http://sedici.unlp.edu.ar:80/handle/10915/140629 2026-10-14T04:53:09Z 2026-10-14T04:53:09Z Rodríguez, Mariela E. Boixader, Francesc Wong, Alvaro Rexachs del Rosario, Dolores Luque, Emilio http://sedici.unlp.edu.ar:80/handle/10915/140665 2022-08-18T20:01:19Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Simulation, in health services, has become an important tool that has made it possible to replicate real scenarios that have a critical degree and thus have reliable information to obtain knowledge about the variables managed in an emergency. In the activity of a Hospital Emergency Service (HED), we can find active agents such as doctors, nurses, patients and passive ones such as medical rooms, beds, among others, interacting between them. HEDs have been overwhelmed by catastrophic events, such as those induced by COVID 19, in the last two years. In these situations, the availability of resources, due to the considerable reduction in health personnel and the excessive arrival of patients for care, implies that the emergency service has become inoperative. The recovery of this essential service is fundamental and a priority. For this reason, it is necessary to analyze the period of time for departments, after a catastrophe, to reach a stable state that allows them to adapt to the new workload, that is, to analyze how resilient the ED is. The present work shows the proposal of a research project for the modeling and simulation of an ED that supports the design and evaluation of a resilient service considering the infrastructures, resources and services that occur in an ED. The model must consider the organization and infrastructure capacity to support and recover from extreme situations, in order to provide and maintain its operation and services in an adequate manner. To achieve this objective, the normal service index and the ability to resist, restore and evolve in the face of future risks and threats will be measured. The design of the model will be integrated and participatory, and it will focus on people and communities, governed by the principles of sustainable development goals regarding Health and Wellbeing, Gender Equality, Industry, Innovation and Infrastructure.

2022-01-01T00:00:00Z Simulation, in health services, has become an important tool that has made it possible to replicate real scenarios that have a critical degree and thus have reliable information to obtain knowledge about the variables managed in an emergency. In the activity of a Hospital Emergency Service (HED), we can find active agents such as doctors, nurses, patients and passive ones such as medical rooms, beds, among others, interacting between them. HEDs have been overwhelmed by catastrophic events, such as those induced by COVID 19, in the last two years. In these situations, the availability of resources, due to the considerable reduction in health personnel and the excessive arrival of patients for care, implies that the emergency service has become inoperative. The recovery of this essential service is fundamental and a priority. For this reason, it is necessary to analyze the period of time for departments, after a catastrophe, to reach a stable state that allows them to adapt to the new workload, that is, to analyze how resilient the ED is. The present work shows the proposal of a research project for the modeling and simulation of an ED that supports the design and evaluation of a resilient service considering the infrastructures, resources and services that occur in an ED. The model must consider the organization and infrastructure capacity to support and recover from extreme situations, in order to provide and maintain its operation and services in an adequate manner. To achieve this objective, the normal service index and the ability to resist, restore and evolve in the face of future risks and threats will be measured. The design of the model will be integrated and participatory, and it will focus on people and communities, governed by the principles of sustainable development goals regarding Health and Wellbeing, Gender Equality, Industry, Innovation and Infrastructure. González Cuevas, Antonio Suppi, Remo http://sedici.unlp.edu.ar:80/handle/10915/140664 2022-08-18T20:01:22Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics The objectives of this research is to solve, through high-performance simulation, different mobility problems in cities with mixed traffic (public and private transport, emergencies, bicycles, scooters). To achieve these objectives, the research is oriented to develop new ABM models (Agent Based Models), integrated with Geographic Information Systems (GIS) in order to provide answers to different situations and mobility scenarios in big cities. We want to analyze different episodes and strategies (real or possible) to solve questions based on 'What if?' with variables such as mixed traffic, bicycles exclusive lanes, reduction of private vehicles lanes, among others. This work shows the first phase of ongoing research focused on the city of Barcelona, but the proposed methodology and models can be quickly extended to other cities. For this, we work with data captured in real traffic analysis processed with vehicle recognition algorithms based on AI (artificial intelligence) and the results are used as the basis for the ABM model based on GIS. The simulation is carried out by integrating initial real data with those generated by the simulation in order to analyze the mobility and their interactions.

