2,892 research outputs found
Information preserving XML schema embedding
A fundamental concern of information integration in an XML context is the ability to embed one or more source documents in a target document so that (a) the target document conforms to a target schema and (b) the information in the source document(s) is preserved. In this paper, information preservation for XML is formally studied, and the results of this study guide the definition of a novel notion of schema embedding between two XML DTD schemas represented as graphs. Schema embedding generalizes the conventional notion of graph similarity by allowing an edge in a source DTD schema to be mapped to a path in the target DTD. Instance-level embeddings can be defined from the schema embedding in a straightforward manner, such that conformance to a target schema and information preservation are guaranteed. We show that it is NP-complete to find an embedding between two DTD schemas. We also provide efficient heuristic algorithms to find candidate embeddings, along with experimental results to evaluate and compare the algorithms. These yield the first systematic and effective approach to finding information preserving XML mappings.
Encoding databases satisfying a given set of dependencies
Consider a relation schema with a set of dependency constraints. A fundamental question is what is the minimum space where the possible instances of the schema can be "stored". We study the following model. Encode the instances by giving a function which maps the set of possible instances into the set of words of a given length over the binary alphabet in a decodable way. The problem is to find the minimum length needed. This minimum is called the information content of the database. We investigate several cases where the set of dependency constraints consist of relatively simple sets of functional or multivalued dependencies. We also consider the following natural extension. Is it possible to encode the instances such a way that small changes in the instance cause a small change in the code. © 2012 Springer-Verlag
Comparative Analysis of Five XML Query Languages
XML is becoming the most relevant new standard for data representation and
exchange on the WWW. Novel languages for extracting and restructuring the XML
content have been proposed, some in the tradition of database query languages
(i.e. SQL, OQL), others more closely inspired by XML. No standard for XML query
language has yet been decided, but the discussion is ongoing within the World
Wide Web Consortium and within many academic institutions and Internet-related
major companies. We present a comparison of five, representative query
languages for XML, highlighting their common features and differences.Comment: TeX v3.1415, 17 pages, 6 figures, to be published in ACM Sigmod
Record, March 200
The XFM view adaptation mechanism: An essential component for XML data warehouses
In the past few years, with many organisations providing web services for business and communication purposes, large volumes of XML transactions take place on a daily basis.
In many cases, organisations maintain these transactions in their native XML format due to its flexibility for xchanging data between heterogeneous systems. This XML data
provides an important resource for decision support systems. As a consequence, XML technology has slowly been included within decision support systems of data warehouse
systems. The problem encountered is that existing native XML database systems suffer from poor performance in terms of managing data volume and response time for complex
analytical queries. Although materialised XML views can be used to improve the performance for XML data warehouses, update problems then become the bottleneck of using
materialised views. Specifically, synchronising materialised views in the face of changing view definitions, remains a significant issue. In this dissertation, we provide a method for XML-based data warehouses to manage updates caused by the change of view definitions (view redefinitions), which is referred to as the view adaptation problem. In our approach, views are defined using XPath and then modelled using a set of novel algebraic operators and fragments. XPath views are integrated into a single view graph called the XML Fragment
Materialisation (XFM) View Graph, where common parts between different views are shared and appear only once in the graph. Fragments within the view graph can be selected
for materialisation to facilitate the view adaptation process. While changes are applied, our view adaptation algorithms can quickly determine what part of the XFM view graph is affected.
