The need to understand and manage provenance arises in almost every scientific application. In many cases, information about provenance constitutes the proof of correctness of results that are generated by scientific applications. It also determines the quality and amount of trust one places on the results. For these reasons, the knowledge of provenance of a scientific result is typically regarded to be as important as the result itself. In this paper, we provide an overview of research in provenance in databases and discuss some future research directions. The content of this paper is largely based on the tutorial presented at SIGMOD 2007 [11].

Translating data and data access operations between applications and databases is a longstanding data management problem. We present a novel approach to this problem, in which the relationship between the application data and the persistent storage is specified using a declarative mapping, which is compiled into bidirectional views that drive the data transformation engine. Expressing the application model as a view on the database is used to answer queries, while viewing the database in terms of the application model allows us to leverage view maintenance algorithms for update translation. This approach has been implemented in a commercial product. It enables developers to interact with a relational database via a conceptual schema and an object-oriented programming surface. We outline the implemented system and focus on the challenges of mapping compilation, which include rewriting queries under constraints and supporting non-relational constructs. Categories and Subject Descriptors: H.2 [Database Management], D.3 [Programming Languages]

A schema mapping is a high-level specification that describes the relationship between two database schemas. As schema mappings constitute the essential building blocks of data exchange and data integration, an extensive investigation of the foundations of schema mappings has been carried out in recent years. Even though several different aspects of schema mappings have been explored in considerable depth, the study of schema-mapping optimization remains largely uncharted territory to date. In this paper, we lay the foundation for the development of a theory of schema-mapping optimization. Since schema mappings are constructs that live at the logical level of information integration systems, the first step is to introduce concepts and to develop techniques for transforming schema mappings to “equivalent ” ones that are more manageable from the standpoint of data exchange or of some other data interoperability task. In turn, this has to start by introducing and studying suitable notions of “equivalence ” between schema mappings. To this effect, we introduce the concept of dataexchange equivalence and the concept of conjunctive-query equivalence. These two concepts of equivalence are natural relaxations of the classical notion of logical equivalence; the first captures indistinguishability for data-exchange purposes, while the second captures indistinguishability for conjunctive-query-answering purposes. Moreover, they coincide with logical equivalence on schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), but differ on richer classes of dependencies, such as second-order tuple-generating dependencies (SO tgds) and sets of s-t tgds and target tuple-generating dependencies (target tgds). After exploring the basic properties of these three notions of equivalence between schema mappings, we focus on the following question: under what conditions is a schema mapping conjunctivequery equivalent to a schema mapping specified by a finite set of s-t tgds? We answer this question by obtaining complete characteriza-

Abstract. Ontology mapping is the key to data interoperability in the semantic web. This problem has received a lot of research attention, however, the research emphasis has been mostly devoted to automating the mapping process, even though the creation of mappings often involve the user. As industry interest in semantic web technologies grows and the number of widely adopted semantic web applications increases, we must begin to support the user. In this paper, we combine data gathered from background literature, theories of cognitive support and decision making, and an observational case study to propose a theoretical framework for cognitive support in ontology mapping tools. We also describe a tool called COGZ that is based on this framework. 1

Abstract — A fundamental problem in information integration is that of designing the relationships, called schema mappings, between two schemas. The specification of a semantically correct schema mapping is typically a complex task. Automated tools can suggest potential mappings, but few tools are available for helping a designer understand mappings and design alternative mappings. We describe Muse, a mapping design wizard that uses data examples to assist designers in understanding and refining a schema mapping towards the desired specification. We present novel algorithms behind Muse and show how Muse systematically guides the designer on two important components of a mapping design: the specification of the desired grouping semantics for sets of data and the choice among alternative interpretations for semantically ambiguous mappings. In every component, Muse infers the desired semantics based on the designer’s actions on a short sequence of small examples. Whenever possible, Muse draws examples from a familiar database, thus facilitating the design process even further. We report our experience with Muse on some publicly available schemas. I.