2022-01-01T00:00:00Z The objectives of this research is to solve, through high-performance simulation, different mobility problems in cities with mixed traffic (public and private transport, emergencies, bicycles, scooters). To achieve these objectives, the research is oriented to develop new ABM models (Agent Based Models), integrated with Geographic Information Systems (GIS) in order to provide answers to different situations and mobility scenarios in big cities. We want to analyze different episodes and strategies (real or possible) to solve questions based on 'What if?' with variables such as mixed traffic, bicycles exclusive lanes, reduction of private vehicles lanes, among others. This work shows the first phase of ongoing research focused on the city of Barcelona, but the proposed methodology and models can be quickly extended to other cities. For this, we work with data captured in real traffic analysis processed with vehicle recognition algorithms based on AI (artificial intelligence) and the results are used as the basis for the ABM model based on GIS. The simulation is carried out by integrating initial real data with those generated by the simulation in order to analyze the mobility and their interactions. Corbalán, Leonardo César Fernández Sosa, Juan Francisco Tesone, Fernando Dapoto, Sebastián H. Cristina, Federico Thomas, Pablo Javier Pesado, Patricia Mabel http://sedici.unlp.edu.ar:80/handle/10915/140662 2022-08-18T20:01:25Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Applications for mobile devices tend to face a greater number of adverse situations derived from the very nature of mobility. In this context, improving resilience becomes relevant. Here we present some recommendations, based on preliminary studies, to increase resilience to the performance drop and power consumption increase caused by increased computational load.

2022-01-01T00:00:00Z Applications for mobile devices tend to face a greater number of adverse situations derived from the very nature of mobility. In this context, improving resilience becomes relevant. Here we present some recommendations, based on preliminary studies, to increase resilience to the performance drop and power consumption increase caused by increased computational load. Cañibano, Rodrigo S. Castagno, Santino Conchillo, Mariano Chiarotto, Guillermo Rozas, Claudia Zanellato, Claudio Orlandi, Cristina Balladini, Javier http://sedici.unlp.edu.ar:80/handle/10915/140661 2022-08-18T20:01:28Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Different mobile applications and smart systems are being developed to increase users’ wellness and happiness. Unfortunately, some of the most recent technological advances in the field of affective computing, Internet of things or service computing have not yet been included in these solutions. In this paper, we briefly present a smart system that analyses the user’s emotions during her/his diary activities and configures mood regulation experiences when she/he comes back at home. These emotion-aware experiences are based on the Spotify music services and are personalised for each particular user considering her/his musical tastes and preferences. Besides, the system integrates an emotion recognition system based on wearables and artificial intelligence techniques. The recognised emotions are then used to determine the user’s mood and to make decisions on the music interventions to be carried out.

2022-01-01T00:00:00Z Different mobile applications and smart systems are being developed to increase users’ wellness and happiness. Unfortunately, some of the most recent technological advances in the field of affective computing, Internet of things or service computing have not yet been included in these solutions. In this paper, we briefly present a smart system that analyses the user’s emotions during her/his diary activities and configures mood regulation experiences when she/he comes back at home. These emotion-aware experiences are based on the Spotify music services and are personalised for each particular user considering her/his musical tastes and preferences. Besides, the system integrates an emotion recognition system based on wearables and artificial intelligence techniques. The recognised emotions are then used to determine the user’s mood and to make decisions on the music interventions to be carried out. Álvarez, P. García de Quirós, J. Baldassarri, S. http://sedici.unlp.edu.ar:80/handle/10915/140659 2022-08-18T20:01:32Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Different mobile applications and smart systems are being developed to increase users’ wellness and happiness. Unfortunately, some of the most recent technological advances in the field of affective computing, Internet of things or service computing have not yet been included in these solutions. In this paper, we briefly present a smart system that analyses the user’s emotions during her/his diary activities and configures mood regulation experiences when she/he comes back at home. These emotion-aware experiences are based on the Spotify music services and are personalised for each particular user considering her/his musical tastes and preferences. Besides, the system integrates an emotion recognition system based on wearables and artificial intelligence techniques. The recognised emotions are then used to determine the user’s mood and to make decisions on the music interventions to be carried out.

2022-01-01T00:00:00Z Different mobile applications and smart systems are being developed to increase users’ wellness and happiness. Unfortunately, some of the most recent technological advances in the field of affective computing, Internet of things or service computing have not yet been included in these solutions. In this paper, we briefly present a smart system that analyses the user’s emotions during her/his diary activities and configures mood regulation experiences when she/he comes back at home. These emotion-aware experiences are based on the Spotify music services and are personalised for each particular user considering her/his musical tastes and preferences. Besides, the system integrates an emotion recognition system based on wearables and artificial intelligence techniques. The recognised emotions are then used to determine the user’s mood and to make decisions on the music interventions to be carried out. Orellana, Marcos Jiménez Sarango, Ángel Alberto Zambrano Martínez, Jorge Luis http://sedici.unlp.edu.ar:80/handle/10915/140657 2022-08-18T20:01:33Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics In recent years, Automatic Speech Recognition (ASR) services have performed notable progress in the research efforts of big companies such as Google and Amazon. However, the ASRs are still sensitive to the audio processing quality in other languages. To solve this issue, various speech enhancement algorithms that are the most prominent in improving speech intelligibility were proposed, such as Singular Value Decomposition (SVD), log Minimum Mean Square Error (log-MMSE) and Wiener. By preprocessing the audio files with these algorithms, we seek to reduce the Word Error Rate (WER), which compares the transcription performed by the ASR against a manual transcription. Thus, we can determine the percentage of error that the ASR service has acquired. Results demonstrated that Google is more efficient than Amazon and Vosk counterparts. Also, we decided that applying a Low-pass filter combined with a log-MMSE algorithm to the audio files can substantially reduce the WER percentage of transcription depending on the noise characteristics contained in the audio.

2022-01-01T00:00:00Z In recent years, Automatic Speech Recognition (ASR) services have performed notable progress in the research efforts of big companies such as Google and Amazon. However, the ASRs are still sensitive to the audio processing quality in other languages. To solve this issue, various speech enhancement algorithms that are the most prominent in improving speech intelligibility were proposed, such as Singular Value Decomposition (SVD), log Minimum Mean Square Error (log-MMSE) and Wiener. By preprocessing the audio files with these algorithms, we seek to reduce the Word Error Rate (WER), which compares the transcription performed by the ASR against a manual transcription. Thus, we can determine the percentage of error that the ASR service has acquired. Results demonstrated that Google is more efficient than Amazon and Vosk counterparts. Also, we decided that applying a Low-pass filter combined with a log-MMSE algorithm to the audio files can substantially reduce the WER percentage of transcription depending on the noise characteristics contained in the audio. Walas Mateo, Federico Redchuk, Andrés http://sedici.unlp.edu.ar:80/handle/10915/140656 2022-08-18T20:01:34Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Traditional industries like steelmaking, are in the spotlight for the need of improving processes towards net zero emissions. This article presents a case on a new business model to ease the adoption of Machine Learning (ML) to optimize industrial processes, applied to a blast furnace at a steel company. The focus of the paper is to illustrate the way a ML platform with a Low Code solution approach can give results in two months to optimize a production process at a steel mill. The methodology used in the case allows obtaining a data model to be validated in less time than conventional approaches. This work pretends to give more light to the use of industrial data and the way traditional industries can evolve towards the industry 4.0 paradigm. The adoption of the low code solution is based on lean startup methodology. The cycle to obtain valid results includes the involvement of people from the process as well as analytics experts. At the end it can be seen that the solution contribute to improve Operational Equipment Effectiveness (OEE) and lower energy consumption. Besides process operators became empowered with the predictions that give the platform.

2022-01-01T00:00:00Z Traditional industries like steelmaking, are in the spotlight for the need of improving processes towards net zero emissions. This article presents a case on a new business model to ease the adoption of Machine Learning (ML) to optimize industrial processes, applied to a blast furnace at a steel company. The focus of the paper is to illustrate the way a ML platform with a Low Code solution approach can give results in two months to optimize a production process at a steel mill. The methodology used in the case allows obtaining a data model to be validated in less time than conventional approaches. This work pretends to give more light to the use of industrial data and the way traditional industries can evolve towards the industry 4.0 paradigm. The adoption of the low code solution is based on lean startup methodology. The cycle to obtain valid results includes the involvement of people from the process as well as analytics experts. At the end it can be seen that the solution contribute to improve Operational Equipment Effectiveness (OEE) and lower energy consumption. Besides process operators became empowered with the predictions that give the platform. Alfaro Peral, Yolanda Concepción, Eugenio López-Samaniego, Izaskun Zarza, Gonzalo http://sedici.unlp.edu.ar:80/handle/10915/140655 2022-08-18T20:01:35Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics This article addresses the banking interaction needs of the senior population, devoting special attention to the unique needs of this age demographic delineated by the natural cognitive decline caused by ageing. Despite being a compelling business audience, the financial sector, and particularly banking institutions, have significantly underestimated the importance of designing a customer experience that is better adapted to the needs of senior users. A compelling yet achievable solution to this problem is the adoption of AI-based technologies, such as dynamic user interfaces, biometric authentication methods, automatic recommendations, and experience-based APIs. The curated adoption of these tools will enable banks to better serve this increasingly growing population by creating seamless and respectful experiences for their senior customers and, above all, will provide them greater autonomy.

2022-01-01T00:00:00Z This article addresses the banking interaction needs of the senior population, devoting special attention to the unique needs of this age demographic delineated by the natural cognitive decline caused by ageing. Despite being a compelling business audience, the financial sector, and particularly banking institutions, have significantly underestimated the importance of designing a customer experience that is better adapted to the needs of senior users. A compelling yet achievable solution to this problem is the adoption of AI-based technologies, such as dynamic user interfaces, biometric authentication methods, automatic recommendations, and experience-based APIs. The curated adoption of these tools will enable banks to better serve this increasingly growing population by creating seamless and respectful experiences for their senior customers and, above all, will provide them greater autonomy. Estrebou, César Armando Saavedra, Marcos David Adra, Federico Fleming, Martín http://sedici.unlp.edu.ar:80/handle/10915/140652 2022-08-18T20:01:36Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics This paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers.

2022-01-01T00:00:00Z This paper describes the progress made in the context of a research and development project on machine learning techniques and algorithms applied to small microcontrollers. The beginning of the development of EmbedIA, a machine learning framework for microcontrollers, is presented. The experiments carried out comparing the proposed framework with other similar frameworks such as Tensorflow Lite Micro, μTensor and EloquentTinyML show an important advantage with respect to memory and inference time required by small microcontrollers. Vegega, Cinthia Pollo Cattaneo, María Florencia Olivas, José A. http://sedici.unlp.edu.ar:80/handle/10915/140650 2022-08-18T20:01:38Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics This paper presents a first literature overview on the nowadays use of Knowledge Engineering in the framework of the development of solutions in the field of Artificial Intelligence. With the assumption that the conceptualization, formalization and modeling of data is fundamental in this type of projects, it is considered that Knowledge Engineering can actively collaborate orienting and guiding part of these activities. In this context, a doctoral research line is proposed about the use of Knowledge Engineering to perform Intelligent Data Analysis. This line of research arises from the fact that existing methodologies in the field of Artificial Intelligence and Data Mining do not incorporate complete domain and context knowledge in data analysis. The incorporation of this knowledge allows contrasting the hypotheses obtained from the data and enriches the analysis reaching better results.

2022-01-01T00:00:00Z This paper presents a first literature overview on the nowadays use of Knowledge Engineering in the framework of the development of solutions in the field of Artificial Intelligence. With the assumption that the conceptualization, formalization and modeling of data is fundamental in this type of projects, it is considered that Knowledge Engineering can actively collaborate orienting and guiding part of these activities. In this context, a doctoral research line is proposed about the use of Knowledge Engineering to perform Intelligent Data Analysis. This line of research arises from the fact that existing methodologies in the field of Artificial Intelligence and Data Mining do not incorporate complete domain and context knowledge in data analysis. The incorporation of this knowledge allows contrasting the hypotheses obtained from the data and enriches the analysis reaching better results. Herrera, Claudia M. Chayle, Carolina I. Pauletto, Ana C. Barrera, María Alejandra Morales Apaza, Nadir http://sedici.unlp.edu.ar:80/handle/10915/140645 2022-08-18T20:01:40Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Data is the fuel of the 21st century. Increasingly our economies are driven by this input. Data has the potential to create social value, something that higher education institutions are beginning to rely on, as evidenced by the initiatives of public access to their data and the various advantages and benefits provided by big data in improving educational management, the development of customized curricula, monitoring the academic performance of students, as well as in the generation of digital repositories that are the product of years of academic, teaching and research activity. The present research aims to evaluate the scope of existing initiatives on the subject of Big Data and Open Data in education, in the Faculty of Technology and Applied Sciences of the National University of Catamarca (UNCA), producing an effective contribution with preliminary results for the research project called "ICT in the Service of Open Data: Current Situation, Conceptualization and Initiatives for Opening Public Information" which is being carried out in the Department of Informatics.