The adaptation algorithms then perform a structural adaptation to update the view graph, followed by data adaptation to update materialised fragments
: Méthodes d'Inférence Symbolique pour les Bases de Données
This dissertation is a summary of a line of research, that I wasactively involved in, on learning in databases from examples. Thisresearch focused on traditional as well as novel database models andlanguages for querying, transforming, and describing the schema of adatabase. In case of schemas our contributions involve proposing anoriginal languages for the emerging data models of Unordered XML andRDF. We have studied learning from examples of schemas for UnorderedXML, schemas for RDF, twig queries for XML, join queries forrelational databases, and XML transformations defined with a novelmodel of tree-to-word transducers.Investigating learnability of the proposed languages required us toexamine closely a number of their fundamental properties, often ofindependent interest, including normal forms, minimization,containment and equivalence, consistency of a set of examples, andfinite characterizability. Good understanding of these propertiesallowed us to devise learning algorithms that explore a possibly largesearch space with the help of a diligently designed set ofgeneralization operations in search of an appropriate solution.Learning (or inference) is a problem that has two parameters: theprecise class of languages we wish to infer and the type of input thatthe user can provide. We focused on the setting where the user inputconsists of positive examples i.e., elements that belong to the goallanguage, and negative examples i.e., elements that do not belong tothe goal language. In general using both negative and positiveexamples allows to learn richer classes of goal languages than usingpositive examples alone. However, using negative examples is oftendifficult because together with positive examples they may cause thesearch space to take a very complex shape and its exploration may turnout to be computationally challenging.Ce mĂ©moire est une courte prĂ©sentation dâune direction de recherche, Ă laquelle jâai activementparticipĂ©, sur lâapprentissage pour les bases de donnĂ©es Ă partir dâexemples. Cette recherchesâest concentrĂ©e sur les modĂšles et les langages, aussi bien traditionnels quâĂ©mergents, pourlâinterrogation, la transformation et la description du schĂ©ma dâune base de donnĂ©es. Concernantles schĂ©mas, nos contributions consistent en plusieurs langages de schĂ©mas pour les nouveaumodĂšles de bases de donnĂ©es que sont XML non-ordonnĂ© et RDF. Nous avons ainsi Ă©tudiĂ©lâapprentissage Ă partir dâexemples des schĂ©mas pour XML non-ordonnĂ©, des schĂ©mas pour RDF,des requĂȘtes twig pour XML, les requĂȘtes de jointure pour bases de donnĂ©es relationnelles et lestransformations XML dĂ©finies par un nouveau modĂšle de transducteurs arbre-Ă -mot.Pour explorer si les langages proposĂ©s peuvent ĂȘtre appris, nous avons Ă©tĂ© obligĂ©s dâexaminerde prĂšs un certain nombre de leurs propriĂ©tĂ©s fondamentales, souvent souvent intĂ©ressantespar elles-mĂȘmes, y compris les formes normales, la minimisation, lâinclusion et lâĂ©quivalence, lacohĂ©rence dâun ensemble dâexemples et la caractĂ©risation finie. Une bonne comprĂ©hension de cespropriĂ©tĂ©s nous a permis de concevoir des algorithmes dâapprentissage qui explorent un espace derecherche potentiellement trĂšs vaste grĂące Ă un ensemble dâopĂ©rations de gĂ©nĂ©ralisation adaptĂ© Ă la recherche dâune solution appropriĂ©e.Lâapprentissage (ou lâinfĂ©rence) est un problĂšme Ă deux paramĂštres : la classe prĂ©cise delangage que nous souhaitons infĂ©rer et le type dâinformations que lâutilisateur peut fournir. Nousnous sommes placĂ©s dans le cas oĂč lâutilisateur fournit des exemples positifs, câest-Ă -dire desĂ©lĂ©ments qui appartiennent au langage cible, ainsi que des exemples nĂ©gatifs, câest-Ă -dire qui nâenfont pas partie. En gĂ©nĂ©ral lâutilisation Ă la fois dâexemples positifs et nĂ©gatifs permet dâapprendredes classes de langages plus riches que lâutilisation uniquement dâexemples positifs. Toutefois,lâutilisation des exemples nĂ©gatifs est souvent difficile parce que les exemples positifs et nĂ©gatifspeuvent rendre la forme de lâespace de recherche trĂšs complexe, et par consĂ©quent, son explorationinfaisable
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