Target Schema Research Articles

Validating XML document adaptations via Hedge Automata transformations

We present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of update primitives of XQuery Update. The key feature of the method is the use of an automatic inference algorithm for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as a conformance test w.r.t. the type extracted from the target schema S′.

The consistency and absolute consistency problems of XML schema mappings between restricted DTDs

Consistency of XML schema mappings, which means that some document conforming to the source schema can be mapped into a document conforming to the target schema, is an essentially necessary property. It is also important for XML schema mappings to be absolutely consistent, that is, every document conforming to the source schema can be mapped into a document conforming to the target schema. As a known result, consistency of a mapping between general DTDs is EXPTIME-complete even if the class of document patterns for defining mappings is restricted to downward axes and qualifiers. In addition, the known tractability result is only on a restricted class of document patterns under restricted DTDs called nested-relational DTDs. Moreover, there are few known results on the tractability of absolute consistency. In this paper, we discuss the consistency and absolute consistency problems under restricted DTDs called disjunction-capsuled DTDs, which were proposed by Ishihara et al, and their extended classes. We first show that for many document pattern classes, both problems are solvable in polynomial time under disjunction-capsuled DTDs. Then, we show that our results are extended to some superclasses of disjunction-capsuled DTDs, which are also proper superclasses of nested-relational DTDs.

XML Schema Mappings

Relational schema mappings have been extensively studied in connection with data integration and exchange problems, but mappings between XML schemas have not received the same amount of attention. Our goal is to develop a theory of expressive XML schema mappings. Such mappings should be able to use various forms of navigation in a document, and specify conditions on data values. We develop a language for XML schema mappings, and study both data exchange with such mappings and metadata management problems. Specifically, we concentrate on four types of problems: complexity of mappings, query answering, consistency issues, and composition. We first analyze the complexity of mappings, that is, recognizing pairs of documents such that one can be mapped into the other, and provide a classification based on sets of features used in mappings. Next, we chart the tractability frontier for the query answering problem. We show that the problem is tractable for expressive schema mappings and simple queries, but not vice versa. Then, we move to static analysis. We study the complexity of the consistency problem, that is, deciding whether it is possible to map some document of a source schema into a document of the target schema. Finally, we look at composition of XML schema mappings. We analyze its complexity and show that it is harder to achieve closure under composition for XML than for relational mappings. Nevertheless, we find a robust class of XML schema mappings that, in addition to being closed under composition, have good complexity properties with respect to the main data management tasks. Due to its good properties, we suggest this class as the class to use in applications of XML schema mappings.

Learning schema mappings

A schema mapping is a high-level specification of the relationship between a source schema and a target schema. Recently, a line of research has emerged that aims at deriving schema mappings automatically or semi-automatically with the help of data examples, that is, pairs consisting of a source instance and a target instance that depict, in some precise sense, the intended behavior of the schema mapping. Several different uses of data examples for deriving, refining, or illustrating a schema mapping have already been proposed and studied. In this article, we use the lens of computational learning theory to systematically investigate the problem of obtaining algorithmically a schema mapping from data examples. Our aim is to leverage the rich body of work on learning theory in order to develop a framework for exploring the power and the limitations of the various algorithmic methods for obtaining schema mappings from data examples. We focus on GAV schema mappings, that is, schema mappings specified by GAV (Global-As-View) constraints. GAV constraints are the most basic and the most widely supported language for specifying schema mappings. We present an efficient algorithm for learning GAV schema mappings using Angluin's model of exact learning with membership and equivalence queries. This is optimal, since we show that neither membership queries nor equivalence queries suffice, unless the source schema consists of unary relations only. We also obtain results concerning the learnability of schema mappings in the context of Valiant's well-known PAC (Probably-Approximately-Correct) learning model, and concerning the learnability of restricted classes of GAV schema mappings. Finally, as a byproduct of our work, we show that there is no efficient algorithm for approximating the shortest GAV schema mapping fitting a given set of examples, unless the source schema consists of unary relations only.

The BioCASe Monitor Service - A tool for monitoring progress and quality of data provision through distributed data networks.

The BioCASe Monitor Service (BMS) is a web-based tool for coordinators of distributed data networks that provide information to web-portals and data aggregators via the BioCASe Provider Software. Building on common standards and protocols, it has three main purposes: (1) monitoring provider’s progress in data provision, (2) facilitating checks of data mappings with a focus on the structure, plausibility and completeness, and (3) verifying compliance of provided data for transformation into other target schemas.Herein two use cases, GBIF-D and OpenUp!, are presented in which the BMS is being applied for monitoring the progress in data provision and performing quality checks on the ABCD (Access to Biological Collection Data) schema mapping.However, the BMS can potentially be used with any conceptual data schema and protocols for querying web services. Through flexible configuration options it is highly adaptable to specific requirements and needs. Thus, the BMS can be easily implemented into coordination workflows and reporting duties within other distributed data network projects.

