�ݺ�ߣshows by User: SidgleyAndrade

�ݺ�ߣshows by User: SidgleyAndrade / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: SidgleyAndrade / Wed, 09 Jan 2019 16:09:56 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: SidgleyAndrade Situational awareness in social media: lessons learned using information entropy in flood risk management /slideshow/situational-awareness-in-social-media-lessons-learned-using-information-entropy-in-flood-risk-management/127633992 deandradeetalnuvem-190109160956
Increasing situational awareness using social media data is still a problem for the surveillance of disaster-related events due to the amount of data. In order to address this problem, a number of studies have been conducted on the basis of the Tobler's first law of geography, in which social media messages nearest to events are more relevant than the more distant messages. However, these studies fail to take the explicit content of the messages in terms of quantitative measures into account. A quantitative measure is important to prioritize and rank social media messages using another criterion beyond the geographical distance. This paper conducts a case study in the city of S\~{a}o Paulo, Brazil, for assessing the relationship between the information entropy and the distance to flooded areas of rain- flood-related Twitter messages. The results provide evidence that the entropy measure of the tweets is not governed by the Tobler's law of geography. Nonetheless, our findings do not challenge the Tobler's law assumption, but put forward discussions in terms of the relevance of the social media's content in relation to distance to the affected areas by disasters.]]>
Increasing situational awareness using social media data is still a problem for the surveillance of disaster-related events due to the amount of data. In order to address this problem, a number of studies have been conducted on the basis of the Tobler's first law of geography, in which social media messages nearest to events are more relevant than the more distant messages. However, these studies fail to take the explicit content of the messages in terms of quantitative measures into account. A quantitative measure is important to prioritize and rank social media messages using another criterion beyond the geographical distance. This paper conducts a case study in the city of S\~{a}o Paulo, Brazil, for assessing the relationship between the information entropy and the distance to flooded areas of rain- flood-related Twitter messages. The results provide evidence that the entropy measure of the tweets is not governed by the Tobler's law of geography. Nonetheless, our findings do not challenge the Tobler's law assumption, but put forward discussions in terms of the relevance of the social media's content in relation to distance to the affected areas by disasters.]]> Wed, 09 Jan 2019 16:09:56 GMT /slideshow/situational-awareness-in-social-media-lessons-learned-using-information-entropy-in-flood-risk-management/127633992 SidgleyAndrade@slideshare.net(SidgleyAndrade) Situational awareness in social media: lessons learned using information entropy in flood risk management SidgleyAndrade Increasing situational awareness using social media data is still a problem for the surveillance of disaster-related events due to the amount of data. In order to address this problem, a number of studies have been conducted on the basis of the Tobler's first law of geography, in which social media messages nearest to events are more relevant than the more distant messages. However, these studies fail to take the explicit content of the messages in terms of quantitative measures into account. A quantitative measure is important to prioritize and rank social media messages using another criterion beyond the geographical distance. This paper conducts a case study in the city of S\~{a}o Paulo, Brazil, for assessing the relationship between the information entropy and the distance to flooded areas of rain- flood-related Twitter messages. The results provide evidence that the entropy measure of the tweets is not governed by the Tobler's law of geography. Nonetheless, our findings do not challenge the Tobler's law assumption, but put forward discussions in terms of the relevance of the social media's content in relation to distance to the affected areas by disasters. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deandradeetalnuvem-190109160956-thumbnail.jpg?width=120&height=120&fit=bounds" /> Increasing situational awareness using social media data is still a problem for the surveillance of disaster-related events due to the amount of data. In order to address this problem, a number of studies have been conducted on the basis of the Tobler's first law of geography, in which social media messages nearest to events are more relevant than the more distant messages. However, these studies fail to take the explicit content of the messages in terms of quantitative measures into account. A quantitative measure is important to prioritize and rank social media messages using another criterion beyond the geographical distance. This paper conducts a case study in the city of S\~{a}o Paulo, Brazil, for assessing the relationship between the information entropy and the distance to flooded areas of rain- flood-related Twitter messages. The results provide evidence that the entropy measure of the tweets is not governed by the Tobler's law of geography. Nonetheless, our findings do not challenge the Tobler's law assumption, but put forward discussions in terms of the relevance of the social media's content in relation to distance to the affected areas by disasters.

