Track Information

Track: Big Data and Analytics & Machine Learning

In recent years the necessity of analysis of huge amount of data has gained increasing attention of researchers and practitioners. The term big data was coined and has started to appear in many different domains including retail, banking, insurance, health, and education. Smart cities may use big data to improve planning and create better services for their citizens. The use of semantics may help utilization of big data. The purpose of this track is to gather researchers and practitioners who are dealing with solving real life issues with support of semantics and big data analysis. Special attention will be paid to applications of semantics and big data in intelligent cities design.

The track is well aligned with the conference theme on climate change as big data analytics offers valuable insights into climate change by processing and analyzing large and complex datasets. By leveraging advanced analytics techniques, it contributes to climate modeling, real-time monitoring, impact assessments, energy efficiency, and communication efforts. The integration of big data analytics with other emerging technologies can unlock new possibilities in addressing the multifaceted challenges of climate change and facilitating a transition to a more sustainable future.

Topics of interests include, but are not limited to:

  • Big Data, Climate Modeling and Prediction
  • Big Data,, Real-Time Monitoring and Early Warning Systems
  • Big Data, and Climate Impact Assessments
  • Analytics and Energy Efficiency and Emissions Reduction
  • Big Data, and Climate Change Communication and Public Engagement
  • Emerging Data Sources for Big Data
  • Smart Cities, Smart Buildings, Smart People
  • Utilization of Big Data in Smart Cities, e.g. GPS
  • Use of Sensor Data
  • Social Media Data Mining
  • Anomalies Detection Using Big Data Analysis
  • Decision Making using Big Data
  • Big Data Analysis in the context of Privacy, Trust and Security
  • The Power and Danger of Big Data
  • Big Data Visualization
  • Challenges of Big Data Processing
  • Big Data Case Studies and Applications
  • Semantics in Big Data Analysis
  • Big RDF Data
  • Semantic Analysis of Big Data
  • Open Data
  • Open Linked Data
  • Big data and machine learning

Track Chair

Lasse Berntzen, University of South-Eastern Norway, Norway, lasse.berntzen@usn.no