Track: Quality Aspects in Big Data Systems

"Big data" has become a major force of research progress in HPC-based data mining and innovation across enterprises of all sizes. A lot of new platforms with increasingly more features for managing big datasets have been proposed recently. Big Data mining is also related to the management of cloud and modern HPC clusters. Quality assurance in Big Data mining in such systems is the important research and engineering challenge in today's data intensive computing. Quality in Big Data systems can be directly related to the quality of data - poor quality data is predominant in many such systems. The velocity of Big Data directly refers to data quality problems. On the other hand, Big Data processing and analytics requires high quality services and resource and data management tools.

In this thematic track, we expect new concepts and research results addressing all quality issues in Big Data Systems. Suggested topics of interest include, but are not restricted to:

  • Quality in Big Data Fusion and Integration
  • Big Data Quality Management
  • Big Data Quality Metrics
  • Big Data management across distributed databases and datacentres
  • Algorithms and Approaches for Detecting Outliers, Duplicate Data, and Inconsistent Data
  • Security aspects in Big Data Processing and Analytics
  • Algorithms and Approaches for Big Data Healing
  • Big Data Persistence and Preservation
  • Efficiency versus Accuracy Trade-off
  • Data Quality in Distributed and Streaming Analytics
  • Big Data Quality in cloud systems
  • Big Data Quality in monitoring the e-health and human behaviour

Thematic Track Committee

Track Chairs:

  • Joanna Kolodziej – Cracow University of Technology, Poland
  • Sabri Pllana - Linnaeus University, Sweden

Track PC Members:

  • Rocco Aversa - Second University of Naples, Italy
  • Sanja Brdar – BioSense Institute, Serbia
  • Luis Correia – University of Lisbon, Portugal
  • Alejandro Fernandez-Montes – University of Sevilla, Spain
  • Daniel Grzonka – Cracow University of Technology, Poland
  • Mauro Iacono – Second University of Naples, Italy
  • Agnieszka Jakóbik – Cracow University of Technology, Poland
  • Zuzana Kominkova-Oplatkova, Toomas Bata University in Zlin, Czech Republic
  • Jose Manuel Molina Lopez – Universidad Carlos III de Madrid, Spain
  • Valentina Nejkovic - University of Nis, Serbia
  • Ana Respicio – University of Lisbon, Portugal
  • Dragan H. Stojanovic - University of Nis, Serbia

Joanna Kołodziej

Joanna Kołodziej is the Professor in Research and Academic Computer Network (NASK) Institute. She is also the Head of the Department of Computer Sciences at Cracow University of Technology. Prof. Kolodziej serves as the President of the Polish Chapter of IEEE Computational Intelligence Society. She participated in several international and national projects including ECONET, 7FP and PARAPHRASE 7FP Grants. Currently, she is the Chair of the cHiPSet Cost Action IC1406 (

Sabri Pllana

Sabri Pllana is currently an Associate Professor at the Dept. of Computer Science of the Linnaeus University in Sweden. He has published over 70 peer-reviewed scientific papers. His research interests include heterogeneous parallel computing systems, and cognitive computing techniques for learning parallel programming. He contributed to several EU-funded projects and he coordinated the FP7 project PEPPHER. He is associate editor of Computing journal (Springer), and a Senior Member of the IEEE.