Nélson R. S. Passos, Ariel F. Rodrigues, Hendrik T. Macedo, Bruno O. Prado, G. J. F. D. Silva, L. Matos
Democracy is a political regime based on the majority's choice. However, people can only make conscious decisions if they have access to high quality information. This paper aimed to join data from different sources and to transform them in knowledge to Brazilians voters. It applied ETL (Extract, Transform, Load) methods on open and property data to build a process that covers data gathering and transformation, dataset generation, database modeling and population, public APIs development, and a mobile app as the knowledge's visualization model. As a result, for 2018 Brazil's general elections, it processed almost two million candidacies, half a million deputies' tasks and five thousand court lawsuits. Furthermore, the products released by this research reached good performance indicators: the access logs recorded more than three million hits for the public API and twelve thousand downloads for the mobile app in the last week of the first-round's political campaign.
{"title":"Open Data Extraction, Transformation, and Loading as a Tool for Supporting 2018 Elections' Voters","authors":"Nélson R. S. Passos, Ariel F. Rodrigues, Hendrik T. Macedo, Bruno O. Prado, G. J. F. D. Silva, L. Matos","doi":"10.1145/3330204.3330232","DOIUrl":"https://doi.org/10.1145/3330204.3330232","url":null,"abstract":"Democracy is a political regime based on the majority's choice. However, people can only make conscious decisions if they have access to high quality information. This paper aimed to join data from different sources and to transform them in knowledge to Brazilians voters. It applied ETL (Extract, Transform, Load) methods on open and property data to build a process that covers data gathering and transformation, dataset generation, database modeling and population, public APIs development, and a mobile app as the knowledge's visualization model. As a result, for 2018 Brazil's general elections, it processed almost two million candidacies, half a million deputies' tasks and five thousand court lawsuits. Furthermore, the products released by this research reached good performance indicators: the access logs recorded more than three million hits for the public API and twelve thousand downloads for the mobile app in the last week of the first-round's political campaign.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115314346","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}
Software design models play a key role in many activities of information systems engineering, such as documenting software artefacts, communicating project decisions, and code generation. In this scenario, the techniques for comparison of software design models are used for several purposes, such as, for detecting clones, and model evolution. In the last decades, academia proposed different techniques for comparing software models. Even using these different techniques for model comparison, this process is still an activity of a subjective nature, because during this process, different developers can interpret the similarity differently. Thus, the problem is that it is still unknown if developers has the same intuition in order to resolve comparison of software design models. For this, the main objective of this work is to explore the effects of their experience level, i.e., experienced and inexperienced developers, relative to their effort and correctness for resolving activities of comparing software design models. Therefore, a controlled experiment was conducted to evaluate the developer's experience level regarding on similarities of UML Models. The results show that the developer's experience does not affect the understanding of similarities activities.
{"title":"On the Effects of Developers' Intuition on Measuring Similarity Between UML Models","authors":"L. Gonçales, Kleinner Farias, Vinícius Bischoff","doi":"10.1145/3330204.3330238","DOIUrl":"https://doi.org/10.1145/3330204.3330238","url":null,"abstract":"Software design models play a key role in many activities of information systems engineering, such as documenting software artefacts, communicating project decisions, and code generation. In this scenario, the techniques for comparison of software design models are used for several purposes, such as, for detecting clones, and model evolution. In the last decades, academia proposed different techniques for comparing software models. Even using these different techniques for model comparison, this process is still an activity of a subjective nature, because during this process, different developers can interpret the similarity differently. Thus, the problem is that it is still unknown if developers has the same intuition in order to resolve comparison of software design models. For this, the main objective of this work is to explore the effects of their experience level, i.e., experienced and inexperienced developers, relative to their effort and correctness for resolving activities of comparing software design models. Therefore, a controlled experiment was conducted to evaluate the developer's experience level regarding on similarities of UML Models. The results show that the developer's experience does not affect the understanding of similarities activities.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122684826","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}
This paper presents a computational model entitled Kairós for prediction and recommendation in project schedules. The model uses context prediction mechanisms based on task data and projects stored during its execution. The recommendations are made to the manager in a proactive manner, considering best practices in project management and learning with the approval or rejection of each recommendation. A prototype was implemented based on the proposed model, and through it, an evaluation was carried out using simulated use cases with real data from a large company. The results showed that the model was able to predict with precision of 93% if a task would be completed with delay, with 87% accuracy.