Situational awareness in social media: lessons learned using information entropy in flood risk management from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 Does keyword noise change over space and time? A case study of flood- and rain-related social media messages /slideshow/does-keyword-noise-change-over-space-and-time-a-case-study-of-flood-and-rainrelated-social-media-messages/127620446 deandradeetalshortapres-190109141620
Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales.]]>
Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales.]]> Wed, 09 Jan 2019 14:16:20 GMT /slideshow/does-keyword-noise-change-over-space-and-time-a-case-study-of-flood-and-rainrelated-social-media-messages/127620446 SidgleyAndrade@slideshare.net(SidgleyAndrade) Does keyword noise change over space and time? A case study of flood- and rain-related social media messages SidgleyAndrade Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deandradeetalshortapres-190109141620-thumbnail.jpg?width=120&height=120&fit=bounds" /> Social media is a valuable source of information for different domains, since users share their opinion and knowledge in (near) real-time. Moreover, users usually use different words to refer to a particular event (e.g., a rain event). These words may be later employed to filter social media messages regarding new occurrences of the event and, thus, to reduce the number of unrelated messages. These words, however, may have different meanings and, thus, may not reduce the number of messages. In this work, we conduct a case study to measure which rain- or flood-related keywords are less relevant to reduce the number of unrelated messages. The results show that the keywords change over space, due to local language/culture, and time, specially in different time scales.

Does keyword noise change over space and time? A case study of flood- and rain-related social media messages from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 Mining rainfall spatio-temporal patterns in Twitter: a temporal approach /slideshow/mining-rainfall-spatiotemporal-patterns-in-twitter-a-temporal-approach/75707772 agile2017miningrainfallspatio-temporalpatternsintwitterpresentationscandrade-170505120852
Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of S\~{a}o Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from -10 to +10 minutes. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages.]]>
Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of S\~{a}o Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from -10 to +10 minutes. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages.]]> Fri, 05 May 2017 12:08:52 GMT /slideshow/mining-rainfall-spatiotemporal-patterns-in-twitter-a-temporal-approach/75707772 SidgleyAndrade@slideshare.net(SidgleyAndrade) Mining rainfall spatio-temporal patterns in Twitter: a temporal approach SidgleyAndrade Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of S\~{a}o Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from -10 to +10 minutes. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agile2017miningrainfallspatio-temporalpatternsintwitterpresentationscandrade-170505120852-thumbnail.jpg?width=120&height=120&fit=bounds" /> Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of S\~{a}o Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from -10 to +10 minutes. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages.

Mining rainfall spatio-temporal patterns in Twitter: a temporal approach from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 An Introduction to Metric Learning for Clustering /slideshow/an-introduction-to-metric-learning-for-clustering/63208498 20160613seminario-metriclearningforclusteringsidgley-160618182351
An introduction to metric learning for clustering]]>
An introduction to metric learning for clustering]]> Sat, 18 Jun 2016 18:23:51 GMT /slideshow/an-introduction-to-metric-learning-for-clustering/63208498 SidgleyAndrade@slideshare.net(SidgleyAndrade) An Introduction to Metric Learning for Clustering SidgleyAndrade An introduction to metric learning for clustering <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20160613seminario-metriclearningforclusteringsidgley-160618182351-thumbnail.jpg?width=120&height=120&fit=bounds" /> An introduction to metric learning for clustering

An Introduction to Metric Learning for Clustering from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 An introduction to automated analysis of feature models through propositional logic /slideshow/introduction-to-automated-analysis-of-feature-models-through-propositional-logic/56102392 ssc5793-intr-automated-analysis-feature-model-151213190149
An introduction to automated analysis of feature models in software product line through propositional logic.]]>
An introduction to automated analysis of feature models in software product line through propositional logic.]]> Sun, 13 Dec 2015 19:01:48 GMT /slideshow/introduction-to-automated-analysis-of-feature-models-through-propositional-logic/56102392 SidgleyAndrade@slideshare.net(SidgleyAndrade) An introduction to automated analysis of feature models through propositional logic SidgleyAndrade An introduction to automated analysis of feature models in software product line through propositional logic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ssc5793-intr-automated-analysis-feature-model-151213190149-thumbnail.jpg?width=120&height=120&fit=bounds" /> An introduction to automated analysis of feature models in software product line through propositional logic.

