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

In the study of data exchange one usually assumes an open-world semantics, making it possible to extend instances of target schemas. An alternative closed-world semantics only moves ‘as much data as needed’ from the source to the target to satisfy constraints of a schema mapping. It avoids some of the problems exhibited by the open-world semantics, but limits the expressivity of schema mappings. Here we propose a mixed approach: one can designate different attributes of target schemas as open or closed, to combine the additional expressivity of the open-world semantics with the better behavior of query answering in closed worlds. We define such schema mappings, and show that they cover a large space of data exchange solutions with two extremes being the known open and closed-world semantics. We investigate the problems of query answering and schema mapping composition, and prove two trichotomy theorems, classifying their complexity based on the number of open attributes. We find conditions under which schema mappings compose, extending known results to a wide range of closed-world mappings. We also provide results for restricted classes of queries and mappings guaranteeing lower complexity.

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.

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.

The notion of certain answers arises when one queries incompletely specified databases, e.g., in data integration and exchange scenarios, or databases with missing information. While in the relational case this notion is well understood, there is no natural analog of it for XML queries that return documents. We develop an approach to defining certain answers for such XML queries, and apply it in the settings of incomplete information and XML data exchange. We first revisit the relational case, and show how to present the key concepts related to certain answers in a new model-theoretic language. This new approach naturally extends to XML. We prove a number of generic, application-independent results about computability and complexity of certain answers produced by it. We then turn our attention to a pattern-based XML query language with trees as outputs, and present a technique for computing certain answers that relies on the notion of a basis of a set of trees. We show how to compute such bases for documents with nulls and for documents arising in data exchange scenarios, and provide complexity bounds. While in general complexity of query answering in XML data exchange could be high, we exhibit a natural class of XML schema mappings for which not only query answering, but also many static analysis problems can be solved efficiently.

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.

The amount of information produced in the world increases by 30 % every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [11, 13]. Data integration systems face two folds of challenges. First, data from disparate sources are often heterogeneous. Heterogeneity can exist at the schema level, where different data sources often describe the same domain using different schemas; it can also exist at the instance level, where different sources can represent the same real-world entity in different ways. There has been rich body of work on resolving heterogeneity in data, including, at the schema level, schema mapping and matching [14], model management [1], answering queries using views [12], data exchange [8], and at the instance level, record linkage (entity resolution, object matching, reference linkage, etc.) [7, 15], string similarity comparison [4], etc. Second, different sources can provide conflicting data. Conflicts can arise because of incomplete data, erroneous data, and out-of-date data. Returning incorrect data in a query result can be misleading and even harmful: one may contact a person by an out-of-date phone number, visit a clinic at a wrong address, and even make poor business decisions. It is thus critical for data integration systems to resolve conflicts from various sources and identify true values. This problem becomes especially prominent with the ease of publishing and spreading false information on the Web. This tutorial focuses on data fusion, which addresses the second challenge by fusing records on the same real-world entity into a single record and resolving possible conflicts from different data sources. Data fusion plays an important