�ݺ�ߣshows by User: mbrambil

�ݺ�ߣshows by User: mbrambil / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: mbrambil / Tue, 24 Sep 2024 21:00:26 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: mbrambil Essential concepts of data architectures /slideshow/essential-concepts-of-data-architectures/271999110 smbud-04-data-architectures-240924210026-7919f784
The basic concepts that are needed to understand relational and non-relational database architectures]]>
The basic concepts that are needed to understand relational and non-relational database architectures]]> Tue, 24 Sep 2024 21:00:26 GMT /slideshow/essential-concepts-of-data-architectures/271999110 mbrambil@slideshare.net(mbrambil) Essential concepts of data architectures mbrambil The basic concepts that are needed to understand relational and non-relational database architectures <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/smbud-04-data-architectures-240924210026-7919f784-thumbnail.jpg?width=120&height=120&fit=bounds" /> The basic concepts that are needed to understand relational and non-relational database architectures

Essential concepts of data architectures from Marco Brambilla

]]> 7 0 https://cdn.slidesharecdn.com/ss_thumbnails/smbud-04-data-architectures-240924210026-7919f784-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco /slideshow/msc-thesis-topics-and-proposals-polimi-data-science-lab-2024-prof-brambilla-marco/264819563 thesis-proposals-2024-231221073444-c945b49d
M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco ]]>
M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco ]]> Thu, 21 Dec 2023 07:34:44 GMT /slideshow/msc-thesis-topics-and-proposals-polimi-data-science-lab-2024-prof-brambilla-marco/264819563 mbrambil@slideshare.net(mbrambil) M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco mbrambil M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis-proposals-2024-231221073444-c945b49d-thumbnail.jpg?width=120&height=120&fit=bounds" /> M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco

M.Sc. Thesis Topics and Proposals @ Polimi Data Science Lab - 2024 - prof. Brambilla Marco from Marco Brambilla

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0 Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - prof. Brambilla Marco /slideshow/thesis-topics-and-proposals-polimi-data-science-lab-2023-prof-brambilla-marco/264819340 thesis-topics-and-proposals-brambilla-2023-231221072715-7bcfb0fb
Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - Brambilla Marco]]>
Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - Brambilla Marco]]> Thu, 21 Dec 2023 07:27:15 GMT /slideshow/thesis-topics-and-proposals-polimi-data-science-lab-2023-prof-brambilla-marco/264819340 mbrambil@slideshare.net(mbrambil) Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - prof. Brambilla Marco mbrambil Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - Brambilla Marco <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thesis-topics-and-proposals-brambilla-2023-231221072715-7bcfb0fb-thumbnail.jpg?width=120&height=120&fit=bounds" /> Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - Brambilla Marco

Thesis Topics and Proposals @ Polimi Data Science Lab - 2023 - prof. Brambilla Marco from Marco Brambilla

