A Survey of Sensors in Healthcare Workflow Monitoring

2018 ◽  
Vol 51 (2) ◽  
pp. 1-37 ◽  
Author(s):  
Rodolfo S. Antunes ◽  
Lucas A. Seewald ◽  
Vinicius F. Rodrigues ◽  
Cristiano A. Da Costa ◽  
Luiz Gonzaga Jr. ◽  
...  
Keyword(s):  
Workflow Monitoring

scholarly journals Automatic Workflow Monitoring in Industrial Environments

2011 ◽  
pp. 200-213 ◽  
Author(s):  
Galina Veres ◽  
Helmut Grabner ◽  
Lee Middleton ◽  
Luc Van Gool
Keyword(s):  
Workflow Monitoring ◽  
Industrial Environments

Multi-agent collaboration for B2B workflow monitoring

2002 ◽  
Vol 15 (8) ◽  
pp. 485-491 ◽  
Author(s):  
Dongming Xu ◽  
Huaiqing Wang
Keyword(s):  
Agent Collaboration ◽  
Workflow Monitoring ◽  
Multi Agent

MO-D-213-01: Workflow Monitoring for a High Volume Radiation Oncology Center

2015 ◽  
Vol 42 (6Part27) ◽  
pp. 3553-3553
Author(s):  
S Laub ◽  
M Dunn ◽  
G Galbreath ◽  
S Gans ◽  
M Pankuch
Keyword(s):  
Radiation Oncology ◽  
High Volume ◽  
Workflow Monitoring ◽  
Oncology Center

scholarly journals On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis
Keyword(s):  
Visual Recognition ◽  
Clustering Algorithm ◽  
Sample Selection ◽  
Semisupervised Learning ◽  
Learning Approaches ◽  
Training Set ◽  
Learning Framework ◽  
Workflow Monitoring ◽  
Defect Recognition ◽  
The Impact

One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework. In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.

The design of intelligent workflow monitoring with agent technology

2005 ◽  
Vol 18 (6) ◽  
pp. 257-266 ◽  
Author(s):  
Minhong Wang ◽  
Huaiqing Wang ◽  
Dongming Xu
Keyword(s):  
Agent Technology ◽  
Workflow Monitoring

A Case Study into Using Common Real-Time Workflow Monitoring Infrastructure for Scientific Workflows

2013 ◽  
Vol 11 (3) ◽  
pp. 381-406 ◽  
Author(s):  
Karan Vahi ◽  
Ian Harvey ◽  
Taghrid Samak ◽  
Daniel Gunter ◽  
Kieran Evans ◽  
...  
Keyword(s):  
Real Time ◽  
Scientific Workflows ◽  
Workflow Monitoring

MONITORING AND ANALYSIS CONCERNS IN WORKFLOW APPLICATIONS: FROM CONCEPTUAL SPECIFICATIONS TO CONCRETE IMPLEMENTATIONS

2011 ◽  
Vol 20 (04) ◽  
pp. 371-404 ◽  
Author(s):  
OSCAR GONZÁLEZ ◽  
RUBBY CASALLAS ◽  
DIRK DERIDDER
Keyword(s):  
Domain Specific Language ◽  
Workflow Technology ◽  
Specific Language ◽  
Domain Specific ◽  
Level Of Abstraction ◽  
Workflow Monitoring ◽  
Workflow Application

Workflow monitoring and analysis concerns aim at identifying potential improvements of workflow applications. This paper presents an approach to specify and implement monitoring and analysis concerns on workflow applications raising the level of abstraction for workflow analysts. First, the specification of monitoring and analysis concerns is declared in a technology-independent way with a domain-specific language named MonitA. MonitA makes extensive use of the data available in the workflow application and its constituents to enhance the monitoring and analysis specifications. Second, we defined and implemented a strategy to assist developers to enhance a given workflow technology to support the generation of the monitoring and analysis code and its composition with the workflow application. This instrumentation-based approach enables the monitoring and analysis of workflow applications during their operational execution. We illustrate the flexibility of our approach by targeting different workflow platforms and different workflow applications.

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