query is guaranteed to be certain if the algorithm claims so. However, there are polynomial time certain queries (with self-joins) which are not identified as such by the algorithm.

We consider the dichotomy conjecture for consistent query answering under primary key constraints. It states that, for every fixed Boolean conjunctive query q, testing whether q is certain (i.e. whether it evaluates to true over all repairs of a given inconsistent database) is either PTime or CoNP-complete. This conjecture has been verified for self-join-free and path queries. We show that it also holds for queries with two atoms.

Abstract To answer database queries over incomplete data, the gold standard is finding certain answers: those that are true regardless of how incomplete data is interpreted. Such answers can be found efficiently for conjunctive queries and their unions, even in the presence of constraints. With negation added, the problem becomes intractable however. We concentrate on the complexity of certain answers under constraints and on effficiently answering queries outside the usual classes of (unions) of conjunctive queries by means of rewriting as Datalog and first-order queries. We first notice that there are three different ways in which query answering can be cast as a decision problem. We complete the existing picture and provide precise complexity bounds on all versions of the decision problem, for certain and best answers. We then study a well-behaved class of queries that extends unions of conjunctive queries with a mild form of negation. We show that for them, certain answers can be expressed in Datalog with negation, even in the presence of functional dependencies, thus making them tractable in data complexity. We show that in general, Datalog cannot be replaced by first-order logic, but without constraints such a rewriting can be done in first order.

We consider the dichotomy conjecture for consistent query answering under primary key constraints. It states that, for every fixed Boolean conjunctive query q, testing whether q is certain (i.e. whether it evaluates to true over all repairs of a given inconsistent database) is either polynomial time or coNP-complete. This conjecture has been verified for self-join-free and path queries. We propose a simple inflationary fixpoint algorithm for consistent query answering which, for a given database, naively computes a set $Δ$ of subsets of facts of the database of size at most k, where k is the size of the query q. The algorithm runs in polynomial time and can be formally defined as: (1) Initialize $Δ$ with all sets $S$ of at most $k$ facts such that $S\models q$. (2) Add any set $S$ of at most k facts to $Δ$ if there exists a block $B$ (i.e., a maximal set of facts sharing the same key) such that for every fact $a \in B$ there is a set $S' \subseteq S \cup \{a\}$ such that $S'\in Δ$. For an input database $D$, the algorithm answers "q is certain" iff $Δ$ eventually contains the empty set. The algorithm correctly computes certainty when the query q falls in the polynomial time cases of the known dichotomies for self-join-free queries and path queries. For arbitrary Boolean conjunctive queries, the algorithm is an under-approximation: the query is guaranteed to be certain if the algorithm claims so. However, there are polynomial time certain queries (with self-joins) which are not identified as such by the algorithm.

Answering queries over incomplete data is ubiquitous in data management and in many AI applications that use query rewriting to take advantage of relational database technology. In these scenarios one lacks full information on the data but queries still need to be answered with certainty. The certainty aspect often makes query answering unfeasible except for restricted classes, such as unions of conjunctive queries. In addition often there are no, or very few certain answers, thus expensive computation is in vain. Therefore we study a relaxation of certain answers called best answers. They are defined as those answers for which there is no better one (that is, no answer true in more possible worlds). When certain answers exist the two notions coincide. We compare different ways of casting query answering as a decision problem and characterise its complexity for first-order queries, showing significant differences in the behavior of best and certain answers.We then restrict attention to best answers for unions of conjunctive queries and produce a practical algorithm for finding them based on query rewriting techniques.

We consider query answering using views on graph databases, i.e. databases structured as edge-labeled graphs. We mainly consider views and queries specified by Regular Path Queries (RPQ). These are queries selecting pairs of nodes in a graph database that are connected via a path whose sequence of edge labels belongs to some regular language. We say that a view V determines a query Q if for all graph databases D, the view image V(D) always contains enough information to answer Q on D. In other words, there is a well defined function from V(D) to Q(D). Our main result shows that when this function is monotone, there exists a rewriting of Q as a Datalog query over the view instance V(D). In particular the rewriting query can be evaluated in time polynomial in the size of V(D). Moreover this implies that it is decidable whether an RPQ query can be rewritten in Datalog using RPQ views.