{"title":"Kairós","authors":"F. C. Rodrigues, A. Filippetto, J. Barbosa","doi":"10.1145/3330204.3330205","DOIUrl":"https://doi.org/10.1145/3330204.3330205","url":null,"abstract":"This paper presents a computational model entitled Kairós for prediction and recommendation in project schedules. The model uses context prediction mechanisms based on task data and projects stored during its execution. The recommendations are made to the manager in a proactive manner, considering best practices in project management and learning with the approval or rejection of each recommendation. A prototype was implemented based on the proposed model, and through it, an evaluation was carried out using simulated use cases with real data from a large company. The results showed that the model was able to predict with precision of 93% if a task would be completed with delay, with 87% accuracy.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114587063","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}
Tamires A. S. Sousa, V. D. Ferreira, A. B. Marques
Autistic Spectrum Disorder (ASD) is characterized by persistent deficits in communication and social interaction, restrictive and repetitive patterns of behavior. The development of information systems for supporting the treatment of ASD has been intensified in recent years, allowing new ways of treatment. Although new systems are developed, there are still few studies that investigate the impact on the use of these systems on the daily of autistic users. In this sense, we conducted a research to investigate the impact on the use of information systems by autistic users. Our methodology adopted the following steps: 1) immersion in groups of social networks in which discussions about ASD are carried out; 2) identification of the research target audience; 3) creation of the research collection instrument; 4) execution of a survey with parents and professionals that care of autistic children; 5) analysis of the data obtained. During two weeks, we obtained 53 questionnaire responses. We observed that 96.2% of the respondents indicate that their children have access to software technologies and 90.5% agree that the technologies can support the teaching and learning of autistic people. We identified positive and negative characteristics of software technologies in order to provide opportunities for improving the existing systems or developing systems more adequate to needs of autistic children.
{"title":"How do software technologies impact the daily of people with autism in Brazil: A survey","authors":"Tamires A. S. Sousa, V. D. Ferreira, A. B. Marques","doi":"10.1145/3330204.3330274","DOIUrl":"https://doi.org/10.1145/3330204.3330274","url":null,"abstract":"Autistic Spectrum Disorder (ASD) is characterized by persistent deficits in communication and social interaction, restrictive and repetitive patterns of behavior. The development of information systems for supporting the treatment of ASD has been intensified in recent years, allowing new ways of treatment. Although new systems are developed, there are still few studies that investigate the impact on the use of these systems on the daily of autistic users. In this sense, we conducted a research to investigate the impact on the use of information systems by autistic users. Our methodology adopted the following steps: 1) immersion in groups of social networks in which discussions about ASD are carried out; 2) identification of the research target audience; 3) creation of the research collection instrument; 4) execution of a survey with parents and professionals that care of autistic children; 5) analysis of the data obtained. During two weeks, we obtained 53 questionnaire responses. We observed that 96.2% of the respondents indicate that their children have access to software technologies and 90.5% agree that the technologies can support the teaching and learning of autistic people. We identified positive and negative characteristics of software technologies in order to provide opportunities for improving the existing systems or developing systems more adequate to needs of autistic children.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477778","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}
L. Schulte, N. Perez, Leonardo Bidese de Pinho, G. Trentin
The increasing worldwide demand for resources such as water and food brings the need for the application of scientific methods in agriculture and livestock to increase their productivity. One way to increase the efficiency of productive systems that make extensive beef cattle breeding is by adjusting the pasture stocking rate to optimize the animal weight gain per hectare. The present work describes a module for Farm Management Information System (FMIS) based on Long Short-Term Memory (LSTM) neural networks to estimate forage mass by means of historical pasture growth data collected through the direct method associated with meteorological data. The proposed method is based on exploratory and experimental interdisciplinary research, with systematic bibliographic research and study case. The results show that LSTM neural networks are able to make a reasonable estimate for the dry mass variation over time. Using this estimate, one can obtain a gain/hectare/year of 121 kg of live weight against 70 kg where there is no adjustment of animal load and 98 kg where this adjustment is made based on the estimate of the previous month.
