Towards automatic detection and prioritization of pre-logging overhead: a case study of hadoop ecosystem

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Chen Zhi ◽  
Shuiguang Deng ◽  
Junxiao Han ◽  
Jianwei Yin
Keyword(s):  
Automatic Detection ◽  
Hadoop Ecosystem

scholarly journals Behavioral Patterns for Automatic Detection of Learning Styles in Learning Management Systems: a Case Study

2015 ◽  
Vol 106 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Guillermo Salazar Lugo ◽  
Luis-Felipe Rodríguez ◽  
Ramona Imelda García López ◽  
Adrián Macías Estrada ◽  
Moisés Rodríguez Echeverría
Keyword(s):  
Learning Styles ◽  
Automatic Detection ◽  
Learning Management Systems ◽  
Management Systems ◽  
Behavioral Patterns ◽  
Learning Management

scholarly journals Method for automatic detection of defective ultrasound linear array transducers based on uniformity assessment of clinical images - A case study

2018 ◽  
Vol 19 (2) ◽  
pp. 265-274
Author(s):  
Robert Lorentsson ◽  
Nasser Hosseini ◽  
Jan-Olof Johansson ◽  
Wiebke Rosenberg ◽  
Benny Stenborg ◽  
...  
Keyword(s):  
Linear Array ◽  
Automatic Detection ◽  
Clinical Images

scholarly journals Utilisation of Sentinel-1 SAR Imagery for Oil Spill Mapping: A Case Study of Balikpapan Bay Oil Spill

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Ratna Prastyani ◽  
Abdul Basith
Keyword(s):  
Cost Effectiveness ◽  
Oil Spill ◽  
Marine Ecosystem ◽  
Automatic Detection ◽  
Automatic Method ◽  
Public Concern ◽  
State Of Emergency ◽  
The World ◽  
Sar Imagery

Oil spill is a great threat for marine ecosystem. Oil discharge has become a public concern in all over the world with the increase of marine traffics. Indonesian government has declared state of emergency over oil spill which devastated Balikpapan bay in March 2018. The deadly oil spill impacts marine ecosystem as well as the communities. This study aims to map the oil spill over Balikpapan bay by utilising SAR imagery of Sentinel-1. Automatic and semi-automatic approaches are presented in this study to detect the oil spill. Oil spill is automatically detected using Oil Spill Detection toolbox of Sentinel Application Platform (SNAP). Furthermore, semi-automatic method is also demonstrated by utilising pre and post-oil spill SAR imageries. Our experiment shows that semi-automatic method has better performance than automatic detection by SNAP. Nevertheless, both approaches are useful to map oil spill in term of time and cost effectiveness.

Sign in / Sign up

Export Citation Format

Share Document