]]> 1155 0 https://cdn.slidesharecdn.com/ss_thumbnails/thesis-topics-and-proposals-brambilla-2023-231221072715-7bcfb0fb-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Hierarchical Transformers for User Semantic Similarity - ICWE 2023 /slideshow/hierarchical-transformersfor-user-semantic-similarity-icwe-2023/258328252 icwe2023-hierarchical-transformers-for-user-semantic-similarity-final-230609095113-5c911c28
We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks. We use a large dataset of Twitter users and apply an automatic labeling approach. The dataset consists of English tweets posted in November and December 2020, totaling about 27GB of compressed data. Preprocessing steps include filtering out short texts, cleaning user connections, and selecting a benchmark set of users for evaluation. Since Transformer architectures are known to work well on short text, we cannot use them on extensive collections of tweets describing the activity of a user. Therefore, we propose a hierarchical structure of transformer models to be used first on tweets and then on their aggregations. The models used in the study include hierarchical transformers, and the tweet embeddings are obtained using four Transformer-based models: RoBERTa2, BERTweet3, Sentence BERT4, and Twitter4SSE5. The researchers test different techniques for processing tweet embeddings to generate accurate user embeddings, including mean pooling, recurrence over BERT (RoBERT), and transformer over BERT (ToBERT). The evaluation of the models is done on a set of 5,000 users, comparing user similarities with 30 other candidate users, 5 of which are considered similar and 25 considered dissimilar. The evaluation metrics used include mean average precision (MAP), mean reciprocal rank (MRR) at 10, and normalized discounted cumulative gain (nDCG). The optimization process involves selecting a loss function and using the AdamW optimizer with specific hyperparameters. The results show that the hierarchical approach with a Stage-1 Twitter4SSE model and a Stage-2 Transformer model performs the best among the alternatives. In conclusion, the research provides a large unbiased dataset for user similarity analysis, presents a hierarchical language model optimized for accurate user similarity computation, and validates the models' performance on similarity tasks, with potential applications to related problems. The future work includes investigating the impact of time and topic drift on the models' performance. ]]>
We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks. We use a large dataset of Twitter users and apply an automatic labeling approach. The dataset consists of English tweets posted in November and December 2020, totaling about 27GB of compressed data. Preprocessing steps include filtering out short texts, cleaning user connections, and selecting a benchmark set of users for evaluation. Since Transformer architectures are known to work well on short text, we cannot use them on extensive collections of tweets describing the activity of a user. Therefore, we propose a hierarchical structure of transformer models to be used first on tweets and then on their aggregations. The models used in the study include hierarchical transformers, and the tweet embeddings are obtained using four Transformer-based models: RoBERTa2, BERTweet3, Sentence BERT4, and Twitter4SSE5. The researchers test different techniques for processing tweet embeddings to generate accurate user embeddings, including mean pooling, recurrence over BERT (RoBERT), and transformer over BERT (ToBERT). The evaluation of the models is done on a set of 5,000 users, comparing user similarities with 30 other candidate users, 5 of which are considered similar and 25 considered dissimilar. The evaluation metrics used include mean average precision (MAP), mean reciprocal rank (MRR) at 10, and normalized discounted cumulative gain (nDCG). The optimization process involves selecting a loss function and using the AdamW optimizer with specific hyperparameters. The results show that the hierarchical approach with a Stage-1 Twitter4SSE model and a Stage-2 Transformer model performs the best among the alternatives. In conclusion, the research provides a large unbiased dataset for user similarity analysis, presents a hierarchical language model optimized for accurate user similarity computation, and validates the models' performance on similarity tasks, with potential applications to related problems. The future work includes investigating the impact of time and topic drift on the models' performance. ]]> Fri, 09 Jun 2023 09:51:13 GMT /slideshow/hierarchical-transformersfor-user-semantic-similarity-icwe-2023/258328252 mbrambil@slideshare.net(mbrambil) Hierarchical Transformers for User Semantic Similarity - ICWE 2023 mbrambil We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks. We use a large dataset of Twitter users and apply an automatic labeling approach. The dataset consists of English tweets posted in November and December 2020, totaling about 27GB of compressed data. Preprocessing steps include filtering out short texts, cleaning user connections, and selecting a benchmark set of users for evaluation. Since Transformer architectures are known to work well on short text, we cannot use them on extensive collections of tweets describing the activity of a user. Therefore, we propose a hierarchical structure of transformer models to be used first on tweets and then on their aggregations. The models used in the study include hierarchical transformers, and the tweet embeddings are obtained using four Transformer-based models: RoBERTa2, BERTweet3, Sentence BERT4, and Twitter4SSE5. The researchers test different techniques for processing tweet embeddings to generate accurate user embeddings, including mean pooling, recurrence over BERT (RoBERT), and transformer over BERT (ToBERT). The evaluation of the models is done on a set of 5,000 users, comparing user similarities with 30 other candidate users, 5 of which are considered similar and 25 considered dissimilar. The evaluation metrics used include mean average precision (MAP), mean reciprocal rank (MRR) at 10, and normalized discounted cumulative gain (nDCG). The optimization process involves selecting a loss function and using the AdamW optimizer with specific hyperparameters. The results show that the hierarchical approach with a Stage-1 Twitter4SSE model and a Stage-2 Transformer model performs the best among the alternatives. In conclusion, the research provides a large unbiased dataset for user similarity analysis, presents a hierarchical language model optimized for accurate user similarity computation, and validates the models' performance on similarity tasks, with potential applications to related problems. The future work includes investigating the impact of time and topic drift on the models' performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icwe2023-hierarchical-transformers-for-user-semantic-similarity-final-230609095113-5c911c28-thumbnail.jpg?width=120&height=120&fit=bounds" /> We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks. We use a large dataset of Twitter users and apply an automatic labeling approach. The dataset consists of English tweets posted in November and December 2020, totaling about 27GB of compressed data. Preprocessing steps include filtering out short texts, cleaning user connections, and selecting a benchmark set of users for evaluation. Since Transformer architectures are known to work well on short text, we cannot use them on extensive collections of tweets describing the activity of a user. Therefore, we propose a hierarchical structure of transformer models to be used first on tweets and then on their aggregations. The models used in the study include hierarchical transformers, and the tweet embeddings are obtained using four Transformer-based models: RoBERTa2, BERTweet3, Sentence BERT4, and Twitter4SSE5. The researchers test different techniques for processing tweet embeddings to generate accurate user embeddings, including mean pooling, recurrence over BERT (RoBERT), and transformer over BERT (ToBERT). The evaluation of the models is done on a set of 5,000 users, comparing user similarities with 30 other candidate users, 5 of which are considered similar and 25 considered dissimilar. The evaluation metrics used include mean average precision (MAP), mean reciprocal rank (MRR) at 10, and normalized discounted cumulative gain (nDCG). The optimization process involves selecting a loss function and using the AdamW optimizer with specific hyperparameters. The results show that the hierarchical approach with a Stage-1 Twitter4SSE model and a Stage-2 Transformer model performs the best among the alternatives. In conclusion, the research provides a large unbiased dataset for user similarity analysis, presents a hierarchical language model optimized for accurate user similarity computation, and validates the models' performance on similarity tasks, with potential applications to related problems. The future work includes investigating the impact of time and topic drift on the models' performance.