{"title":"Decision Support System for Precision Livestock: Machine Learning-Based Prediction Module for Stocking Rate Adjustment","authors":"L. Schulte, N. Perez, Leonardo Bidese de Pinho, G. Trentin","doi":"10.1145/3330204.3330222","DOIUrl":"https://doi.org/10.1145/3330204.3330222","url":null,"abstract":"The increasing worldwide demand for resources such as water and food brings the need for the application of scientific methods in agriculture and livestock to increase their productivity. One way to increase the efficiency of productive systems that make extensive beef cattle breeding is by adjusting the pasture stocking rate to optimize the animal weight gain per hectare. The present work describes a module for Farm Management Information System (FMIS) based on Long Short-Term Memory (LSTM) neural networks to estimate forage mass by means of historical pasture growth data collected through the direct method associated with meteorological data. The proposed method is based on exploratory and experimental interdisciplinary research, with systematic bibliographic research and study case. The results show that LSTM neural networks are able to make a reasonable estimate for the dry mass variation over time. Using this estimate, one can obtain a gain/hectare/year of 121 kg of live weight against 70 kg where there is no adjustment of animal load and 98 kg where this adjustment is made based on the estimate of the previous month.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099205","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}
This work proposes an investigation to improve the efficiency of a lexical-based classifier, the SentiStrength, for automatic sentiment detection in postings related to the use of systems. To achieve this goal, the TF-IDF metric was used to select words that are related to the domain of the posts, which will enrich the dictionary used by the tool to generate the polarity of the posts. The efficiency of a dictionarie enriched with words in their root form and a dictionarie enriched with lematized words will also be investigated. The research was conducted with 2108 sentences extracted from the reviews section of the Play Store on urban mobility applications, such as Waze, Google Maps and GPS Brazil. One of the results obtained was a 7.3 % increase in the accuracy of the classifier when using enriched dictionaries.
{"title":"Enrichment of dictionaries to improve the automatic classification of feelings in postings related to the use of systems","authors":"Afonso Matheus Sousa Lima, M. Mendes, L. A. Cruz","doi":"10.1145/3330204.3330219","DOIUrl":"https://doi.org/10.1145/3330204.3330219","url":null,"abstract":"This work proposes an investigation to improve the efficiency of a lexical-based classifier, the SentiStrength, for automatic sentiment detection in postings related to the use of systems. To achieve this goal, the TF-IDF metric was used to select words that are related to the domain of the posts, which will enrich the dictionary used by the tool to generate the polarity of the posts. The efficiency of a dictionarie enriched with words in their root form and a dictionarie enriched with lematized words will also be investigated. The research was conducted with 2108 sentences extracted from the reviews section of the Play Store on urban mobility applications, such as Waze, Google Maps and GPS Brazil. One of the results obtained was a 7.3 % increase in the accuracy of the classifier when using enriched dictionaries.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509397","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}
Tatiany Xavier de Godoi, A. Menolli, Gustavo Marcelino Dionisio
Entrepreneurship, innovation and startup are widely used terms lately, and much information is currently available on this subject. Software startups are companies that have particular characteristics, such as being scalable, developing innovative products, and living in an environment of uncertainty. Considering the scenario of these types of companies is still incipient, this paper aims to analyze the perception of software startups in relation to the main factors described in the literature that can lead to success or failure. For that, a survey was carried out with software startups incubated in Paraná. The results show that the startups perception is that their success is related only to internal factors of the company, and that many software startups are not prepared as they should in the early stages of development, not applying several concepts described in the literature as fundamental to aid in the business development and validation.
{"title":"Software Startups Success Factors Study under the Entrepreneurial Perspective","authors":"Tatiany Xavier de Godoi, A. Menolli, Gustavo Marcelino Dionisio","doi":"10.1145/3330204.3330263","DOIUrl":"https://doi.org/10.1145/3330204.3330263","url":null,"abstract":"Entrepreneurship, innovation and startup are widely used terms lately, and much information is currently available on this subject. Software startups are companies that have particular characteristics, such as being scalable, developing innovative products, and living in an environment of uncertainty. Considering the scenario of these types of companies is still incipient, this paper aims to analyze the perception of software startups in relation to the main factors described in the literature that can lead to success or failure. For that, a survey was carried out with software startups incubated in Paraná. The results show that the startups perception is that their success is related only to internal factors of the company, and that many software startups are not prepared as they should in the early stages of development, not applying several concepts described in the literature as fundamental to aid in the business development and validation.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124003840","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}
Data Ecosystems are a cultural, technological, and social phenomenon based on the interplay of technology, actors and businesses, which provide an environment for creating, managing and sustaining data sharing initiatives. There is a general consensus as to the crucial role metadata can play on the Data Ecosystem. However, in most cases, the metadata management is underspecified, if not unaddressed at all. The employment of a metadata curation strategy can bring an ecosystem success and further ensure realization of Data Ecosystem actors' purposes. In this work, our contribution is proposing a Metadata Curation Framework, called Louvre, which proposes a wide range of processes for curating metadata in Data Ecosystems. The promise is the employment of a well-conceived, efficient curation strategy for metadata.
