Cristina Sirangelo

Reasoning about pattern-based XML queries

We survey results about static analysis of pattern-based queries over XML documents. These queries are analogs of conjunctive queries, their unions and Boolean combinations, in which tree patterns play the role of atomic formulae. As in the relational case, they can be viewed as both queries and incomplete documents, and thus static analysis problems can also be viewed as finding certain answers of queries over such documents. We look at satisfiability of patterns under schemas, containment of queries for various features of XML used in queries, finding certain answers, and applications of pattern-based queries in reasoning about schema mappings for data exchange.

XML with incomplete information

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.

Reasoning about XML with temporal logics and automata

Abstract We show that problems arising in static analysis of XML specifications and transformations can be dealt with using techniques similar to those developed for static analysis of programs. Many properties of interest in the XML context are related to navigation, and can be formulated in temporal logics for trees. We choose a logic that admits a simple single-exponential translation into unranked tree automata, in the spirit of the classical LTL-to-Buchi automata translation. Automata arising from this translation have a number of additional properties; in particular, they are convenient for reasoning about unary node-selecting queries, which are important in the XML context. We give two applications of such reasoning: one deals with a classical XML problem of reasoning about navigation in the presence of schemas, and the other relates to verifying security properties of XML views.

Constant-memory validation of streaming XML documents against DTDs

In this paper we investigate the problem of validating, with constant memory, streaming XML documents with respect to a DTD. Such constant memory validations can only be performed for some but not all DTDs. This paper gives a non trivial interesting step towards characterizing those DTDs for which a constant-memory on-line algorithm exists.

Declarative semantics of production rules for integrity maintenance

This paper presents a declarative semantics for the maintenance of integrity constraints expressed by means of production rules. A production rule is a special form of active rule, called active integrity constraint, whose body contains an integrity constraint (conjunction of literals which must be false) and whose head contains a disjunction of update atoms, i.e. actions to be performed if the corresponding constraint is not satisfied (i.e. is true). The paper introduces i) a formal declarative semantics allowing the computation of founded repairs, that is repairs whose actions are specified and supported by active integrity constraint, ii) an equivalent semantics obtained by rewriting production rules into disjunctive logic rules, so that repairs can be derived from the answer sets of the logic program and finally iii) a characterization of production rules allowing a methodology for integrity maintenance.

Reasoning about XML with Temporal Logics and Automata

We show that problems arising in static analysis of XML specifications and transformations can be dealt with using techniques similar to those developed for static analysis of programs. Many properties of interest in the XML context are related to navigation, and can be formulated in temporal logics for trees. We choose a logic that admits a simple single-exponential translation into unranked tree automata, in the spirit of the classical LTL-to-Buchi automata translation. Automata arising from this translation have a number of additional properties; in particular, they are convenient for reasoning about unary node-selecting queries, which are important in the XML context. We give two applications of such reasoning: one deals with a classical XML problem of reasoning about navigation in the presence of schemas, and the other relates to verifying security properties of XML views.

Naïve Evaluation of Queries over Incomplete Databases

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.

XML with incomplete information: models, properties, and query answering

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.

Hierarchical binary histograms for summarizing multi-dimensional data

The need to compress data into synopses of summarized information often arises in many application scenarios, where the aim is to retrieve aggregate data efficiently, possibly trading off the computational efficiency with the accuracy of the estimation. A widely used approach for summarizing multi-dimensional data is the histogram-based representation scheme, which consists in partitioning the data domain into a number of blocks (called buckets), and then storing summary information for each block. In this paper, a new histogram-based summarization technique which is very effective for multi-dimensional data is proposed. This technique exploits a multi-resolution organization of summary data, on which an efficient physical representation model is defined. The adoption of this representation model (based on a hierarchical organization of the buckets) enables some storage space to be saved w.r.t. traditional histograms, which can be invested to obtain finer grain blocks, thus approximating data with more detail. Experimental results show that our technique yields higher accuracy in retrieving aggregate information from the histogram w.r.t. traditional approaches (classical multi-dimensional histograms as well as other types of summarization technique).

Exploiting cluster analysis for constructing multi-dimensional histograms on both static and evolving data

Density-based clusterization techniques are investigated as a basis for constructing histograms in multi-dimensional scenarios, where traditional techniques fail in providing effective data synopses. The main idea is that locating dense and sparse regions can be exploited to partition the data into homogeneous buckets, preventing dense and sparse regions from being summarized into the same aggregate data. The use of clustering techniques to support the histogram construction is investigated in the context of either static and dynamic data, where the use of incremental clustering strategies is mandatory due to the inefficiency of performing the clusterization task from scratch at each data update.