This proceedings volume contains selected papers from the Fourth International Conference on Big Data Applications and Services (BigDAS 2017), held in Tashkent, Uzbekistan on August 15-18, 2017. Big data has become a core technology providing innovative solutions in many fields including social media, healthcare and manufacturing. The Fourth International Conference on Big Data Applications and Services (BigDAS 2017) presented innovative results, encouraged academic and industrial interaction, and promoted collaborative research in the field of big data worldwide. The conference was organized by the Korea Big Data Services Society and National University of Uzbekistan.
Table of Contents:
Chapter 1. 3D volume visualization system based on GPUs for medical big data.- Chapter 2. A fire frame simulation scheme with massively parallel processing.- Chapter 3. A framework for calculating damages of personal information leakage accidents.- Chapter 4. A hierarchical structure for representing 3D respiration organ models.- Chapter 5. An effective method for detecting outlying regions in a 2-dimensional array.- Chapter 6. An effective recall-oriented information retrieval system evaluation.- Chapter 7. Chentry: Automated Evaluation of Students’ Learning Progress for Entry Education Software.- Chapter 8. Constrained big data mining in an edge computing environment.- Chapter 9. Constrained frequent pattern mining from big data via crowdsourcing.- Chapter 10. Data-driven prediction of ship destinations in the harbor area using deep learning.- Chapter 11. Design and implementation of a sunshine duration calculation system with massively parallel processing.- Chapter 12. Developing3D annotation features for 3D digital textbooks.- Chapter 13. Efficient mining of time interval-based association rules.- Chapter 14. Investigating the role of musical genre in human perception of music stretching resistance.- Chapter 15. Keyword-based metadata modeling for experimental omics data dissemination.- Chapter 16. Non-linear time-series mining of social influence.- Chapter 17. PEGASEF: A provenance-based big data service framework for efficient simulation execution on shared computing clusters.- Chapter 18. Preference-aware music recommendation using song lyrics.- Chapter 19. Real time smart safe-return-home service based on big data analytics.- Index.
About the Author :
Wookey Lee received the B.S., M.S., and Ph.D. from Seoul National University, Korea, and the M.S.E. degree from Carnegie Mellon University, USA. He currently is a Professor in Inha University, Korea. He has served as PC member and chair for many conferences such as CIKM, IEEE DEST, DASFAA, ICDE, VLDB, etc, and also as executive committee of IEEE TCDE. He won the best paper awards in IEEE DESC, ACM BigDas, KORMS and KIISE. He is the EIC of Journal of Information Technology and Architecture and the Big Data Service Journal, and an associate editor for WWW Journal. His research interests include Graph DB, Patent Information, Privacy, and Data Warehousing.
Carson K. Leung received his B.Sc.(Hons.), M.Sc., and Ph.D. from The University of British Columbia, Vancouver, Canada. He is currently a Professor at the University of Manitoba, Canada. He is also a Senior Member of the ACM and the IEEE. He has published more than 190 papers on the topics of his research interests, which include big data mining and analysis (including data analytics, data science & business intelligence solutions), databases, data management, data warehousing, data visualization and visual analytics, health and bioinformatics, Web technology and services, as well as social computing and social network analysis. He has also served as a PC member and an Organizing Committee member of many conferences such as ACM CIKM, ACM SIGMOD, IIEEE/ACM ASONAM, EEE DSAA, and IEEE ICDM.
