scholarly journals Mediation of Lazy Update Propagation in a Replicated Database over a Decentralized P2P Architecture

2019 ◽  
pp. 1-31
Author(s):  
Katembo Kituta Ezéchiel ◽  
Shri Kant ◽  
Ruchi Agarwal
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Execution Time ◽  
Prediction Models ◽  
Data Migration ◽  
P2p Network ◽  
Update Propagation ◽  
Methodological Approaches ◽  
Network Overload ◽  
New Algorithms

While replicating data over a decentralized Peer-to- Peer (P2P) network, transactions broadcasting updates arising from different peers run simultaneously so that a destination peer replica can be updated concurrently, that always causes transaction and data conflicts. Moreover, during data migration, connectivity interruption and network overload corrupt running transactions so that destination peers can experience duplicated data or improper data or missing data, hence replicas remain inconsistent. Different methodological approaches have been combined to solve these problems: the audit log technique to capture the changes made to data; the algorithmic method to design and analyse algorithms and the statistical method to analyse the performance of new algorithms and to design prediction models of the execution time based on other parameters. A Graphical User Interface software as prototype, have been designed with C #, to implement these new algorithms to obtain a database synchronizer-mediator. A stream of experiments, showed that the new algorithms were effective. So, the hypothesis according to which “The execution time of replication and reconciliation transactions totally depends on independent factors.” has been confirmed.

scholarly journals The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes

2016 ◽  
Author(s):  
Luis M Rodriguez-R ◽  
Konstantinos T Konstantinidis
Keyword(s):  
User Interface ◽  
Case Studies ◽  
Graphical User Interface ◽  
Biological Research ◽  
Microbial Genomics ◽  
Online Analysis ◽  
Microbial Genomes ◽  
Novel Method ◽  
Over Dispersion ◽  
New Algorithms

Genomic and metagenomic analyses are increasingly becoming commonplace in several areas of biological research, but recurrent specialized analyses are frequently reported as in-house scripts rarely available after publication. We describe the enveomics collection, a growing set of actively maintained scripts for several recurrent and specialized tasks in microbial genomics and metagenomics, and present a graphical user interface and several case studies. Our resource includes previously described as well as new algorithms such as Transformed-space Resampling In Biased Sets (TRIBS), a novel method to evaluate phylogenetic under- or over-dispersion in reference sets with strong phylogenetic bias. The enveomics collection is freely available under the terms of the Artistic License 2.0 at https://github.com/lmrodriguezr/enveomics and for online analysis at http://enve-omics.ce.gatech.edu

scholarly journals The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes

2016 ◽  
Author(s):  
Luis M Rodriguez-R ◽  
Konstantinos T Konstantinidis
Keyword(s):  
User Interface ◽  
Case Studies ◽  
Graphical User Interface ◽  
Biological Research ◽  
Microbial Genomics ◽  
Online Analysis ◽  
Microbial Genomes ◽  
Novel Method ◽  
Over Dispersion ◽  
New Algorithms

Genomic and metagenomic analyses are increasingly becoming commonplace in several areas of biological research, but recurrent specialized analyses are frequently reported as in-house scripts rarely available after publication. We describe the enveomics collection, a growing set of actively maintained scripts for several recurrent and specialized tasks in microbial genomics and metagenomics, and present a graphical user interface and several case studies. Our resource includes previously described as well as new algorithms such as Transformed-space Resampling In Biased Sets (TRIBS), a novel method to evaluate phylogenetic under- or over-dispersion in reference sets with strong phylogenetic bias. The enveomics collection is freely available under the terms of the Artistic License 2.0 at https://github.com/lmrodriguezr/enveomics and for online analysis at http://enve-omics.ce.gatech.edu

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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