The problem of exchanging data between different databases with different schemas is an area of immense importance. Consequently data exchange has been one of the most active research topics in databases over the past decade. Foundational questions related to data exchange largely revolve around three key problems: how to build target solutions; how to answer queries over target solutions; and how to manipulate schema mappings themselves? The last question is also known under the name 'metadata management', since mappings represent metadata, rather than data in the database. In this book the authors summarize the key developments of a decade of research. Part I introduces the problem of data exchange via examples, both relational and XML; Part II deals with exchanging relational data; Part III focuses on exchanging XML data; and Part IV covers metadata management.

Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target. Such a target instance should correctly represent information from the source instance under the constraints imposed by the target schema, and it should allow one to evaluate queries on the target instance in a way that is semantically consistent with the source data. Data exchange is an old problem that re-emerged as an active research topic recently, due to the increased need for exchange of data in various formats, often in e-business applications.In this lecture, we give an overview of the basic concepts of data exchange in both relational and XML contexts. We give examples of data exchange problems, and we introduce the main tasks that need to addressed. We then discuss relational data exchange, concentrating on issues such as relational schema mappings, materializing target instances (including canonical solutions and cores), query answering, and query rewriting. After that, we discuss metadata management, i.e., handling schema mappings themselves. We pay particular attention to operations on schema mappings, such as composition and inverse. Finally, we describe both data exchange and metadata management in the context of XML. We use mappings based on transforming tree patterns, and we show that they lead to a host of new problems that did not arise in the relational case, but they need to be addressed for XML. These include consistency issues for mappings and schemas, as well as imposing tighter restrictions on mappings and queries to achieve tractable query answering in data exchange.

Research directions for Principles of Data ManagementPDM played a foundational role in the relational database model, with the robust connection between algebraic and calculus-based query languages, the connection between integrity constraints and database design, key insights for the field of query optimization, and the fundamentals of consistent concurrent transactions. This early work included rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. Since the 1990s we have seen an overwhelming increase in both the production of data and the ability to store and access such data. This has led to a phenomenal metamorphosis in the ways that we manage and use data. During this time, we have gone (1) from stand-alone disk-based databases to data that is spread across and linked by the Web, (2) from rigidly structured towards loosely structured data, and (3) from relational data to many different data models (hierarchical, graph-structured, data points, NoSQL, text data, image data, etc.). Research on PDM has developed during that time, too, following, accompanying and influencing this process. It has intensified research on extensions of the relational model (data exchange, incomplete data, probabilistic data, . . . ), on other data models (hierachical, semi-structured, graph, text, . . . ), and on a variety of further data management areas, including knowledge representation and the semantic web, data privacy and security, and data-aware (business) processes. Along the way, the PDM community expanded its cross-fertilization with related areas, to include automata theory, web services, parallel computation, document processing, data structures, scientific workflow, business process management, data-centered dynamic systems, data mining, machine learning, information extraction, etc.Looking forward, three broad areas of data management stand out where principled, mathematical thinking can bring new approaches and much-needed clarity. The first relates to the full lifecycle of so-called "Big Data Analytics", that is, the application of statistical and machine learning techniques to make sense out of, and derive value from, massive volumes of data. The second stems from new forms of data creation and processing, especially as it arises in applications such as web-based commerce, social media applications, and dataaware workflow and business process management. The third, which is just beginning to emerge, is the development of new principles and approaches in support of ethical data management. We briefly illustrate some of the primary ways that these three areas can be supported by the seven PDM research themes that are explored in this report.The overall lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. As documented in numerous sources, so-called "data wrangling" can form 50% to 80% of the labor costs in an analytics investigation. The challenges of data wrangling can be ...

We study the description logic SQ with number restrictions applicable to transitive roles, extended with either nominals or inverse roles. We show tight 2EXPTIME upper bounds for unrestricted entailment of regular path queries for both extensions and finite entailment of positive existential queries for nominals. For inverses, we establish 2EXPTIME-completeness for unrestricted and finite entailment of instance queries (the latter under restriction to a single, transitive role).

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.