Most existing concepts in data warehousing provide a central database system storing gathered raw data and redundantly computed materialized views. While in current system architectures client tools are sending queries to a central data warehouse system and are only used to graphically present the result, the steady rise in power of personal computers and the expansion of network bandwidth makes it possible to store replicated parts of the data warehouse at the client thus saving network bandwidth and utilizing local computing power. Within such a scenario a - potentially mobile - client does not need to be connected to a central server while performing local analyses. Although this scenario seems attractive, several problems arise by introducing such an architecture: For example schema data could be changed or new fact data could be available. This paper is focusing on the first problem and presents ideas on how changed schema data can be detected and efficiently synchronized between client and server exploiting the special needs and requirements of data warehousing.

The issue of write-read contention is one of the most prevalent problems when deploying real-time data warehouses. With increasing load, updates are increasingly delayed and previously fast queries tend to be slowed down considerably. However, depending on the user requirements, we can improve the response time or the data quality by scheduling the queries and updates appropriately. If both criteria are to be considered simultaneously, we are faced with a so-called multi-objective optimization problem. We transformed this problem into a knapsack problem with additional inequalities and solved it efficiently. Based on our solution, we developed a scheduling approach that provides the optimal schedule with regard to the user requirements at any given point in time. We evaluated our scheduling in an extensive experimental study, where we compared our approach with the respective optimal schedule policies of each single optimization objective.

Materialized views are a well-known optimization strategy with the potential for massive improvements in query processing time, especially for aggregation queries over large tables. To realize this potential, the query optimizer has to know how and when to exploit materialized views. Reporting functions represent a novel technique to formulate sequence-oriented queries in SQL. They provide a column-wise ordering, partitioning, and windowing mechanism for aggregation functions and therefore extend the well-known way of grouping and applying simple aggregation functions. Up to now, current work has not considered the frequently used reporting functions in data warehouse environments. In this paper, we introduce materialized reporting function views and show how to rewrite queries with reporting functions as well as aggregation queries to this new kind of materialized views. We demonstrate the efficiency of our approach with a large number of experiments.

Data marts and caching are two closely related concepts in the domain of multi-dimensional data. Both store pre-computed data to provide fast response times for complex OLAP queries, and for both it must be guaranteed that every query can be completely processed. However, they differ extremely in their update behaviour which we utilise to build a specific data mart extended by cache semantics. In this paper, we introduce a novel cache exploitation concept for data marts - coarse-grained caching - in which the containedness check for a multi-dimensional query is done through the comparison of the expected and the actual cardinalities. Therefore, we subdivide the multi-dimensional data into coarse partitions, the so called cubletets, which allow to specify the completeness criteria for incoming queries. We show that during query processing, the completeness check is done with no additional costs.

This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations.

This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations.