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Papers by Juan Sebastián Rojas

Research paper thumbnail of Mejorando los sistemas rurales de alertas tempranas a través de la integración de OpenBTS y jain slee

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.Actualmente existe una tendencia que combina las características de los servicios We...

Research paper thumbnail of Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation

IEEE Access, 2020

Data caps and service degradation are techniques used to control subscribers' data consumption. T... more Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing realworld users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in missioncritical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter.

Research paper thumbnail of Consumption Behavior Analysis of Over The Top Services: Incremental Learning or Traditional Methods?

IEEE Access, 2019

Network monitoring and analysis of consumption behavior are important aspects for network operato... more Network monitoring and analysis of consumption behavior are important aspects for network operators. The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over The Top applications are known by their large consumption of network resources. Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences. With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning. However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time. In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users' Over The Top consumption behavior. Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way. After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.

Research paper thumbnail of Improving Rural Early Warning Systems through the Integration of OpenBTS and jain slee

Revista Ingenierías Universidad de Medellín, 2017

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.

Research paper thumbnail of Validation of Coffee Rust Warnings Based on Complex Event Processing

Lecture Notes in Computer Science, 2016

The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations... more The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations, the damage leads to a yield reduction of 30 % and 35 % respectively in regions where the meteorological conditions are propitious to the disease. Recently, researchers have focused on detecting the coffee rust disease starting from climate monitoring and parameters of crop control; however most of the monitoring systems lack the ability to process multiple source information and analyse it in order to identify abnormal situations and validate the generated warnings. In this paper, we propose a CEP engine and a prediction system integration for early warning systems applied to the coffee rust detection, capable of analysing multiple incoming events from the monitoring system and validating the warnings detection; evaluating an experimental prototype in a field test with satisfactory results.

Research paper thumbnail of Validation of Coffee Rust Warnings Based on Complex Event Processing

The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations... more The rust is the main coffee crop disease in the world. In the Colombian
and Brazilian plantations, the damage leads to a yield reduction of 30 % and 35 % respectively in regions where the meteorological conditions are propitious to the disease. Recently, researchers have focused on detecting the coffee rust disease starting from climate monitoring and parameters of crop control; however most of the monitoring systems lack the ability to process multiple source information and analyse it in order to identify abnormal situations and validate the generated
warnings. In this paper, we propose a CEP engine and a prediction system integration for early warning systems applied to the coffee rust detection, capable of analysing multiple incoming events from the monitoring system and validating the warnings detection; evaluating an experimental prototype in a field test with satisfactory results.

Volúmen 16, Número 30 (2017) by Juan Sebastián Rojas

Research paper thumbnail of Improving Rural Early Warning Systems through the Integration of OpenBTS and Jain Slee

Improving Rural Early Warning Systems through the Integration of OpenBTS and Jain Slee, 2017

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.

Research paper thumbnail of Mejorando los sistemas rurales de alertas tempranas a través de la integración de OpenBTS y jain slee

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.Actualmente existe una tendencia que combina las características de los servicios We...

Research paper thumbnail of Smart User Consumption Profiling: Incremental Learning-Based OTT Service Degradation

IEEE Access, 2020

Data caps and service degradation are techniques used to control subscribers' data consumption. T... more Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing realworld users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in missioncritical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter.

Research paper thumbnail of Consumption Behavior Analysis of Over The Top Services: Incremental Learning or Traditional Methods?

IEEE Access, 2019

Network monitoring and analysis of consumption behavior are important aspects for network operato... more Network monitoring and analysis of consumption behavior are important aspects for network operators. The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over The Top applications are known by their large consumption of network resources. Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences. With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning. However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time. In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users' Over The Top consumption behavior. Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way. After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.

Research paper thumbnail of Improving Rural Early Warning Systems through the Integration of OpenBTS and jain slee

Revista Ingenierías Universidad de Medellín, 2017

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.

Research paper thumbnail of Validation of Coffee Rust Warnings Based on Complex Event Processing

Lecture Notes in Computer Science, 2016

The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations... more The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations, the damage leads to a yield reduction of 30 % and 35 % respectively in regions where the meteorological conditions are propitious to the disease. Recently, researchers have focused on detecting the coffee rust disease starting from climate monitoring and parameters of crop control; however most of the monitoring systems lack the ability to process multiple source information and analyse it in order to identify abnormal situations and validate the generated warnings. In this paper, we propose a CEP engine and a prediction system integration for early warning systems applied to the coffee rust detection, capable of analysing multiple incoming events from the monitoring system and validating the warnings detection; evaluating an experimental prototype in a field test with satisfactory results.

Research paper thumbnail of Validation of Coffee Rust Warnings Based on Complex Event Processing

The rust is the main coffee crop disease in the world. In the Colombian and Brazilian plantations... more The rust is the main coffee crop disease in the world. In the Colombian
and Brazilian plantations, the damage leads to a yield reduction of 30 % and 35 % respectively in regions where the meteorological conditions are propitious to the disease. Recently, researchers have focused on detecting the coffee rust disease starting from climate monitoring and parameters of crop control; however most of the monitoring systems lack the ability to process multiple source information and analyse it in order to identify abnormal situations and validate the generated
warnings. In this paper, we propose a CEP engine and a prediction system integration for early warning systems applied to the coffee rust detection, capable of analysing multiple incoming events from the monitoring system and validating the warnings detection; evaluating an experimental prototype in a field test with satisfactory results.

Research paper thumbnail of Improving Rural Early Warning Systems through the Integration of OpenBTS and Jain Slee

Improving Rural Early Warning Systems through the Integration of OpenBTS and Jain Slee, 2017

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications ser... more Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.