AOP-Based Approach for Local Data Management in Adaptive Interfaces

2016 ◽  
Author(s):  
Jiri Sebek ◽  
Tomas Cerny
Keyword(s):  
Data Management ◽  
Adaptive Interfaces ◽  
Local Data

scholarly journals Analysis, Deployment and Integration of Platforms for Fog Computing

2020 ◽  
Vol 20 (2) ◽  
pp. e12
Author(s):  
Joaquín De Antueno ◽  
Santiago Medina ◽  
Laura De Giusti ◽  
Armando De Giusti
Keyword(s):  
Data Management ◽  
Sensor Network ◽  
Experimental Work ◽  
Response Times ◽  
Fog Computing ◽  
Data Capture ◽  
Local Data ◽  
Iot Applications ◽  
Flow Of Information ◽  
Large Flow

In IoT applications, data capture in a sensor network can generate a large flow of information between the nodes and the cloud, affecting response times and device complexity but, above all, increasing costs. Fog computing refers to the use of pre-processing tools to improve local data management and communication with the cloud. This work presents an analysis of the features that platforms implementing fog computing solutions should have. Additionally, an experimental work integrating two specific platforms used for controlling devices in a sensor network, processing the generated data, and communicating with the cloud is presented.

Views on local data management in cancer clinical trials

1997 ◽  
Vol 9 (6) ◽  
pp. 403-406 ◽  
Author(s):  
J.M. Brown ◽  
S.A. Haining ◽  
J.M. Hale
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Cancer Clinical Trials ◽  
Local Data

05 Local data management in cancer clinical trials

1997 ◽  
Vol 18 (3) ◽  
pp. S43-S44
Author(s):  
Shona A. Haining ◽  
Janet M. Hale ◽  
Julia M. Brown
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Cancer Clinical Trials ◽  
Local Data

scholarly journals Evaluation of different data management scenarios for estimating daily reference evapotranspiration

2013 ◽  
Vol 44 (6) ◽  
pp. 1058-1070 ◽  
Author(s):  
Jalal Shiri ◽  
Ali Ashraf Sadraddini ◽  
Amir Hossein Nazemi ◽  
Ozgur Kisi ◽  
Pau Marti ◽  
...  
Keyword(s):  
Data Management ◽  
Fuzzy Inference ◽  
Reference Evapotranspiration ◽  
Gene Expression Programming ◽  
Local Data ◽  
Management Scenarios ◽  
Inference System ◽  
Pooled Data ◽  
Temperature Radiation ◽  
Better Than

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves–Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario.

The role of a central laboratory's local data management system in a multicenter study

1980 ◽  
Vol 1 (2) ◽  
pp. 172
Author(s):  
Patricia Moore ◽  
Kenneth W. Clark ◽  
Kathleen A Madden ◽  
Lewis J. Thomas
Keyword(s):  
Data Management ◽  
Multicenter Study ◽  
Management System ◽  
Data Management System ◽  
Local Data
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