2022-01-01T00:00:00Z Data is the fuel of the 21st century. Increasingly our economies are driven by this input. Data has the potential to create social value, something that higher education institutions are beginning to rely on, as evidenced by the initiatives of public access to their data and the various advantages and benefits provided by big data in improving educational management, the development of customized curricula, monitoring the academic performance of students, as well as in the generation of digital repositories that are the product of years of academic, teaching and research activity. The present research aims to evaluate the scope of existing initiatives on the subject of Big Data and Open Data in education, in the Faculty of Technology and Applied Sciences of the National University of Catamarca (UNCA), producing an effective contribution with preliminary results for the research project called "ICT in the Service of Open Data: Current Situation, Conceptualization and Initiatives for Opening Public Information" which is being carried out in the Department of Informatics. Nuñez, Gustavo Buckle, Carlos Zárate, Marcos http://sedici.unlp.edu.ar:80/handle/10915/140643 2022-08-18T20:01:41Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics The purpose of data exploration and data visualization (DV) is to offer ways of perceiving and manipulating information, as well as extracting and inferring knowledge. In this short paper we present advances in visual representations and intuitive interaction techniques based on artificial intelligence. This contributes significantly to the exploration and understanding of information related to marine sciences represented by ontologies and linked data. This preliminary research allows scientists and non-expert users to analyze sets with information related to oceanography, meteorology and environmental parameters to promote scientific knowledge and productive innovation in the South Atlantic ocean using Linked Open Data (LOD).

2022-01-01T00:00:00Z The purpose of data exploration and data visualization (DV) is to offer ways of perceiving and manipulating information, as well as extracting and inferring knowledge. In this short paper we present advances in visual representations and intuitive interaction techniques based on artificial intelligence. This contributes significantly to the exploration and understanding of information related to marine sciences represented by ontologies and linked data. This preliminary research allows scientists and non-expert users to analyze sets with information related to oceanography, meteorology and environmental parameters to promote scientific knowledge and productive innovation in the South Atlantic ocean using Linked Open Data (LOD). Morán, Marina Balladini, Javier Rexachs del Rosario, Dolores Rucci, Enzo http://sedici.unlp.edu.ar:80/handle/10915/140642 2022-08-18T20:01:42Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics Inquiring about different ways to reduce energy consumption during the execution of large-scale applications is essential to maintain and increase the enormous computing power achieved in HPC systems. Fault tolerance methods can have an impact on power consumption. In particular, rollback-recovery methods using uncoordinated checkpoints prevent all processes from re-executing in the event of a failure. In this context, it is possible to take actions on the nodes of the processes that do not re-execute to reduce energy consumption. In this work, we describe some issues to consider when we extend the application of energy-saving strategies beyond the nodes that communicate directly with the failed one.

2022-01-01T00:00:00Z Inquiring about different ways to reduce energy consumption during the execution of large-scale applications is essential to maintain and increase the enormous computing power achieved in HPC systems. Fault tolerance methods can have an impact on power consumption. In particular, rollback-recovery methods using uncoordinated checkpoints prevent all processes from re-executing in the event of a failure. In this context, it is possible to take actions on the nodes of the processes that do not re-execute to reduce energy consumption. In this work, we describe some issues to consider when we extend the application of energy-saving strategies beyond the nodes that communicate directly with the failed one. Guidet, Sebastián Hernández-García, Ruber Frati, Fernando Emmanuel Barrientos, Ricardo J. http://sedici.unlp.edu.ar:80/handle/10915/140636 2022-08-17T20:01:19Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics When searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm.