Abstract. Ontology mapping is the key to data interoperability in the semantic web vision. Computing mappings is the first step to applications such as query rewriting, instance sharing, web-service integration, and ontology merging. This problem has received a lot of attention in recent years, but little is known about how users actually construct mappings. Several ontology-mapping tools have been developed, but which tools do users actually use? What processes are users following to discover, track, and compute mappings? How do teams coordinate when performing mappings? In this paper, we discuss the results from an online user survey where we gathered feedback from the community to help answer these important questions. We discuss the results from the survey and the implications they may have on the mapping research community. 1

In the last few years, a lot of attention has been paid to the specification and subsequent manipulation of schema mappings, a problem which is of fundamental importance in metadata management. There have been many achievements in this area, and semantics have been defined for operators on schema mappings such as composition and inverse. However, little research has been pursued towards providing formal tools to compare schema mappings, in terms of their ability to transfer data and avoid storing redundant information, which has hampered the development of foundations for more complex operators as many of them involve these notions. In this paper, we address the problem of providing foundations for metadata management by developing an order to compare the amount of information transferred by schema mappings. From this order we derive several other criteria to compare mappings, we provide tools to deal with these criteria, and we show their usefulness in defining and studying schema mapping operators. More precisely, we show how the machinery developed can be used to study the extract and merge operators, that have been identified as fundamental for the development of a metadata management framework. We also use our machinery to provide simpler proofs for some fundamental results regarding the inverse operator, and we give an effective characterization for the decidability of the wellknown schema evolution problem.

Data exchange is the process of converting an instance of one schema into an instance of a different schema according to a given specification. Recent data exchange systems have largely dealt with the case where the schemas are given a priori and transformations can only migrate data from the first schema to an instance of the second schema. In particular, the ability to perform data-metadata translations, transformation in which data is converted into metadata or metadata is converted into data, is largely ignored. This paper provides a systematic study of the data exchange problem with data-metadata translation capabilities. We describe the problem, our solution, implementation and experiments. Our solution is a principled and systematic extension of the existing data exchange framework; all the way from the constructs required in the visual interface to specify data-metadata correspondences, which naturally extend the traditional value correspondences, to constructs required for the mapping language to specify data-metadata translations, and algorithms required for generating mappings and queries that perform the exchange.

Schema integration is the problem of creating a unified target schema based on a set of existing source schemas that relate to each other via specified correspondences. The unified schema gives a standard representation of the data, thus offering a way to deal with the heterogeneity in the sources. In this paper, we develop a method and a design tool that provide: 1) adaptive enumeration of multiple interesting integrated schemas, and 2) easy-to-use capabilities for refining the enumerated schemas via user interaction. Our method is a departure from previous approaches to schema integration, which do not offer a systematic exploration of the possible integrated schemas. The method operates at a logical level, where we recast each source schema into a graph of concepts with Has-A relationships. We then identify matching concepts in different graphs by taking into account the correspondences between their attributes. For every pair of matching concepts, we have two choices: merge them into one integrated concept or keep them as separate concepts. We develop an algorithm that can systematically output, without duplication, all possible integrated schemas resulting from the previous choices. For each integrated schema, the algorithm also generates a mapping from the source schemas to the integrated schema that has precise information-preserving properties. Furthermore, we avoid a full enumeration, by allowing users to specify constraints on the merging process, based on the schemas produced so far. These constraints are then incorporated in the enumeration of the subsequent schemas. The result is an adaptive and interactive enumeration method that significantly reduces the space of alternative schemas, and facilitates the selection of the final integrated schema.

In this article we present extensional mappings, that are based on second order tuple generating dependencies between models in our Generic Role-based Metamodel GeRoMe. Our mappings support data translation between heterogeneous models, such as XML Schemas, relational schemas, or OWL ontologies. The mapping language provides grouping functionalities that allow for complete restructuring of data, which is necessary for handling object oriented models and nested data structures such as XML. Furthermore, we present algorithms for mapping composition and optimization of the composition result. To verify the genericness, correctness, and composability of our approach we implemented a data translation tool and mapping export for several data manipulation languages. Furthermore, we address the question how generic schema mappings can be harnessed for answering queries against an integrated global schema.