An introduction to automated analysis of feature models through propositional logic from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 pSets TSI32B - Estrutura, Pesquisa e Ordena??o de Dados (TSI UTFPR-Toledo) https://pt.slideshare.net/slideshow/tsi32-b-epopsetall/45587740 tsi32b-epo-psetall-150308204712-conversion-gate01
Conjunto de problemas referente aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.]]>
Conjunto de problemas referente aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.]]> Sun, 08 Mar 2015 20:47:12 GMT https://pt.slideshare.net/slideshow/tsi32-b-epopsetall/45587740 SidgleyAndrade@slideshare.net(SidgleyAndrade) pSets TSI32B - Estrutura, Pesquisa e Ordena??o de Dados (TSI UTFPR-Toledo) SidgleyAndrade Conjunto de problemas referente aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tsi32b-epo-psetall-150308204712-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Conjunto de problemas referente aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.

from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 Aulas TSI32B - Estrutura, Pesquisa e Ordena??o de Dados (TSI UTFPR-Toledo) https://pt.slideshare.net/SidgleyAndrade/aulas-tsi32b-banco-de-dados-i-tsi-utfprtoledo tsi32b-epo-all-150308203731-conversion-gate01
Este material �� uma introdu??o aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.]]>
Este material �� uma introdu??o aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.]]> Sun, 08 Mar 2015 20:37:31 GMT https://pt.slideshare.net/SidgleyAndrade/aulas-tsi32b-banco-de-dados-i-tsi-utfprtoledo SidgleyAndrade@slideshare.net(SidgleyAndrade) Aulas TSI32B - Estrutura, Pesquisa e Ordena??o de Dados (TSI UTFPR-Toledo) SidgleyAndrade Este material �� uma introdu??o aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tsi32b-epo-all-150308203731-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Este material �� uma introdu??o aos conceitos, t��cnicas e recursos fundamentais para a manipula??o de cadeia de caracteres, pilhas, listas, filas, tabela hash e grafos, bem como a pesquisa e ordena??o de dados.

from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 pSets TSI33A - Banco de Dados I (TSI UTFPR-Toledo) https://pt.slideshare.net/slideshow/tsi33a-banco-de-dados-i-tsi-utfprtoledo-45585327/45585327 tsi33a-bdi-psetall-150308183854-conversion-gate01
Conjunto de problemas referente aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.]]>
Conjunto de problemas referente aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.]]> Sun, 08 Mar 2015 18:38:54 GMT https://pt.slideshare.net/slideshow/tsi33a-banco-de-dados-i-tsi-utfprtoledo-45585327/45585327 SidgleyAndrade@slideshare.net(SidgleyAndrade) pSets TSI33A - Banco de Dados I (TSI UTFPR-Toledo) SidgleyAndrade Conjunto de problemas referente aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tsi33a-bdi-psetall-150308183854-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Conjunto de problemas referente aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.

from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 Aulas TSI33A - Banco de Dados I (TSI UTFPR-Toledo) https://pt.slideshare.net/slideshow/tsi33a-banco-de-dados-i-tsi-utfprtoledo/45579352 tsi33a-bdi-all-150308141842-conversion-gate01
Este material �� uma introdu??o aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.]]>
Este material �� uma introdu??o aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.]]> Sun, 08 Mar 2015 14:18:42 GMT https://pt.slideshare.net/slideshow/tsi33a-banco-de-dados-i-tsi-utfprtoledo/45579352 SidgleyAndrade@slideshare.net(SidgleyAndrade) Aulas TSI33A - Banco de Dados I (TSI UTFPR-Toledo) SidgleyAndrade Este material �� uma introdu??o aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tsi33a-bdi-all-150308141842-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Este material �� uma introdu??o aos conceitos e recursos fundamentais necess��rios para projetar e manipular banco de dados relacionais, bem como desenvolver aplica??es com sistemas de banco de dados relacionais.

from Federal University of Technology - Paranߵ/Brazil (UTFPR)

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0 https://cdn.slidesharecdn.com/profile-photo-SidgleyAndrade-48x48.jpg?cb=1592240717 I am a Professor at the Federal University of Technology - Paran��, Brazil. I love address interdisciplinary problems involving spatio-temporal data analysis. pessoal.utfpr.edu.br/sidgleyandrade https://cdn.slidesharecdn.com/ss_thumbnails/deandradeetalnuvem-190109160956-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/situational-awareness-in-social-media-lessons-learned-using-information-entropy-in-flood-risk-management/127633992 Situational awareness ... https://cdn.slidesharecdn.com/ss_thumbnails/deandradeetalshortapres-190109141620-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/does-keyword-noise-change-over-space-and-time-a-case-study-of-flood-and-rainrelated-social-media-messages/127620446 Does keyword noise cha... https://cdn.slidesharecdn.com/ss_thumbnails/agile2017miningrainfallspatio-temporalpatternsintwitterpresentationscandrade-170505120852-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/mining-rainfall-spatiotemporal-patterns-in-twitter-a-temporal-approach/75707772 Mining rainfall spatio...