2022-01-01T00:00:00Z When searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm. Medina, Santiago Montezanti, Diego Miguel Gomez D'Orazio, Lucas Compagnucci, Evaristo De Giusti, Armando Eduardo Naiouf, Marcelo http://sedici.unlp.edu.ar:80/handle/10915/140632 2022-08-17T20:01:22Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics In recent years, Internet of Things (IoT) has become extremely popular because of its ability of sensing information from the environment and processing it in the cloud. Edge and Fog Computing are new paradigms that aim to localize some of the processing near the sensors, helping to cope with high communications latencies and bandwidth bottlenecks. As Wireless Sensor Networks (WSN) and Fog nodes are prone to failure, affecting system reliability and performance, the implementation of resilience strategies becomes essential to ensure reliable delivery of data and system availability during interruptions or in the presence of faults. In this article, we present three lines of research in progress: Redundant Image Processing, Integration of a WSN with an IoT Platform for Intelligent Control and Resilient Monitorization and Control of Robots. We aim to incorporate resilience mechanisms to platforms that integrate Edge, Fog and Cloud Computing, and to evaluate the proposed solutions in terms of the coverage achieved, processing and communication times and power consumption.

2022-01-01T00:00:00Z In recent years, Internet of Things (IoT) has become extremely popular because of its ability of sensing information from the environment and processing it in the cloud. Edge and Fog Computing are new paradigms that aim to localize some of the processing near the sensors, helping to cope with high communications latencies and bandwidth bottlenecks. As Wireless Sensor Networks (WSN) and Fog nodes are prone to failure, affecting system reliability and performance, the implementation of resilience strategies becomes essential to ensure reliable delivery of data and system availability during interruptions or in the presence of faults. In this article, we present three lines of research in progress: Redundant Image Processing, Integration of a WSN with an IoT Platform for Intelligent Control and Resilient Monitorization and Control of Robots. We aim to incorporate resilience mechanisms to platforms that integrate Edge, Fog and Cloud Computing, and to evaluate the proposed solutions in terms of the coverage achieved, processing and communication times and power consumption. Gibeaux, Soizic Pitti, Javier González, Carlos http://sedici.unlp.edu.ar:80/handle/10915/140631 2022-08-17T20:01:26Z 2022-01-01T00:00:00Z

Objeto de conferencia X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022); https://sedici.unlp.edu.ar/handle/10915/139373; Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics The agricultural activities need optimal soil and the agro-climatic conditions in the production areas such as Cerro Punta in Panama. Among the innovative technological alternatives, there are tools that can provide farmers with information describing the agro-meteorological conditions of some areas, such as precision agriculture. Due to the data range of wireless communication and low power capabilities, one of the most promising technologies for precision farming is WSN with integrated LoRa systems and energy self-sufficient. This study presents the methodology of the first LoRa WSN project destined to precision agriculture in Panama, and bring the preliminary results in the evaluation of LoRa signal, micro solar panel efficiency and environmental data. In the tropical vegetation of this part of the country, the range of LoRa communication is smaller than theoretically expected. The efficiency of the solar panels is enough to maintain the batteries always charged. Finally, slightly different microclimates have been highlighted between two close monitored areas.

2022-01-01T00:00:00Z The agricultural activities need optimal soil and the agro-climatic conditions in the production areas such as Cerro Punta in Panama. Among the innovative technological alternatives, there are tools that can provide farmers with information describing the agro-meteorological conditions of some areas, such as precision agriculture. Due to the data range of wireless communication and low power capabilities, one of the most promising technologies for precision farming is WSN with integrated LoRa systems and energy self-sufficient. This study presents the methodology of the first LoRa WSN project destined to precision agriculture in Panama, and bring the preliminary results in the evaluation of LoRa signal, micro solar panel efficiency and environmental data. In the tropical vegetation of this part of the country, the range of LoRa communication is smaller than theoretically expected. The efficiency of the solar panels is enough to maintain the batteries always charged. Finally, slightly different microclimates have been highlighted between two close monitored areas. Naiouf, Marcelo De Giusti, Armando Eduardo Chichizola, Franco De Giusti, Laura Cristina Rucci, Enzo http://sedici.unlp.edu.ar:80/handle/10915/139373 2023-07-07T17:42:47Z 2022-07-11T00:00:00Z

Libro Naiouf, Marcelo; De Giusti, Armando Eduardo; Chichizola, Franco; De Giusti, Laura Cristina; Rucci, Enzo Compilación de los short papers presentados en las 10mas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2022), llevadas a cabo en modalidad híbrida durante junio de 2021 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP, en colaboración con universidades de Argentina y del exterior. Facultad de Informática (UNLP)

2022-07-11T00:00:00Z Compilación de los short papers presentados en las 10mas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2022), llevadas a cabo en modalidad híbrida durante junio de 2021 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP, en colaboración con universidades de Argentina y del exterior.