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.

The erosion of trust put in traditional database servers and in Database Service Providers (DSP), the growing interest for different forms of data dissemination and the concern for protecting children from suspicious Internet content are different factors that lead to move the access control from servers to clients. Due to the intrinsic untrustworthiness of client devices, client-based access control solutions rely on data encryption. The data are kept encrypted at the server and a client is granted access to subparts of them according to the decryption keys in its possession. Several variations of this basic model have been proposed (e.g., [1, 6]) but they have in common to minimize the trust required on the client at the cost of a static way of sharing data. Indeed, whatever the granularity of sharing, the dataset is split in subsets reflecting a current sharing situation, each encrypted with a different key. Once the dataset is encrypted, changes in the access control rules definition may impact the subset boundaries, hence incurring a partial re-encryption of the dataset and a potential redistribution of keys.