{"title":"Louvre: A Framework for Metadata Curation in Data Ecosystem","authors":"Marcelo Iury S. Oliveira, B. Lóscio","doi":"10.1145/3330204.3330248","DOIUrl":"https://doi.org/10.1145/3330204.3330248","url":null,"abstract":"Data Ecosystems are a cultural, technological, and social phenomenon based on the interplay of technology, actors and businesses, which provide an environment for creating, managing and sustaining data sharing initiatives. There is a general consensus as to the crucial role metadata can play on the Data Ecosystem. However, in most cases, the metadata management is underspecified, if not unaddressed at all. The employment of a metadata curation strategy can bring an ecosystem success and further ensure realization of Data Ecosystem actors' purposes. In this work, our contribution is proposing a Metadata Curation Framework, called Louvre, which proposes a wide range of processes for curating metadata in Data Ecosystems. The promise is the employment of a well-conceived, efficient curation strategy for metadata.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116387036","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}
Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.
{"title":"A Deep Learning Approach to the Malware Classification Problem using Autoencoders","authors":"Dhiego Ramos Pinto, J. C. Duarte, R. Sant'Ana","doi":"10.1145/3330204.3330229","DOIUrl":"https://doi.org/10.1145/3330204.3330229","url":null,"abstract":"Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126953424","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}
Anderson Soares Costa, Rodolfo Sobreira Alves, F. Silva, M. Endler
The Internet of Things (IoT) is a combination of ubiquitous computing and the Internet, in which IoT (smart objects) devices can collect and exchange data, cooperating with people and the environment in which they find themselves. The Internet of Mobile Thing (IoMT), which is an extension of IoT, proposes scenarios in which smart objects and gateways are mobile. In this context, this work is focused on the discovery of smart objects in IoT/IoMT environments considering the following problems: mobility of both smart objects and gateways; great heterogeneity of smart objects and communication technologies to access them; the need for interoperability in these environments; the need to combine data from smart objects with knowledge bases. Therefore, the objective of this work is to combine Semantic Flow Processing with knowledge representation techniques to enrich the instantaneous and continuous discovery of smart objects and their services in IoT/IoMT environments. To this end, an ontology was developed to describe IoT/IoMT scenarios, a semantic middleware, an API for building information systems and applications, and a cloud infrastructure for querying and semantic streaming of smart objects. The evaluation of this work is done through a use case in the field of intelligent parking lots.
物联网(IoT)是无处不在的计算和互联网的结合，其中IoT(智能对象)设备可以收集和交换数据，与人及其所在的环境合作。移动物联网(Internet of Mobile Thing, IoMT)是物联网的延伸，提出了智能对象和智能网关移动的场景。在此背景下，本工作的重点是在IoT/IoMT环境中发现智能对象，考虑以下问题:智能对象和网关的移动性;智能对象的巨大异质性和访问它们的通信技术;在这些环境中需要互操作性;需要将来自智能对象的数据与知识库相结合。因此，本工作的目标是将语义流处理与知识表示技术相结合，以丰富IoT/IoMT环境中智能对象及其服务的即时和连续发现。为此，开发了描述IoT/IoMT场景的本体、语义中间件、用于构建信息系统和应用程序的API以及用于查询和智能对象语义流的云基础设施。通过智能停车场领域的一个用例对该工作进行了评估。
{"title":"Dynamic Discovery of IoT Services Based on Semantic Processing of Event Flows","authors":"Anderson Soares Costa, Rodolfo Sobreira Alves, F. Silva, M. Endler","doi":"10.1145/3330204.3330280","DOIUrl":"https://doi.org/10.1145/3330204.3330280","url":null,"abstract":"The Internet of Things (IoT) is a combination of ubiquitous computing and the Internet, in which IoT (smart objects) devices can collect and exchange data, cooperating with people and the environment in which they find themselves. The Internet of Mobile Thing (IoMT), which is an extension of IoT, proposes scenarios in which smart objects and gateways are mobile. In this context, this work is focused on the discovery of smart objects in IoT/IoMT environments considering the following problems: mobility of both smart objects and gateways; great heterogeneity of smart objects and communication technologies to access them; the need for interoperability in these environments; the need to combine data from smart objects with knowledge bases. Therefore, the objective of this work is to combine Semantic Flow Processing with knowledge representation techniques to enrich the instantaneous and continuous discovery of smart objects and their services in IoT/IoMT environments. To this end, an ontology was developed to describe IoT/IoMT scenarios, a semantic middleware, an API for building information systems and applications, and a cloud infrastructure for querying and semantic streaming of smart objects. The evaluation of this work is done through a use case in the field of intelligent parking lots.","PeriodicalId":348938,"journal":{"name":"Proceedings of the XV Brazilian Symposium on Information Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128853786","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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