Abstract. This paper describes a rule-based algorithm to derive a relational schema from an extended entity-relationship model. Our work is based on an approach by Atzeni and Torlone in which the source EER model is imported into a universal metamodel, a series of transformations are performed to eliminate constructs not appearing in the relational metamodel, and the result is exported. Our algorithm includes novel features that are needed for practical object to relational mapping systems: First, it generates forward- and reverseviews that transform instances of the source model into instances of the target and back again. These views automate the object-to-relational (O/R) mapping. Second, it supports a flexible mapping of inheritance hierarchies to flat relations that subsumes and extends prior approaches. Third, it propagates incremental updates of the source model into incremental updates of the target. We prove the algorithm’s correctness and demonstrate its practicality in an implementation. 1.

Abstract. The model management operator ModelGen translates a schema expressed in one modelling language into an equivalent schema expressed in another modelling language, and in addition produces a mapping between those two schemas. AutoModelGen is a generic data level implementation of ModelGen that meets these desiderata. Our approach is distinctive in that (i) it takes a generic approach that can be applied to any modelling language, and (ii) it does not rely on knowing the modelling language in which the source schema is expressed in.

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Abstract. It is common to find that the definition or common usage of a data modelling language causes there to be restrictions placed on the evolution of data values that are associated with schemas expressed in that modelling language. This paper terms these restrictions temporal constraints, and defines three types of temporal constraint which are argued to be useful modelling concepts, capturing important real-world semantics about objects and their relationships. By reviewing how these temporal constraints are implied by either the definition or usage of UML and the relational modelling languages, this paper will use the temporal constraints to give precise definitions of modelling concepts that to date have been left only vaguely and partially understood. It will also consider the implementation of these constraints in SQL.

The aim of this work is to provide a robust way for peers with heterogeneous data sources to exchange information in an unreliable network. We address this problem in two ways. Firstly, we define a set of application-layer data exchange protocols to facilitate the discovery of, and communication between, peers. Secondly provide a query processing component with a cache-driven query processor that allows nodes on the network to cache queries and their results on demand, and to use the data caches to give partial or complete answers to a query if the original data sources are unavailable. 1.

Abstract. In model management, the Merge operator takes as input a pair of schemas, together with a set of mappings between their objects, and returns an integrated schema. In this paper we present a new approach to implementing the Merge operator based on semantic mappings between objects. Our approach improves upon previous work by (1) using formal low-level transformation rules that can be translated into higher-level rules and (2) specifying precise BAV mappings, which merge schemas without any information loss or gain. 1

Data integration is the process of building a single view over a collection of data sources. Although well established as a research topic, and supported by some commercial tools, current approaches assume that data exchange between data sources occurs in a standard business environment, with relatively stable data sources and communication channels. In the context of unmanned autonomous vehicles (UAV), reliable data exchange presents a number of challenges. Firstly, there may be the requirement to rapidly integrate a new UAV with different information requirements into an existing network. Secondly, the process of data exchange needs to cope with temporary and/or permanent loss of data sources and communication channels. The RoDeX project deals with these challenges, building upon peer-to-peer (P2P) networking techniques, and the data integration and exchange toolkit. We briefly describe the functionality of the current RoDeX system, and present an example to show how it could be used in one of the demonstration vignettes.

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Dataspace management systems (DSMSs) hold the promise of pay-asyou-go data integration. We describe a comprehensive model of DSMS functionality using an algebraic style. We begin by characterizing a dataspace life cycle and highlighting opportunities for both automation and user-driven improvement techniques. Building on the observation that many of the techniques developed in model management are of use in data integration contexts as well, we briefly introduce the model management area and explain how previous work on both data integration and model management needs extending if the full dataspace life cycle is to be supported. We show that many model management operators already enable important functionality (e.g., the merging of schemas, the composition of mappings, etc.) and formulate these capabilities in an algebraic structure, thereby giving rise to the notion of the core functionality of a DSMS as a many-sorted algebra. Given this view, we show how core tasks in the dataspace life cycle can be enacted by means of algebraic programs. An extended case study illustrates how such algebraic programs capture a challenging, practical scenario.

Abstract. The wide range of data sources available today means that the integration of heterogeneous data sources is now a common and important problem. It is even more challenging in a P2P environment where peers often do not know in advance which schemas of other peers will suit their information needs and there is potentially a greater diversity of data modelling languages in use. In this paper, we propose a new approach to P2P inter model data integration which supports multiple data models whilst allowing peers the flexibility of choosing how to integrate their schemas. 1