The goal of operational Business Intelligence (BI) is to help organizations improve the efficiency of their business by giving every "operational worker" insights needed to make better operational decisions, and aligning day-to-day operations with strategic goals. Operational BI reporting contributes to this goal by embedding analytics and reporting information into workflow applications so that the business user has all required information (contextual and business data) in order to make good decisions. EII systems facilitate the construction of operational BI reports by enabling the creation and querying of customized virtual database schemas over a set of distributed and heterogeneous data sources with a low TCO. Queries over these virtual databases feed the operational BI reports. We describe the characteristics of operational BI reporting applications and show that they increase the complexity of the source to target mapping defined between source data and virtual databases. We show that this complexity yields the execution of "mega queries", i.e., queries with possible a 1,000 tables in their FROM clause. We present some key optimization methods that have been successfully implemented in SAP Business Objects Data Federator system to deal with mega queries.

LeSelect is a mediator system which allows scientists to publish their resources (data and programs) so they can be transparently accessed. The scientists can typically issue queries which access distributed published data and involve the execution of expensive functions (corresponding to programs). Furthermore, the queries can involve large objects, such as images (e.g. archived meteorological satellite data). In this context, the costs of transmitting large objects and invoking expensive functions are the dominant factors of execution time. In this paper, we first propose three query execution techniques which minimize these costs by taking full advantage of the distributed architecture of mediator systems like LeSelect. Then we devise parallel processing strategies for queries including expensive functions. Based on experimentation, we show that it is hard to predict the optimal execution order when dealing with several functions. We propose a new hybrid parallel technique to solve this problem and give some experimental results.

Publish/Subscribe is the paradigm in which users express long-term interests (“subscriptions”) and some agent “publishes” events (e.g., offers). The job of Publish/Subscribe software is to send events to the owners of subscriptions satisfied by those events. For example, a user subscription may consist of an interest in an airplane of a certain type, not to exceed a certain price. A published event may consist of an offer of an airplane with certain properties including price. Each subscription consists of a conjunction of (attribute, comparison operator, value) predicates. A subscription closely resembles a trigger in that it is a long-lived conditional query associated with an action (usually, informing the subscriber). However, it is less general than a trigger so novel data structures and implementations may enable the creation of more scalable, high performance publish/subscribe systems. This paper describes an attempt at the construction of such algorithms and its implementation. Using a combination of data structures, application-specific caching policies, and application-specific query processing our system can handle 600 events per second for a typical workload containing 6 million subscriptions.

Execution plans produced by traditional query optimizers for data integration queries may yield poor performance for several reasons. The cost estimates may be inaccurate, the memory available at run-time may be insufficient, or data delivery rate can be unpredictable. We address the problem of unpredictable data arrival rate. We propose to dynamically schedule queries in order to deal with irregular data delivery rate and gracefully adapt to the available memory. Our approach performs careful step-by-step scheduling of several query fragments and processes these fragments based on data arrivals. We describe a performance evaluation that shows important performance gains in several configurations.

Active database systems are now in widespread use. The use of triggers in these systems, however, is difficult because of the complex interaction between triggers, transactions, and application programs. Repeated calculations of rules may incur costly redundant computations in rule conditions and actions. In this paper, we focus on active relational database systems supporting SQL triggers. In this context, we provide a powerful and complete solution to eliminate redundant computations of SQL triggers when they are costly. We define a model to describe programs, rules and their interactions. We provide algorithms to extract invariant subqueries from trigger's condition and action. We define heuristics to memorize the most “profitable” invariants. Finally, we develop a rewriting technique that enables to generate and execute the optimized code of SQL triggers.