Following the success of the second KomIS edition in 2015 held in Colmar, Alsace (the conference best paper has been awarded to a KomIS paper!) and the positive feedbacks given by 70+ people attended over the past two editions, we are happy to announce the 3rd edition of KomIS, that will be held in Madrid, Spain, as special session of the DATA 2017 conference. The challenges and discussions that emerged in the last year's edition amongst speakers, attendees, and reviewers set the baseline for this year's. Thus, KomIS 2017 will report experiences and lessons learned tackling with “Applications of Big Data Analytics and BI - methodologies, techniques and tools”.

Today huge masses of data are available, thanks to the wide diffusion of Information Systems, which represent the backbone of an increasing number of services and applications. Actually, Enterprises and PAs executives recognise that timely, accurate and significant knowledge derived from these data represent a valuable value as they allow one to deeply understand social, economic, and business phenomena and to improve competitiveness in a dynamic business environment. Here, leveraging Knowledge Discovery techniques to such Information Systems can play a key role, especially in BI applications whose aims are to combine and analyse very large volumes of data to obtain meaningful and useful information for business goals.

The purpose of this special session is to foster a cross-fertilisation between researchers working on Knowledge Discovery and Information Systems with a particular focus on Big Data analytics and BI applications in real-life scenarios, that usually involves computer scientists, mathematicians, and statisticians working in close cooperation with application domain-experts. 

We encourage contributions focusing and reporting experiences and lessons learned in dealing with real-world data applications in public or private sectors. Contributions should discuss the challenges tackled and the solutions adopted, figuring out how one or more of the Knowledge Discovery tasks have been addressed, such as data sources selection and integration, data processing, transformation and cleaning, data mining, data design and visualisation. Furthermore, the sheer volume of available data also raises significant security and privacy concerns, including the potential for inferring sensitive information by combining multiple pieces of non-sensitive information. In order to prevent data leaks and data contamination, decision makers in different roles and with different clearance levels must be presented with different bodies of knowledge, in accordance with their respective clearance level and on a need-to-know basis.

This special session is the ideal venue for discussing what can be shared in terms of experience, techniques, tools, modelling paradigms, real-life problems and to identify new directions on this topic.