Propelled by computational power, the availability of (big and unstructured) data, major advancements in machine intelligence, and unprecedented speeds at which analytics need to be generated and delivered, a wealth of new questions and opportunities arise in creating value for governmental bodies and businesses. As organizations transform into data and analytics-centric enterprises, more research is needed not only on the technical aspects of analytics such as data science algorithms, and computing infrastructure but also on various other organizational issues in the business analytics context (e.g. managerial, strategic, leadership, data governance, and inter-organizational issues). For this track, we invite technical, theoretical, design science, pedagogical and behavioral research as well as novel implementations of data analytics & visualization for varied data (or sources) such as sensors or Internet of Things (IoT) data, text, multimedia, business operations, clickstreams, and user-generated content. We welcome papers examining various contexts including healthcare, security, energy, marketing, supply chain, technology, service, hospitality, education, transportation, fraud prevention, and the environment.

Possible business-oriented topics of submissions include, but are not limited to:

-Big Data and Business Transformation
-Innovative Artifacts for Business Analytics
-Data-Driven Business Modelling
-Data-Driven Process Mining and Innovation
-Data Strategy and Data Privacy
-Social and Ethical Issues in Big Data
-Social Impact of Data Science
-Competences in the Era of Big Data
-Data Science and Industry 4.0
-Big Data Applications / Innovations

Possible technical-oriented topics of submissions include, but are not limited to instantiations of:

-Data mining / Machine Learning / Deep Learning
-Process mining
-Data science
-Text & Multimedia analytics
-Social Network (Media) Analytics
-Real-time data analysis / Stream processing
-Internet of Things (IoT), Sensor data analytics
-Spatial data analysis / Visualization
-Open Data / Data Sets