The term naïve evaluation refers to evaluating queries over incomplete databases as if nulls were usual data values, that is, to using the standard database query evaluation engine. Since the semantics of query answering over incomplete databases is that of certain answers, we would like to know when naïve evaluation computes them, that is, when certain answers can be found without inventing new specialized algorithms. For relational databases it is well known that unions of conjunctive queries possess this desirable property, and results on preservation of formulae under homomorphisms tell us that, within relational calculus, this class cannot be extended under the open-world assumption. Our goal here is twofold. First, we develop a general framework that allows us to determine, for a given semantics of incompleteness, classes of queries for which naïve evaluation computes certain answers. Second, we apply this approach to a variety of semantics, showing that for many classes of queries beyond unions of conjunctive queries, naïve evaluation makes perfect sense under assumptions different from open world. Our key observations are: (1) naïve evaluation is equivalent to monotonicity of queries with respect to a semantics-induced ordering, and (2) for most reasonable semantics of incompleteness, such monotonicity is captured by preservation under various types of homomorphisms. Using these results we find classes of queries for which naïve evaluation works, for example, positive first-order formulae for the closed-world semantics. Even more, we introduce a general relation-based framework for defining semantics of incompleteness, show how it can be used to capture many known semantics and to introduce new ones, and describe classes of first-order queries for which naïve evaluation works under such semantics.

The term naive evaluation refers to evaluating queries over incomplete databases as if nulls were usual data values, i.e., to using the standard database query evaluation engine. Since the semantics of query answering over incomplete databases is that of certain answers, we would like to know when naive evaluation computes them: i.e., when certain answers can be found without inventing new specialized algorithms. For relational databases it is well known that unions of conjunctive queries possess this desirable property, and results on preservation of formulae under homomorphisms tell us that within relational calculus, this class cannot be extended under the open-world assumption.

We study models of incomplete information for XML, their computational properties, and query answering. While our approach is motivated by the study of relational incompleteness, incomplete information in XML documents may appear not only as null values but also as missing structural information. Our goal is to provide a classification of incomplete descriptions of XML documents, and separate features—or groups of features—that lead to hard computational problems from those that admit efficient algorithms. Our classification of incomplete information is based on the combination of null values with partial structural descriptions of documents. The key computational problems we consider are consistency of partial descriptions, representability of complete documents by incomplete ones, and query answering. We show how factors such as schema information, the presence of node ids, and missing structural information affect the complexity of these main computational problems, and find robust classes of incomplete XML descriptions that permit tractable query evaluation.

We study models of incomplete information for XML, their computational properties, and query answering. While our approach is motivated by the study of relational incompleteness, incomplete information in XML documents may appear not only as null values but also as missing structural information. Our goal is to provide a classification of incomplete descriptions of XML documents, and separate features - or groups of features - that lead to hard computational problems from those that admit efficient algorithms. Our classification of incomplete information is based on the combination of null values with partial structural descriptions of documents. The key computational problems we consider are consistency of partial descriptions, representability of complete documents by incomplete ones, and query answering. We show how factors such as schema information, the presence of node ids, and missing structural information affect the complexity of these main computational problems, and find robust classes of incomplete XML descriptions that permit tractable query evaluation.

In the study of data exchange one usually assumes an open-world semantics, making it possible to extend instances of target schemas. An alternative closed-world semantics only moves 'as much data as needed' from the source to the target to satisfy constraints of a schema mapping. It avoids some of the problems exhibited by the open-world semantics, but limits the expressivity of schema mappings. Here we propose a mixed approach: one can designate different attributes of target schemas as open or closed, to combine the additional expressivity of the open-world semantics with the better behavior of query answering in closed worlds.

The objective of this paper is to investigate the problems related to the extensional integration of information sources. In particular, we propose an approach for managing inconsistent databases, i.e. databases violating integrity constraints. The presence of inconsistent data can be resolved by "repairing" the database, i.e. by providing a computational mechanism that ensures obtaining consistent "scenarios" of the information or by consistently answering to queries posed on an inconsistent set of data. In this paper we consider preferences among repairs and possible answers by introducing a partial order among them on the base of some preference criteria. More specifically, preferences are expressed by considering polynomial functions applied to repairs and returning real numbers. The goodness of a repair is measured by estimating how much it violates the desiderata conditions and a repair is preferred if it minimizes the value of the polynomial function used to express the preference criteria. The main contribution of this work consists in the proposal of a logic approach for querying and repairing inconsistent databases that extends previous works by allowing to express and manage preference criteria. The approach here proposed allows to express reliability on the information sources and is also suitable for expressing decision and optimization problems. The introduction of preference criteria strongly reduces the number of feasible repairs and answers; for special classes of constraints and functions it gives a unique repair and answer.

In many application contexts, like statistical databases, scientific databases, query optimizers, OLAP, and so on, data are often summarized into synopses of aggregate values. Summarization has the great advantage of saving space, but querying aggregate data rather than the original ones introduces estimation errors which cannot be in general avoided, as summarization is a lossy compression. A central problem in designing summarization techniques is to retain a certain degree of accuracy in reconstructing query answers. In this paper we restrict our attention to two-dimensional data, which are relevant for a number of applications, and propose a hierarchical summarization technique, which is combined with the use of indices, i.e. compact structures providing an approximate description of portions of the original data. Experimental results show that the technique gives approximation errors much smaller than other "general purpose" techniques, such as wavelets and various types of multi-dimensional histogram.