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 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.

We address the problem of efficient evaluation of non-deterministic logic programs in a database context using incremental evaluation algorithms. We consider a representative non-deterministic Datalog extension which gives a formal basis to various production rule languages. We point out a clear space-time tradeoff in the choice made by some algorithms to materialize and incrementally maintain some data in order to compute the meaning of a rule program. We advocate that the data maintained by an incremental evaluation algorithm should be chosen according to some particular properties of a rule program. We identify such properties and provide means to determine them statically based on a syntactic analysis of the bodies and heads of rules. We show that using these properties, one can improve the performance of the two best known incremental evaluation algorithms TREAT and RETE.

Active rules or triggers are widely accepted as powerful mechanisms to implement applications or systems behaviour. Several rule execution models were proposed as extended functionalities for different database systems. However, these models lack in flexibility and adaptability to specific database systems or specific application requirements. In this paper, we propose a generic framework which provides a set of basic functions which can be used to implement any execution model. This framework, called Active-Design toolkit, can be exploited in many situations where applications need specific model or different models for different subsets of rules.

With the recent developments of the Web, we witness a shift from data integration systems to data and program integration applications, allowing communities of users to share their resources: data, programs, and computing power. This work investigates data and program integration in a fully distributed peer-to-peer mediation architecture. The challenge in making such a system succeed at a large scale is twofold. First, we need a simple concept for modeling resources. Second, we need efficient operators for distributed query execution, capable of handling well costly computations and large data transfers. To model heterogeneous resources, we use the model of table with binding patterns, simple yet powerful enough to capture data and programs. To exploit a resource with restricted binding patterns, we propose an efficient BindJoin operator, in which we build optimization techniques for minimizing large data transfers and costly computations. Furthermore, the proposed BindJoin operator delivers most of its output in the early stages of the execution, which is an important asset in a system meant for human interaction. Our experimental evaluation validates the proposed BindJoin algorithm on queries involving expensive programs. It shows that our BindJoin delivers the major part of the result early on during query execution.

In this work, we investigate data and program integration in a fully distribut- ed peer-to-peer mediation architecture. The challenge in making such a system succeed at a large scale is twofold. First, sharing a resource should be easy; therefore, we need a simple concept for modeling resources. Second, we need an efficient architecture for distributed query execution, capable of handling well costly computations and large data transfers. To model heterogeneous resources, we propose using the unified abstraction of table with binding patterns, simple yet powerful enough to capture data and programs. To exploit a resource with restricted binding patterns, we propose an efficient BindJoin operator, following the classical iterator model, in which we build optimization techniques for minimizing large data transfers and costly computations, and maximizing parallelism. Furthermor- e, our BindJoin operator can be tuned to deliver most of its output in the early stages of the execution, which is an important asset in a system meant for human interaction. Our preliminary experimental evaluation validates the proposed BindJoin algorithms, and shows they can provide good performance in queries involving distributed data and expensive programs.

This work investigates the integration of heterogeneous resources, such as data and programs, in a fully distributed peer-to-peer mediation architecture. The challenge in making such a system succeed at a large scale is twofold. First, we need a simple concept for modeling resources. Second, we need efficient operators for distributed query execution, capable of handling well costly computations and large data transfers. To model heterogeneous resources, we use the model of table with binding patterns. To exploit a resource with restricted binding patterns, we propose an efficient BindJoin operator, optimized for minimizing large data transfers and costly computations. Furthermore, the proposed BindJoin operator delivers most of its output in the early stages of the execution, which is an important asset in a system meant for human interaction. Our experimental evaluation validates the proposed BindJoin algorithm on queries involving expensive programs.

In this paper we present a support that helps organizing distributed design of products belonging to a product family. We model the product to be developed as a component-based tree of object templates, develop a logic-based language that allows expressing diverse development constraints and use these constraints to guide the design process. The main objective of our support is to save time and development effort in increasing parallel work and avoiding roll-backs. For that, it reasons in terms of correctness of the future, final state of the product, and verifies that the product state after each operation allows the reacheability of such a state.

A very large number of data sources on environment, energy, and natural resources are available worldwide. Unfortunately, users usually face several problems when they want to search and use relevant information. In the Ecobase project, we address these problems in the context of several environmental applications in Brazil and Europe. We propose a distributed architecture for environmental information systems (EIS) based on the Le Select middleware developed at INRIA. In this paper, we present this architecture and its capabilities, and discuss the lessons learned and open issues.

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.