In this demo, we present the Microsoft Complex Event Processing (CEP) Server, Microsoft CEP for short. Microsoft CEP is an event stream processing system featured by its declarative query language and its multiple consistency levels of stream query processing. Query composability, query fusing, and operator sharing are key features in the Microsoft CEP query processor. Moreover, the debugging and supportability tools of Microsoft CEP provide visibility of system internals to users. Web click analysis has been crucial to behavior-based online marketing. Streams of web click events provide a typical workload for a CEP server. Meanwhile, a CEP server with its processing capabilities plays a key role in web click analysis. This demo highlights the features of Microsoft CEP under a workload of web click events.

As mainstream data warehouses are growing into the multi-terabyte range, adequate performance for decision support queries remains challenging for database query processors. Proper choice of query plan is essential in data warehouses where fact tables often store billions of rows. This paper discusses query optimization and execution strategies that Microsoft SQL Server employs for decision support queries in dimensionally modeled relational data warehouses. Our approach is based on pattern matching to detect typical star query patterns. When matching the pattern, the optimizer generates additional query plan alternatives specifically optimized for data warehouse performance. For high selectivity queries, the plans use nested loops joins and seeks. Medium selectivity queries in turn rely on right-deep hash joins with bitmap filters. Bitmap filters perform semi-join reductions to efficiently prune out non-qualifying rows early. Final plan choice is left for cost-based optimization which also compares the data warehouse specific plans against conventional query plans. We conducted an extensive experimental investigation using both synthetic workloads and several customer workloads. As our results show, the new plan shapes and execution strategies yield significant performance improvements across the targeted workloads as compared to earlier versions of Microsoft SQL Server.

XML is often used to represent objects that expose different sets of properties. This "property bag" scenario is a prominent use case for the XML support added to Microsoft SQL Server 2005. However, each property extraction in our initial implementation executed as a separate relational subquery. This was problematic since query performance became unacceptable even for small data sizes when returning an increasing number of properties. We addressed this problem by developing an interesting generalization of common subexpressions. This paper makes the following contributions: (1) it introduces an equivalence rewrite for relational query optimization to fold similar scalar subqueries. Several such subqueries are merged into a single equivalent multi-column subquery using both predicate disjunction and rowset pivoting. The rewrite operates at the logical operator level which makes it equally applicable to XML queries and SQL queries. (2) We explain how this optimization can be applied to the XML property bag scenario and how it has been implemented for the XML index in Microsoft SQL Server 2005. (3) An experimental investigation with Microsoft SQL Server 2005 studies the performance characteristics of the optimization. It shows that the optimization yields significant performance improvements - without limiting essential optimizer execution plan choices.

Optimizing SQL subqueries has been an active area in database research and the database industry throughout the last decades. Previous work has already identified some approaches to efficiently execute relational subqueries. For satisfactory performance, proper choice of subquery execution strategies becomes even more essential today with the increase in decision support systems and automatically generated SQL, e.g., with ad-hoc reporting tools. This goes hand in hand with increasing query complexity and growing data volumes, which all pose challenges for an industrial-strength query optimizer.

A common approach to storage and retrieval of XML documents is to store them in a database, together with materialized views on their content. The advantage over "native" XML storage managers seems to be that transactions and concurrency are for free, next to other benefits. But a closer look and preliminary experiments reveal that this results in poor performance of concurrent queries and updates. The reason is that database lock contention hinders parallelism unnecessarily. We therefore investigate concurrency control at the semantic, i.e., XML level and describe a respective transaction manager XMLTM. It features a new locking protocol DGLOCK. It generalizes the protocol for locking on directed acyclic graphs by adding simple predicate locking on the content of elements, e.g., on their text. Instead of using the original XML documents, we propose to take advantage of an abstraction of the XML document collection known as DataGuides. XMLTM allows to run XML processing at the underlying database at low ANSI isolation degrees and to release database locks early without sacrificing correctness in this setting. We have built a complete prototype system that is implemented on top of the XML Extender for IBM DB2. Our evaluation shows that our approach consistently yields performance improvements by an order of magnitude. We stress that our approach can also be implemented within a native XML storage manager, and we expect even better performance.

Our current concern is a scalable infrastructure for information retrieval (IR) with up-to-date retrieval results in the presence of frequent, continuous updates. Timely processing of updates is important with novel application domains, e.g., e-commerce. We want to use off-the-self hardware and software as much as possible. These issues are challenging, given the additional requirement that the resulting system must scale well. We have built PowerDB-IR, a system that has the characteristics sought. This paper describes its design, implementation, and evaluation. PowerDB-IR is a coordination layer for a database cluster. The rationale behind a database cluster is to 'scale-out', i.e., to add further cluster nodes, whenever necessary for better performance. We build on IR-to-database mappings and service decomposition to support high-level parallelism. We follow a three-tier architecture with the database cluster as the bottom layer for storage management. The middle tier provides IR-specific processing and update services. PowerDB-IR has the following features: It allows to insert and retrieve documents concurrently, and it ensures freshness with almost no overhead. Alternative physical data organization schemes provide adequate performance for different workloads. Query processing techniques for the different data organizations efficiently integrate the ranked retrieval results from the cluster nodes. We have run extensive experiments with our prototype using commercial database systems and middleware software products. The main result is that PowerDB-IR shows surprisingly ideal scalability and low response times.

Our concern is the design of a scalable infrastructure for complex application services. We want to find out if a cluster of commodity database systems is well-suited as such an infrastructure. To this end, we have carried out a feasibility study based on document services, e.g. document insertion and retrieval. We decompose a service request into short parallel database transactions. Our system, implemented as an extension of a transaction processing monitor, routes the short transactions to the appropriate database systems in the cluster. Routing depends on the data distribution that we have chosen. To avoid bottlenecks, we distribute document functionality, such as term extraction, over the cluster. Extensive experiments show the following. (1) A relatively small number of components - for example eight components $already suffices to cope with high workloads of more than 100 concurrently active clients. (2) Speedup and throughput increase linearly for insertion operations when increasing the cluster size. These observations also hold when bundling service invocations into transactions at the semantic layer. A specialized coordinator component then implements semantic serializability and atomicity. Our experiments show that such a coordinator has minimal impact on CPU resource consumption and on response times.

When relational database systems were introduced twenty years ago (1980), they were an infrastructure and main platform for application development. With today's information systems, the database system is a storage manager, far away from the applications. Our vision is that hyperdatabases become available that move up and extend database concepts to a higher level, closer to the applications. A hyperdatabase manages distributed objects and software components as well as workflows, in analogy to a database system that manages data and transactions. In short, hyperdatabases, also called "higher order databases", will provide "higher order data independence", e.g., immunity of applications against changes in the implementation of components and workload transparency. They will be the infrastructure for distributed information systems engineering of the future, and they are an abstraction from the host of current infrastructures and middleware technology. The article elaborates on this vision and outlines concrete projects at ETHZ such as PowerDB, a database cluster project. It shows how an efficient document engine can be built on top of a database cluster. A further project studies transactional process management as a layer on top of database transactions. Image similarity and multimedia components is another project where a hyperdatabase coordinates specialized components such as feature extraction and indexing services in a distributed environment.