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

The Inverse of a Schema Mapping

The inversion of schema mappings has been identified as one of the fundamental operators for the development of a general framework for data exchange, data integration, and more generally, for metadata management. Given a mapping M from a schema S to a schema T, an inverse of M is a new mapping that describes the reverse relationship fromT to S, and that is semantically consistent with the relationship previously established by M. In practical scenarios, the inversion of a schema mapping can have several applications. For example, in a data exchange context, if a mapping M is used to exchange data from a source to a target schema, an inverse of M can be used to exchange the data back to the source, thus reversing the application of M. The formalization of a clear semantics for the inverse operator has proved to be a very difficult task. In fact, during the last years, several alternative notions of inversion for schema mappings have been proposed in the literature. This chapter provides a survey on the different formalizations for the inverse operator and the main theoretical and practical results obtained so far. In particular, we present and compare the main proposals for inverting schema mappings that have been considered in the literature. For each one of them we present their formal semantics and characterizations of their existence. We also present algorithms to compute inverses and study the language needed to express such inverses

Semantic Query Reformulation in Social PDMS

We consider social peer-to-peer data management systems (PDMS), where each peer maintains both semantic mappings between its schema and some acquaintances, and social links with peer friends. In this context, reformulating a query from a peer's schema into other peer's schemas is a hard problem, as it may generate as many rewritings as the set of mappings from that peer to the outside and transitively on, by eventually traversing the entire network. However, not all the obtained rewritings are relevant to a given query. In this paper, we address this problem by inspecting semantic mappings and social links to find only relevant rewritings. We propose a new notion of 'relevance' of a query with respect to a mapping, and, based on this notion, a new semantic query reformulation approach for social PDMS, which achieves great accuracy and flexibility. To find rapidly the most interesting mappings, we combine several techniques: (i) social links are expressed as FOAF (Friend of a Friend) links to characterize peer's friendship and compact mapping summaries are used to obtain mapping descriptions; (ii) local semantic views are special views that contain information about external mappings; and (iii) gossiping techniques improve the search of relevant mappings. Our experimental evaluation, based on a prototype on top of PeerSim and a simulated network demonstrate that our solution yields greater recall, compared to traditional query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM

Use of Schema Associative Mapping for Synchronization of the Virtual Machine Audit Logs

Abstract. Compute cloud interoperability across different domains represents a major challenge for the System administrator community. This work takes a look at the issues for enabling heterogeneous synchronization of virtual disk log attributes by use of an associative mapping technique. We explore this concern as a function of providing secure log auditing for the virtual machine (VM) cloud. Our contribution provides novel theoretical foundations that can be used to establish these synchronized log audit requirements supported by practical case study results

METIS in PArADISE: Provenance Management bei der Auswertung von Sensordatenmengen für die Entwicklung von Assistenzsystemen

n diesem Beitrag soll ein langfristiges Forschungsvorhaben im Bereich der Informatik und Elektrotechnik an der Universität Rostock vorgestellt werden, in dem wissenschaftliche Experimente in der Informatik, der Zellbiologie und der Medizin (neurodegenerative Erkrankungen) durch effiziente Analyseverfahren auf sehr großen Mengen von Mess- oder Sensordaten unterstützt und im Sinne des Provenance Management rückverfolgbar gemacht werden sollen. Im Bereich der Informatik ist das experimentelle Anwendungsgebiet das der Erforschung und systematischen Entwicklung von Assistenzsystemen. Da in Assistenzsystemen unterstützte Personen durch eine Vielzahl von Sensoren beobachtet werden, müssen auch Privatheitsaspekte bereits während der Phase der Modellbildung berücksichtigt werden, um diese bei der konkreten Konstruktion des Assistenzsystems automatisch in den Systementwurf zu integrieren. Die Datenbankteilaspekte dieses Forschungsgebietes werden im Beitrag näher beleuchtet: Neben der effizienten Auswertung großer Mengen von Mess- und Sensordaten sind dies das Provenance Management und die Integration von Privatheitsbedingungen. Um diese Problemkreise zu verknüpfen, treffen zwei extrem unterschiedliche Datenbankthemen aufeinander: (1) Ableitung inverser Schema- und Instanzabbildungen, die üblicherweise in der Datenbankintegration, -föderation und -evolution benötigt werden, aus dem Projekt METIS, sowie (2) Effizienz von Analyseverfahren und Integration von Privatheitsaspekten durch Anfragetransformationen für die Entwicklung von Assistenzsystemen im Projekt PArADISE. Im Beitrag werden wir den gemeinsamen Kern beider Themen in den theoretischen Grundlagen von Datenbanken identifizieren

Composition with Target Constraints

It is known that the composition of schema mappings, each specified by source-to-target tgds (st-tgds), can be specified by a second-order tgd (SO tgd). We consider the question of what happens when target constraints are allowed. Specifically, we consider the question of specifying the composition of standard schema mappings (those specified by st-tgds, target egds, and a weakly acyclic set of target tgds). We show that SO tgds, even with the assistance of arbitrary source constraints and target constraints, cannot specify in general the composition of two standard schema mappings. Therefore, we introduce source-to-target second-order dependencies (st-SO dependencies), which are similar to SO tgds, but allow equations in the conclusion. We show that st-SO dependencies (along with target egds and target tgds) are sufficient to express the composition of every finite sequence of standard schema mappings, and further, every st-SO dependency specifies such a composition. In addition to this expressive power, we show that st-SO dependencies enjoy other desirable properties. In particular, they have a polynomial-time chase that generates a universal solution. This universal solution can be used to find the certain answers to unions of conjunctive queries in polynomial time. It is easy to show that the composition of an arbitrary number of standard schema mappings is equivalent to the composition of only two standard schema mappings. We show that surprisingly, the analogous result holds also for schema mappings specified by just st-tgds (no target constraints). This is proven by showing that every SO tgd is equivalent to an unnested SO tgd (one where there is no nesting of function symbols). Similarly, we prove unnesting results for st-SO dependencies, with the same types of consequences.Comment: This paper is an extended version of: M. Arenas, R. Fagin, and A. Nash. Composition with Target Constraints. In 13th International Conference on Database Theory (ICDT), pages 129-142, 201

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions