Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640221
J. Merlino, P. Rodríguez-Bocca
Word embeddings are widely used in natural language processing (NLP) to group semantically similar words but have been applied in other areas to find semantic similarity between entities. In this paper we create a vector embedding for Internet Domain Names (DNS) using a corpus of real anonymized DNS log queries from a large Internet Service Provider (ISP). We then use this embedding as a layer of a recurrent neural network (RNN) that works as a Language Model for the DNS queries generated by the users. We show that this RNN can be used to predict the next DNS query generated by a user with good accuracy (considering the size of the problem). Moreover, we show that training the same RNN without using the pre-trained vector model takes more time and is substantially less accurate. The results presented in this work can have practical applications in many engineering activities related to DNS architecture design. For example, latency reduction in address resolution, optimization of cache systems in recursive DNS servers, automatic filtering of inappropriate domains, and detecting anomalies in traffic.
{"title":"Short-time prediction of DNS queries using deep learning and pre-trained word embedding","authors":"J. Merlino, P. Rodríguez-Bocca","doi":"10.1109/CLEI53233.2021.9640221","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640221","url":null,"abstract":"Word embeddings are widely used in natural language processing (NLP) to group semantically similar words but have been applied in other areas to find semantic similarity between entities. In this paper we create a vector embedding for Internet Domain Names (DNS) using a corpus of real anonymized DNS log queries from a large Internet Service Provider (ISP). We then use this embedding as a layer of a recurrent neural network (RNN) that works as a Language Model for the DNS queries generated by the users. We show that this RNN can be used to predict the next DNS query generated by a user with good accuracy (considering the size of the problem). Moreover, we show that training the same RNN without using the pre-trained vector model takes more time and is substantially less accurate. The results presented in this work can have practical applications in many engineering activities related to DNS architecture design. For example, latency reduction in address resolution, optimization of cache systems in recursive DNS servers, automatic filtering of inappropriate domains, and detecting anomalies in traffic.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"23 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89093413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640008
L. Ordínez, Ivana González Bagur, Marisa Castillo, Federico Amandi, Carlos Buckle
The aim of this work is to characterize and analyze the flow of arrivals to the city of Puerto Madryn (Patagonia - Argentina), during the first six month of social isolation while the pandemic for Covid-19 was occurring. Results remarked how simple traffic data collection based on provenance, local destination, age and gender is more important to understand the spread of the virus and for the design of health policies than reinforcing traffic restrictions. The intention is to expose the potentialities of a more comprehensive analogous analysis of this kind of mobility for local public health policy and urban planning in a post-pandemic context.
{"title":"A space-time analysis of Puerto Madryn arrivals during COVID-19","authors":"L. Ordínez, Ivana González Bagur, Marisa Castillo, Federico Amandi, Carlos Buckle","doi":"10.1109/CLEI53233.2021.9640008","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640008","url":null,"abstract":"The aim of this work is to characterize and analyze the flow of arrivals to the city of Puerto Madryn (Patagonia - Argentina), during the first six month of social isolation while the pandemic for Covid-19 was occurring. Results remarked how simple traffic data collection based on provenance, local destination, age and gender is more important to understand the spread of the virus and for the design of health policies than reinforcing traffic restrictions. The intention is to expose the potentialities of a more comprehensive analogous analysis of this kind of mobility for local public health policy and urban planning in a post-pandemic context.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"37 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85725381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640083
Solange Ramos-Cooper, Guillermo Cámara Chávez
Ear recognition has gained attention in recent years. The possibility of being captured from a distance, contactless, without the cooperation of the subject and not be affected by facial expressions makes ear recognition a captivating choice for surveillance and security applications, and even more in the current COVID-19 pandemic context where modalities like face recognition fail due to mouth and facial covering masks usage. Applying any deep learning (DL) algorithm usually demands a large amount of training data and appropriate network architectures, therefore we introduce a large-scale database and explore fine-tuning pre-trained convolutional neural networks (CNNs) looking for a robust representation of ear images taken under uncontrolled conditions. Taking advantage of the face recognition field, we built an ear dataset based on the VGGFace dataset and use the Mask-RCNN for ear detection. Besides, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained for general image recognition. Experiments on the UERC dataset have shown that fine-tuning from a face recognition model and using a larger dataset leads to a significant improvement of around 9% compared to state-of-the-art methods on the ear recognition field. In addition, we have explored score-level fusion by combining matching scores of the fine-tuning models which leads to an improvement of around 4% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics.
{"title":"Ear Recognition In The Wild with Convolutional Neural Networks","authors":"Solange Ramos-Cooper, Guillermo Cámara Chávez","doi":"10.1109/CLEI53233.2021.9640083","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640083","url":null,"abstract":"Ear recognition has gained attention in recent years. The possibility of being captured from a distance, contactless, without the cooperation of the subject and not be affected by facial expressions makes ear recognition a captivating choice for surveillance and security applications, and even more in the current COVID-19 pandemic context where modalities like face recognition fail due to mouth and facial covering masks usage. Applying any deep learning (DL) algorithm usually demands a large amount of training data and appropriate network architectures, therefore we introduce a large-scale database and explore fine-tuning pre-trained convolutional neural networks (CNNs) looking for a robust representation of ear images taken under uncontrolled conditions. Taking advantage of the face recognition field, we built an ear dataset based on the VGGFace dataset and use the Mask-RCNN for ear detection. Besides, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained for general image recognition. Experiments on the UERC dataset have shown that fine-tuning from a face recognition model and using a larger dataset leads to a significant improvement of around 9% compared to state-of-the-art methods on the ear recognition field. In addition, we have explored score-level fusion by combining matching scores of the fine-tuning models which leads to an improvement of around 4% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"29 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84383665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640086
Gabriela Pereyra, Claudina Rattaro, P. Belzarena
The fifth generation of mobile communications (5G) is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. In this context, as scheduling is always a delicate vendor topic and there are not so many free and complete simulation tools to support all 5G features, in this paper we present Py5cheSim. Py5cheSim is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network (RAN), one of the main innovations of 5G. The present work describes its design and implementation choices and the principal validation results. Finally, as another contribution, we present an exhaustive analysis of the existing available simulation tools highlighting the novelty of Py5cheSim comparing with the others existing simulation software for 5G.
{"title":"Py5cheSim: a 5G Multi-Slice Cell Capacity Simulator","authors":"Gabriela Pereyra, Claudina Rattaro, P. Belzarena","doi":"10.1109/CLEI53233.2021.9640086","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640086","url":null,"abstract":"The fifth generation of mobile communications (5G) is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. In this context, as scheduling is always a delicate vendor topic and there are not so many free and complete simulation tools to support all 5G features, in this paper we present Py5cheSim. Py5cheSim is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network (RAN), one of the main innovations of 5G. The present work describes its design and implementation choices and the principal validation results. Finally, as another contribution, we present an exhaustive analysis of the existing available simulation tools highlighting the novelty of Py5cheSim comparing with the others existing simulation software for 5G.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87572973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640171
D. Silva, W. Watanabe
When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.
{"title":"Cross-Browser Incompatibilities Classification Layout: A comparative study between different models","authors":"D. Silva, W. Watanabe","doi":"10.1109/CLEI53233.2021.9640171","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640171","url":null,"abstract":"When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"34 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86423589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639977
Rafael Alfaro-Flores, José Salas-Bonilla, Loic Juillard, Juan Esquivel-Rodríguez
We present an end-to-end experimentation framework to improve the human annotation of data sets used in the training process of Machine Learning models. It covers the instrumentation of the annotation tool, the aggregation of metrics that highlight usage patterns and hypothesis-testing tools that enable the comparison of experimental groups, to decide whether improvements in the annotation process significantly impact the overall results. We show the potential of the protocol using two real-life annotation use cases.
{"title":"Experiment-driven improvements in Human-in-the-loop Machine Learning Annotation via significance-based A/B testing","authors":"Rafael Alfaro-Flores, José Salas-Bonilla, Loic Juillard, Juan Esquivel-Rodríguez","doi":"10.1109/CLEI53233.2021.9639977","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639977","url":null,"abstract":"We present an end-to-end experimentation framework to improve the human annotation of data sets used in the training process of Machine Learning models. It covers the instrumentation of the annotation tool, the aggregation of metrics that highlight usage patterns and hypothesis-testing tools that enable the comparison of experimental groups, to decide whether improvements in the annotation process significantly impact the overall results. We show the potential of the protocol using two real-life annotation use cases.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"74 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79953129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640100
Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez
Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia's Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.
从视频监控图像中自动检测武器是一项艰巨的任务，因为:照明，焦点，分辨率等。解决这个问题将对公民安全非常有用。从这个意义上说，本研究工作训练了一个基于YOLOv5 (You Only Look Once)的武器检测系统，针对不同的数据源，在视频监控图像中达到98.56%的准确率，在Nvidia的Jetson AGX Xavier上执行实时推理，达到33 fps，与其他现有的研究相比，这是一个很好的结果。
{"title":"Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier","authors":"Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez","doi":"10.1109/CLEI53233.2021.9640100","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640100","url":null,"abstract":"Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia's Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"42 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84266239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640057
Michael Barquero Salazar, Gabriela Marín-Raventós
The implementation of electronic commerce as a new way of commercialization is not only a business decision, since not all companies, nor all consumers in a region are technologically prepared to adopt electronic commerce. To implement this type of trade, companies should carry out a diagnostic study, both to know if they have the technological resources internally, and to diagnose if their potential clients also have such preparation. From this perspective, this work seeks to design an instrument that allows diagnosing the technological preparation and acceptance of micro and small businesses, and their potential consumers in rural areas. For the elaboration of the instruments, an iterative process was used, composed of three iterations of design and evaluation. Academic experts with extensive experience in instrument design carried out the first evaluation; the second iteration was evaluated in a pilot field study where 6 companies and 10 consumers from regions such as Pococí and San Carlos participated. Finally, the third iteration was developed with a case study in the canton of Río Cuarto de Alajuela, 29 companies and 261 consumers from the region participated in this study. The diagnosis highlights that most of these companies state that they are not technologically prepared to implement e-commerce, while most of the participating consumers indicate that they do feel technologically prepared to use these platforms.
实施电子商务作为一种新的商业化方式不仅仅是一个商业决策，因为不是所有的公司，也不是一个地区的所有消费者在技术上都准备好采用电子商务。为了实施这种类型的贸易，公司应该进行诊断研究，既要知道他们内部是否有技术资源，也要诊断他们的潜在客户是否也有这样的准备。从这个角度来看，这项工作旨在设计一种工具，可以诊断微型和小型企业及其在农村地区的潜在消费者的技术准备和接受程度。在制定这些工具时，采用了一个迭代过程，由三次设计和评价迭代组成。具有丰富仪器设计经验的学术专家进行了首次评估;第二次迭代在试点实地研究中进行了评估，来自Pococí和圣卡洛斯等地区的6家公司和10名消费者参与了研究。最后，第三次迭代是在Río阿拉胡埃拉省(Cuarto de Alajuela)进行的案例研究，该地区的29家公司和261名消费者参与了这项研究。诊断强调，大多数这些公司表示，他们没有技术上准备实施电子商务，而大多数参与的消费者表示，他们确实觉得技术上准备使用这些平台。
{"title":"Diagnosis for the adoption of e-commerce platforms in micro and small enterprises in rural areas: Case study of the region of Río Cuarto, Alajuela","authors":"Michael Barquero Salazar, Gabriela Marín-Raventós","doi":"10.1109/CLEI53233.2021.9640057","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640057","url":null,"abstract":"The implementation of electronic commerce as a new way of commercialization is not only a business decision, since not all companies, nor all consumers in a region are technologically prepared to adopt electronic commerce. To implement this type of trade, companies should carry out a diagnostic study, both to know if they have the technological resources internally, and to diagnose if their potential clients also have such preparation. From this perspective, this work seeks to design an instrument that allows diagnosing the technological preparation and acceptance of micro and small businesses, and their potential consumers in rural areas. For the elaboration of the instruments, an iterative process was used, composed of three iterations of design and evaluation. Academic experts with extensive experience in instrument design carried out the first evaluation; the second iteration was evaluated in a pilot field study where 6 companies and 10 consumers from regions such as Pococí and San Carlos participated. Finally, the third iteration was developed with a case study in the canton of Río Cuarto de Alajuela, 29 companies and 261 consumers from the region participated in this study. The diagnosis highlights that most of these companies state that they are not technologically prepared to implement e-commerce, while most of the participating consumers indicate that they do feel technologically prepared to use these platforms.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"90 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75919678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640180
A. Spengler, P. S. Souza
The popularization of Bitcoin and other cryptocurrencies has motivated the interest in using blockchain infrastructure in contexts other than the original. This is due to blockchain allows for a distribution of data with decentralized management and in a secure environment. In this scenario, the goal of this work is to evaluate the impact of database usage when blockchain is employed to manipulate large volumes of heterogeneous data. The methodology used in our evaluation considers the Hyperledger Fabric to set up a network for sharing medical data, which is obtained from a real database. The performance of this network was collected through experimental studies, with the Hyperledger Caliper benchmark, by measuring the throughput and latency of the network with and without the CouchDB database. Our results show the impact of the overhead imposed by the database when it is used in a blockchain network. This work contributes to future developers of blockchain applications as it shows the impact of database usage on such applications.
{"title":"The impact of using CouchDB on Hyperledger Fabric performance for heterogeneous medical data storage","authors":"A. Spengler, P. S. Souza","doi":"10.1109/CLEI53233.2021.9640180","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640180","url":null,"abstract":"The popularization of Bitcoin and other cryptocurrencies has motivated the interest in using blockchain infrastructure in contexts other than the original. This is due to blockchain allows for a distribution of data with decentralized management and in a secure environment. In this scenario, the goal of this work is to evaluate the impact of database usage when blockchain is employed to manipulate large volumes of heterogeneous data. The methodology used in our evaluation considers the Hyperledger Fabric to set up a network for sharing medical data, which is obtained from a real database. The performance of this network was collected through experimental studies, with the Hyperledger Caliper benchmark, by measuring the throughput and latency of the network with and without the CouchDB database. Our results show the impact of the overhead imposed by the database when it is used in a blockchain network. This work contributes to future developers of blockchain applications as it shows the impact of database usage on such applications.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"68 4 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76494224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640179
A. Nebel, Laura González, Guzmán Llambías
The development of large-scale software systems is usually supported by integration platforms, which provide connectivity and mediation capabilities to facilitate the integration of heterogeneous and distributed applications. Integration platforms have traditionally been built as monolithic systems which, in some of the current contexts (e.g. market's high pace of demand, large amount of users and data), present issues in terms of scalability, maintainability and fault tolerance, among others. In turn, microservices architecture is an approach for developing applications as a set of small independent services, which may contribute to address such limitations (e.g. maintaining and scaling services independently, according to their specific needs). Indeed, various integration platform proposals leveraging this approach have emerged during the last years. However, those proposals are domain-specific and/or they do not provide insights regarding the architecture and implementation of the platform. This paper proposes a general-purpose microservice-based integration platform, which aims to address limitations of monolithic solutions and of the aforementioned existing proposals. Our work comprises the definition of the platform and its main functionality, a description of its microservice-based architecture, and implementation alternatives as well as prototypes for some of its main components.
{"title":"MicroIP: A general-purpose microservice-based integration platform","authors":"A. Nebel, Laura González, Guzmán Llambías","doi":"10.1109/CLEI53233.2021.9640179","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640179","url":null,"abstract":"The development of large-scale software systems is usually supported by integration platforms, which provide connectivity and mediation capabilities to facilitate the integration of heterogeneous and distributed applications. Integration platforms have traditionally been built as monolithic systems which, in some of the current contexts (e.g. market's high pace of demand, large amount of users and data), present issues in terms of scalability, maintainability and fault tolerance, among others. In turn, microservices architecture is an approach for developing applications as a set of small independent services, which may contribute to address such limitations (e.g. maintaining and scaling services independently, according to their specific needs). Indeed, various integration platform proposals leveraging this approach have emerged during the last years. However, those proposals are domain-specific and/or they do not provide insights regarding the architecture and implementation of the platform. This paper proposes a general-purpose microservice-based integration platform, which aims to address limitations of monolithic solutions and of the aforementioned existing proposals. Our work comprises the definition of the platform and its main functionality, a description of its microservice-based architecture, and implementation alternatives as well as prototypes for some of its main components.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"9 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77460637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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