OBIS 2017 - International Workshop on Open Business Intelligence Systems (OBIS 2017)

Decision-making is a crucial, yet challenging mission in enterprise management. It is still made based on a reactive approach rather than on facts and proactive approaches. This is often due to underprovided in data, unknown correlation between data and goals, conflicting goals and weak defined strategy. Enterprise success depends on fast and well-defined decisions taken by relevant policy makers and business actors in their specific area. OBIS can be seen as a collection of decision support technologies and tools for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. Business Intelligence is currently reinventing itself in a time of technological emerging big data make it possible to explore new opportunities that will revolutionize business intelligence. From there to completely revolutionize business intelligence, and data warehouse based decision support, the Hadoop (development environment) is an excellent complement to such systems. Indeed, the limitations of traditional BI architectures are beginning to be affected, and new applications can be met: the use of unstructured data, sensor data, social media, machine learning and crowd sourcing, huge data volumes to be managed with the enterprise information system integration. The intelligence has always been seen as a separate element of the information system of the company, but with Big Data, this is changing.
The aim of this session is to review the concept of OBIS as an open innovation strategy and address the importance of them in revolutionizing knowledge towards economics and business sustainability. The main objective is to discuss why the concept of BI has become increasingly important and presents some of the top key applications and technologies to implement open BI systems in organizations.
Topics for the workshop include, but are not limited to:
• Intelligent decision support systems
• Intelligent transport systems
• Crowdsourcing systems and application
• Multi-agent systems and Data analytics
• Crowdsourcing, collaboration, and problem solving with social media
• Social media analytics
• Machine learning and decision support systems
• Opinion mining and sentiment analysis
• Business models for innovative use of big data
• Real-time decision making, forecasting, and fraud detection using big data
• Modeling big data and related decision scenarios
• Methodologies for innovations to handle large data sets
• Big data analytics in business applications