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.
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.
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.
