n today’s competitive and highly dynamic environment, analyzing data to understand how the business is performing, to predict outcomes and trends, and to improve the effectiveness of business processes underlying business operations has become critical. The traditional appraoch to reporting is not longer adequate, users now demand easy-to-use intelligent platforms and applications capable of analyzing real-time business data to provide insight and actionable information at the right time. The end goal is to improve the enterprise performance by better and timelier decision making, enabled by the availability of up-to-date, high quality information.

As a response, the notion of "real-time enterprise" has emerged and is beginning to be recognized in the industry. Gartner defines it as “using up-to-date information, getting rid of delays, and using speed for competitive advantage is what the real-time enterprise is all about...Indeed, the goal of the real-time enterprise is to act on events as they happen” Although there has been progress in this direction and many companies are introducing products towards making this vision reality, there is still a long way to go. In particular, the whole lifecycle of business intelligence requires new techniques and methodologies capable of dealing with the new requirements imposed by the real-time enterprise. From the capturing of real-time business performance data to the injection of actionable information back into business processes, all the stages of the Business Intelligence (BI) cycle call for new algorithms and paradigms as the basis of new functionalities including dynamic integration of real-time data feeds from operational sources, evolution of ETL transformations and analytical models, and dynamic generation of adaptive real-time dashboards, just to name a few.


1. Models and Concepts for Real-time Enterprise Business Intelligence

- Change management
- Metadata, constraints and consistency issues
- Data quality and cleaning
- Decision making
- Metric definition and management
- Optimization
- Schema design

2. Architectures for Real-time Enterprise Business Intelligence

- Architectures
- Change management
- Data capture in real-time
- Performance and scalability
- Real-time decision support
- Staging
- Tuning and management of the real-time data warehouse

3. Uses cases of Real-time Enterprise Business Intelligence

- Case studies
- Pitfalls in applying B.I. tools to real-life problems
- Lessons learned from large practical applications of real-time BI

4. Applications of Real-time Enterprise Business Intelligence

- Applications
- Control of the real-time enterprise

5. Technologies for the Real-time Enterprise Business Intelligence

- Data warehouse evolution
- ETL for the real-time data warehouse
- Data mining and data analysis in real-time
- Real-time Business Activity Monitoring (BAM)
- Real-time OLAP
- Real-time operational data stores
- Streaming data
- Visualization

SPECIAL NOTE: To keep the focus of this workshop a paper must clearly state how the work presented relates to the real-time enterprise, how the results apply in this context and if possible, to provide some validation of the application of the work to a real-life problem.

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