Solutions and query rewriting in data exchange

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema. Given a source instance, there may be many solutions – target instances that satisfy the constraints of the data exchange problem. Previous work has identified two classes of desirable solutions: canonical universal solutions, and their cores. Query answering in data exchange amounts to rewriting a query over the target schema to another query that, over a materialized target instance, gives the result that is semantically consistent with the source (specifically, the “certain answers”). Basic questions are then: (1) how do these solutions compare in terms of query rewriting? and (2) how can we determine whether a query is rewritable over a particular solution?Our goal is to answer these questions. Our first main result is that, in terms of rewritability by relational algebra queries, the core is strictly less expressive than the canonical universal solution, which in turn is strictly less expressive than the source. To develop techniques for proving queries non-rewritable, we establish structural properties of solutions; in fact they are derived from the technical machinery developed in the rewritability proofs. Our second result is that both the canonical universal solution and the core preserve the local structure of the data, and that every target query rewritable over any of these solutions cannot distinguish tuples whose neighborhoods in the source are similar. This gives us a first simple tool for checking whether a query is non-rewritable over the canonical universal solution or over the core. We also show that these tools generalize to arbitrary transformations that preserve the local structure of the data, and investigate an alternative semantics of query answering in data exchange.

Solutions in XML data exchange

The task of XML data exchange is to restructure a document conforming to a source schema under a target schema according to certain mapping rules. The rules are typically expressed as source-to-target dependencies using various kinds of patterns, involving horizontal and vertical navigation, as well as data comparisons. The target schema imposes complex conditions on the structure of solutions, possibly inconsistent with the mapping rules. In consequence, for some source documents there may be no solutions. We investigate three problems: deciding if all documents of the source schema can be mapped to a document of the target schema (absolute consistency), deciding if a given document of the source schema can be mapped (solution existence), and constructing a solution for a given source document (solution building). We show that the complexity of absolute consistency is rather high in general, but within the polynomial hierarchy for bounded depth schemas. The combined complexity of solution existence and solution building behaves similarly, but the data complexity turns out to be very low. In addition to this we show that even for much more expressive mapping rules, based on MSO definable queries, absolute consistency is decidable and data complexity of solution existence is polynomial.

Constraint-aware Schema Transformation

Data schema transformations occur in the context of software evolution, refactoring, and cross-paradigm data mappings. When constraints exist on the initial schema, these need to be transformed into constraints on the target schema. Moreover, when high-level data types are refined to lower level structures, additional target schema constraints must be introduced to balance the loss of structure and preserve semantics.We introduce an algebraic approach to schema transformation that is constraint-aware in the sense that constraints are preserved from source to target schemas and that new constraints are introduced where needed. Our approach is based on refinement theory and point-free program transformation. Data refinements are modeled as rewrite rules on types that carry point-free predicates as constraints. At each rewrite step, the predicate on the reduct is computed from the predicate on the redex. An additional rewrite system on point-free functions is used to normalize the predicates that are built up along rewrite chains.We implemented our rewrite systems in a type-safe way in the functional programming language Haskell. We demonstrate their application to constraint-aware hierarchical-relational mappings.

XML data exchange with target constraints

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema, by following a mapping between the two schemas. There is a rich literature on problems related to data exchange, e.g., the design of a schema mapping language, the consistency of schema mappings, operations on mappings, and query answering over mappings. Data exchange is extensively studied on relational model, and is also recently discussed for XML data. This article investigates the construction of target instance for XML data exchange, which has received far less attention. We first present a rich language for the definition of schema mappings, which allow one to use various forms of document navigation and specify conditions on data values. Given a schema mapping, we then provide an algorithm to construct a canonical target instance. The schema mapping alone is not adequate for expressing target semantics, and hence, the canonical instance is in general not optimal. We recognize that target constraints play a crucial role in the generation of good solutions. In light of this, we employ a general XML constraint model to define target constraints. Structural constraints and keys are used to identify a certain entity, as rules for data merging. Moreover, we develop techniques to enforce non-key constraints on the canonical target instance, by providing a chase method to reason about data. Experimental results show that our algorithms scale well, and are effective in producing target instances of good quality.

