Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehouse such as facts and dimension hierarchies to explore the information and aggregate it at different levels of detail in an accurate way. A conceptual model of such MD structures serves as abstract basis of the subsequent implementation according to one specific technology. However, there is a semantic gap between a conceptual model and its implementation which complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools and cause the failure of the whole data warehouse project. To bridge this gap for relationships between facts and dimension, we present an approach at the conceptual level for (i) identifying problematic situations in fact-dimension relationships, (ii) defining these relationships in a conceptual MD model, and (iii) applying a normalization process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipsebased implementation of this normalization process.

Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.

Although data warehouse modeling is an established branch within data warehouse research, there are still lots of opportunities for further work within this area. In this talk, I will first sketch major achievements and trends in conceptual data warehouse modeling and pinpoint open problems along the way. Afterwards, I will take a closer look at the overall design process, focus on the transformation of conceptual data warehouse schemata into logical ones, and argue that there still is a semantic gap between advanced conceptual data models and relational or multidimensional implementations, which needs to be bridged. Finally, I will turn to one specific aspect of the data warehouse lifecycle, namely schema changes, and highlight challenges in data warehouse schema versioning.

Views play an important role as a means to structure information with respect to specific users' needs. While read access through views is easy to handle, update requests through views are dificult in the sense that they have to be translated into appropriate updates on database relations. In this paper the "constant complement translator" approach towards view updating proposed by Bancilhon and Spyratos is revisited within the realm of SQL databases, and a novel characterization is established showing that constant complement translators exist precisely if users have a chance to undo all effects of their view updates using further view updates. Based on this characterization view updates with and without constant complement translators are presented. As it turns out that users cannot fully understand updates on views violating the constant complement principle, the application of this principle in the context of external schema design is discussed.

Views as a means to describe parts of a given data collection play an important role in many database applications. In dynamic environments where data is updated, not only information provided by views, but also information provided by data sources yet missing from views turns out to be relevant: Previously, this missing information has been characterized in terms of view complements ; recently, it has been shown that view complements can be exploited in the context of data warehouses to guarantee desirable warehouse properties such as independence and self-maintainability. As the complete source information is a trivial complement for any view, a natural interest for "small" or even "minimal" complements arises. However, the computation of minimal complements is still not very well understood. In this article, it is shown how to compute reasonably small (and in special cases even minimal) complements for a large class of relational views.

e-learning has become an important topic, both in academia and industry. This paper reports on a newly created Web-based platform, called eXtreme e-Learning eXperience (XLX), to perform exercise work of technically oriented graduate-level university courses, in support of blended learning. In addition to providing a course portal with traditional teaching material, an e-mail list, and a discussion forum, XLX's main contribution lies in a personalized training section that allows transparent access to underlying (possibly commercial) third-party systems such as database systems, XSLT processors, or workflow management systems for hands-on experience and increased students' motivation.

Views as a means to describe parts of a given data collection play an important role in many database applications. In dynamic environments, where data is updated, not only information provided by views, but also information provided by data sources but missing from views turns out to be relevant: Previously, this missing information was characterized in terms of view complements; recently, it was shown that view complements can be exploited in the context of data warehouses to guarantee desirable warehouse properties such as independence and self-maintainability. As the complete source information is a trivial complement for any given view, a natural interest for "small" or even "minimal" complements arises. However, the computation of minimal complements is still not too well understood. In this paper, we show how to compute reasonably small (and in special cases even minimal) complements for monotonic relational views, where the complexity of constructing complements is polynomial in the size of schema information.

The quest for unified correctness criteria in database concurrency control is addressed from a new perspective. A family of Herbrand semantics is presented, where each semantics provides an interpretation for operations in the read-write model of transactions. Using commutativity arguments, each semantics leads to a notion of conflict, which then gives rise to distinct classes of serializable schedules. Surprisingly, the classical notion of serializability with respect to two of these sematics, update-in-place and deferred-update semantics, already embodies a unified correctness criterion; moreover, prefix-closed variants of it allow for a higher degree of transaction parallelism than, for example, prefix-reducibility. Finally, it is shown that previous criteria may permit undesirable schedules, which are ruled out by a stronger notion of serializability that captures all intuitively correct schedules, but is incomparable to prefix-reducibility and the classes of schedules recognized by optimistic protocols.

Views over databases have recently regained attention in the context of data warehouses, which are seen as materialized views. In this setting, efficient view maintenance is an important issue, for which the notion of self-maintainability has been identified as desirable. We extend self-maintainability to (query and update) independence, and we establish an intuitively appealing connection between warehouse independence and view complements. Moreover, we study minimal complements and show how to compute them in the presence of key constraints and inclusion dependencies in the underlying databases. Taking advantage of these complements, an algorithm is outlined for the specification of independent warehouses.