scholarly journals Using Machine Learning techniques for Data Quality Monitoring in CMS and ALICE experiments

2019 ◽  
Author(s):  
Kamil Rafal Deja ◽  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2019 ◽  
pp. 469-487
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

scholarly journals Data-driven quality monitoring of bending processes in hairpin stator production using machine learning techniques

Procedia CIRP ◽  
2021 ◽  
Vol 103 ◽  
pp. 256-261
Author(s):  
Andreas Mayr ◽  
Philipp Röll ◽  
Daniel Winkle ◽  
Maximilian Enzmann ◽  
Benjamin Bickel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Monitoring ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Learning Techniques

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2017 ◽  
pp. 47-64
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

Air Quality Monitoring Intelligent System Using Machine Learning Techniques

2018 ◽  
Author(s):  
Paul D. Rosero-Montalvo ◽  
Jorge A. Caraguay-Procel ◽  
Edgar D. Jaramillo ◽  
Jaime M. Michilena-Calderon ◽  
Ana C. Umaquinga-Criollo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Intelligent System ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Air Quality Monitoring ◽  
Learning Techniques
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