Generating, Sampling and Counting Subclasses of Regular Tree Languages

To experimentally validate learning and approximation algorithms for XML Schema Definitions (XSDs), we need algorithms to generate uniformly at random a corpus of XSDs as well as a similarity measure to compare how close the generated XSD resembles the target schema. In this paper, we provide the formal foundation for such a testbed. We adopt similarity measures based on counting the number of common and different trees in the two languages, and we develop the necessary machinery for computing them. We use the formalism of extended DTDs (EDTDs) to represent the unranked regular tree languages. In particular, we obtain an efficient algorithm to count the number of trees up to a certain size in an unambiguous EDTD. The latter class of unambiguous EDTDs encompasses the more familiar classes of single-type, restrained competition and bottom-up deterministic EDTDs. The single-type EDTDs correspond precisely to the core of XML Schema, while the others are strictly more expressive. We also show how constraints on the shape of allowed trees can be incorporated. As we make use of a translation into a well-known formalism for combinatorial specifications, we get for free a sampling procedure to draw members of any unambiguous EDTD. When dropping the restriction to unambiguous EDTDs, i.e. taking the full class of EDTDs into account, we show that the counting problem becomes #P-complete and provide an approximation algorithm. Finally, we discuss uniform generation of single-type EDTDs, i.e., the formal abstraction of XSDs. To this end, we provide an algorithm to generate k-occurrence automata (k-OAs) uniformly at random and show how this leads to the uniform generation of single-type EDTDs.

Validation of schema mappings with nested queries

With the emergence of the Web and the wide use of XML for representing data, the ability to map not only flat relational but also nested data has become crucial. The design of schema mappings is a semi-automatic process. A human designer is needed to guide the process, choose among mapping candidates, and successively refine the mapping. The designer needs a way to figure out whether the mapping is what was intended. Our approach to mapping validation allows the designer to check whether the mapping satisfies certain desirable properties. In this paper, we focus on the validation of mappings between nested relational schemas, in which the mapping assertions are either inclusions or equalities of nested queries. We focus on the nested relational setting since most XML?s Document Type Definitions (DTDs) can be represented in this model. We perform the validation by reasoning on the schemas and mapping definition. We take into account the integrity constraints defined on both the source and target schema. We consider constraints and mapping?s queries which may contain arithmetic comparisons and negations. This class of mapping scenarios is significantly more expressive than the ones addressed by previous work on nested relational mapping validation. We encode the given mapping scenario into a single flat database schema, so we can take advantage of our previous work on validating flat relational mappings, and reformulate each desirable property check as a query satisfiability problem.

Automata-based Static Analysis of XML Document Adaptation

The structure of an XML document can be optionally specified by means of XML Schema, thus enabling the exploitation of structural information for efficient document handling. Upon schema evolution, or when exchanging documents among different collections exploiting related but not identical schemas, the need may arise of adapting a document, known to be valid for a given schema S, to a target schema S'. The adaptation may require knowledge of the element semantics and cannot always be automatically derived. In this paper, we present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of XQuery Update update primitives. The key feature of the method is the use of an automatic inference method for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as conformance test w.r.t. the type extracted from the target schema S'.

Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources

One difficulty in integrating geospatial data sets from different sources is variation in feature classification and semantic content of the data. One step towards achieving beneficial semantic interoperability is to assess the semantic similarity among objects that are categorised within data sets. This article focuses on measuring semantic and structural similarities between categories of formal data, such as Ordnance Survey (OS) cartographic data, and volunteered geographic information (VGI), such as that sourced from OpenStreetMap (OSM), with the intention of assessing possible integration. The model involves ‘tokenisation’ to search for common roots of words, and the feature classifications have been modelled as an XML schema labelled rooted tree for hierarchical analysis. The semantic similarity was measured using the WordNet::Similarity package, while the structural similarities between sub-trees of the source and target schemas have also been considered. Along with dictionary and structural matching, the data type of the category itself is a comparison variable. The overall similarity is based on a weighted combination of these three measures. The results reveal that the use of a generic similarity matching system leads to poor agreement between the semantics of OS and OSM data sets. It is concluded that a more rigorous peer-to-peer assessment of VGI data, increasing numbers and transparency of contributors, the initiation of more programs of quality testing and the development of more directed ontologies can improve spatial data integration.

Tractable XML data exchange via relations

We consider data exchange for XML documents: given source and target schemas, a mapping between them, and a document conforming to the source schema, construct a target document and answer target queries in a way that is consistent with the source information. The problem has primarily been studied in the relational context, in which data-exchange systems have also been built. Since many XML documents are stored in relations, it is natural to consider using a relational system for XML data exchange. However, there is a complexity mismatch between query answering in relational and in XML data exchange. This indicates that to make the use of relational systems possible, restrictions have to be imposed on XML schemas and mappings, as well as on XML shredding schemes. We isolate a set of five requirements that must be fulfilled in order to have a faithful representation of the XML data-exchange problem by a relational translation. We then demonstrate that these requirements naturally suggest the in-lining technique for data-exchange tasks. Our key contribution is to provide shredding algorithms for schemas, documents, mappings and queries, and demonstrate that they enable us to correctly perform XML data-exchange tasks using a relational system.

