Abstract. We consider indexing sliding windows in main memory over on-line data streams. Our proposed data structures and query semantics are based on a division of the sliding window into sub-windows. By classifying windowed operators according to their method of execution, we motivate the need for two types of windowed indices: those which provide a list of attribute values and their counts for answering set-valued queries, and those which provide direct access to tuples for answering attribute-valued queries. We propose and evaluate indices for both of these cases and show that our techniques are more efficient than executing windowed queries without an index. 1

The database volumes of enterprise resource planning (ERP) systems like SAP R/3 are growing at a tremendous rate and some of them have already reached a size of several Terabytes. OLTP (Online Transaction Processing) databases of this size are hard to maintain and tend to perform poorly. Therefore most database vendors have implemented new features like horizontal partitioning to optimize such mission critical applications. Horizontal partitioning was already investigated in detail in the context of shared nothing distributed database systems but today's ERP systems mostly use a centralized database with a shared everything architecture. In this work, we therefore investigate how an SAP R/3 system performs when the data in the underlying database is partitioned horizontally.

According to a survey of the University of Berkeley [6], about 5 Exabytes of new information has been created in 2002. This information explosion affects also the database volumes of enterprise resource planning (ERP) systems like SAP R/3, the market leader for ERP systems. Just like the overall information explosion, the database volumes of ERP systems are growing at a tremendous rate and some of them have reached a size of several Terabytes. OLTP (Online Transaction Processing) databases of this size are hard to maintain and tend to perform poorly. One way to limit the size of a database is data staging, i.e., to make use of an SAP technique called archiving. That is, data which are not needed for every-day operations are demoted from the database (disks) to tertiary storage (tapes). In cooperation with our research group, SAP is adapting their archiving techniques to accelerate the archiving process by integrating new technologies like XML and advanced database features. However, so far no benchmark existed to evaluate different archiving scenarios and to measure the impact of a change in the archiving technique. We therefore designed and implemented a generic benchmark which is applicable to many different system layouts and allows the users to evaluate various archiving scenarios.