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
12月04日
2017
12月07日
2017
注册截止日期
留言