Describing immovable monuments with metadata standards

Collections with immovable monuments are less researched than other material of our culture from the aspect of description. This led us to study the metadata elements hosted in various heterogeneous schemas by local inventories, used to describe different types of immovable monuments. In order to achieve some form of semantic interoperability, a series of crosswalks was defined among a derived schema hosting metadata elements from various inventories and official metadata standards that are used to describe architectural works. This allowed us to identify which of these schemas would better fit in these records, in order to reduce semantic heterogeneity. We also highlight weaknesses and adequacies of these standards in the description of immovable monuments, placing particular emphasis in the inevitable semantic gap. Finally, a quantitative measure was defined for the degree of different types of coverage of a source schema by a target schema in order to evaluate the appropriateness of standards.

Answering Non-Monotonic Queries in Relational Data Exchange

Relational data exchange is the problem of translating relational data from a source schema into a target schema, according to a specification of the relationship between the source data and the target data. One of the basic issues is how to answer queries that are posed against target data. While consensus has been reached on the definitive semantics for monotonic queries, this issue turned out to be considerably more difficult for non-monotonic queries. Several semantics for non-monotonic queries have been proposed in the past few years. This article proposes a new semantics for non-monotonic queries, called the GCWA*-semantics. It is inspired by semantics from the area of deductive databases. We show that the GCWA*-semantics coincides with the standard open world semantics on monotonic queries, and we further explore the (data) complexity of evaluating non-monotonic queries under the GCWA*-semantics. In particular, we introduce a class of schema mappings for which universal queries can be evaluated under the GCWA*-semantics in polynomial time (data complexity) on the core of the universal solutions.

EIRENE

One of the first steps in the process of integrating information from multiple sources into a desired target format is to specify the relationships, called schema mappings, between the source schemas and the target schema. In this demonstration, we showcase a new methodology for designing schema mappings. Our system Eirene interactively solicits data examples from the mapping designer in order to design a schema mapping between a source schema and a target schema. A data example, in this context, is a pair consisting of a source instance and a target instance showing the desired outcome of performing data exchange using the schema mapping being designed. One of the central parts of the system is a module that, given a set of data examples, either returns a "best" fitting schema mapping, or reports that no fitting schema mapping exists.

Tableaux-based optimization of schema mappings for data integration

The task of combining data residing at different sources to provide the user a unified view is known as data integration. Schema mappings act as glue between the global schema and the source schemas of a data integration system. Global-and-local-as-view (GLAV) is one the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database. We investigate a general technique for expressing a GLAV mapping by a tabular structure called mapping assertion tableaux (MAT). In a similar way, we also express the tuple generating dependency (tgd) and equality generating dependency (egd) constraints by tabular forms, called tabular tgd (TTGD) and tabular egd (TEGD), respectively. A set consisting of the MATs, TTGDs and TEGDs are called schema mapping tableaux (SMT). We present algorithms that use SMT as operator on an instance of the source schema to produce an instance of the target schema. We show that the target instances computed by the SMT are `minimal' and `most general' in nature. We also define the notion of equivalence between the schema mappings of two data integration systems and present algorithms that optimize schema mappings through the manipulation of the SMT.

Rewriting of visibly pushdown languages for XML data integration

In this work, we focus on XML data integration by studying rewritings of XML target schemas in terms of source schemas. Rewriting is very important in data integration systems where the system is asked to find and assemble XML documents from the data sources and produce documents that satisfy a target schema. As schema representation, we consider Visibly Pushdown Automata (VPAs), which accept Visibly Pushdown Languages (VPLs). The latter have been shown to coincide with the family of (word-encoded) regular tree languages, which are the basis of formalisms for specifying XML schemas. Furthermore, practical semi-formal XML schema specifications (defined by simple pattern conditions on XML) compile into VPAs that are exponentially more concise than other representations based on tree automata. Notably, VPLs enjoy a “well-behavedness” that facilitates us in addressing rewriting problems for XML data integration. Based on VPAs, we positively solve these problems, and present detailed complexity analyses.

Data Exchange: Algorithm for Computing Maybe Answers for Relational Algebra Queries

The concept of certain answers is usually used in the definition of the semantics of answering queries in data exchange. But as certain answers are defined as a relation without null values, the approaches for answering queries over databases do not always lead to semantically correct answer. In order to obtain all possible semantically correct answers, maybe answers have been considered along with certain answers. In this paper, an algorithm is presented that produces semantically correct maybe answers to simple relational algebra queries over target schemas of data exchange under the closed world assumption. As there may be infinitely many maybe answers, sophisticated and restricted representation techniques are used to represent them compactly. Then using the compact representation, an algorithm to compute possible (i.e., maybe) answers incrementally is designed. Finally the algorithm is implemented for a fragment of relational algebra.