Hierarchical Transformers for User Semantic Similarity - ICWE 2023 from Marco Brambilla

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0 Exploring the Bi-verse.�A trip across the digital and physical ecospheres /slideshow/exploring-the-biversea-trip-across-the-digital-and-physical-ecospheres/253969698 exploring-biverse-wise-2022-221102143507-35d90d2d
The Web and social media are the environments where people post their content, opinions, activities, and resources. Therefore, a considerable amount of user-generated content is produced every day for a wide variety of purposes. On the other side, people live their everyday life immersed in the physical world, where society, economy, politics and personal relations continuously evolve. These two opposite and complementary environment are today fully integrated: they reflect each other and they interact with each other in a stronger and stronger way. Exploring and studying content and data coming from both environments offers a great opportunity to understand the ever evolving modern society, in terms of topics of interest, events, relations, and behaviour. In this speech I will discuss through business cases and socio-political scenarios how we can extract insights and understand reality by combining and analyzing data from the digital and physical world, so as to reach a better overall picture of reality itself. Along this path, we need to keep into account that reality is complex and varies in time, space and along many other dimensions, including societal and economic variables. The speech highlights the main challenges that need to be addressed and outlines some data science strategies that can be applied to tackle these specific challenges. This slide deck has been presented as a keynote speech at WISE 2022 in Biarritz, France. ]]>
The Web and social media are the environments where people post their content, opinions, activities, and resources. Therefore, a considerable amount of user-generated content is produced every day for a wide variety of purposes. On the other side, people live their everyday life immersed in the physical world, where society, economy, politics and personal relations continuously evolve. These two opposite and complementary environment are today fully integrated: they reflect each other and they interact with each other in a stronger and stronger way. Exploring and studying content and data coming from both environments offers a great opportunity to understand the ever evolving modern society, in terms of topics of interest, events, relations, and behaviour. In this speech I will discuss through business cases and socio-political scenarios how we can extract insights and understand reality by combining and analyzing data from the digital and physical world, so as to reach a better overall picture of reality itself. Along this path, we need to keep into account that reality is complex and varies in time, space and along many other dimensions, including societal and economic variables. The speech highlights the main challenges that need to be addressed and outlines some data science strategies that can be applied to tackle these specific challenges. This slide deck has been presented as a keynote speech at WISE 2022 in Biarritz, France. ]]> Wed, 02 Nov 2022 14:35:07 GMT /slideshow/exploring-the-biversea-trip-across-the-digital-and-physical-ecospheres/253969698 mbrambil@slideshare.net(mbrambil) Exploring the Bi-verse.�A trip across the digital and physical ecospheres mbrambil The Web and social media are the environments where people post their content, opinions, activities, and resources. Therefore, a considerable amount of user-generated content is produced every day for a wide variety of purposes. On the other side, people live their everyday life immersed in the physical world, where society, economy, politics and personal relations continuously evolve. These two opposite and complementary environment are today fully integrated: they reflect each other and they interact with each other in a stronger and stronger way. Exploring and studying content and data coming from both environments offers a great opportunity to understand the ever evolving modern society, in terms of topics of interest, events, relations, and behaviour. In this speech I will discuss through business cases and socio-political scenarios how we can extract insights and understand reality by combining and analyzing data from the digital and physical world, so as to reach a better overall picture of reality itself. Along this path, we need to keep into account that reality is complex and varies in time, space and along many other dimensions, including societal and economic variables. The speech highlights the main challenges that need to be addressed and outlines some data science strategies that can be applied to tackle these specific challenges. This slide deck has been presented as a keynote speech at WISE 2022 in Biarritz, France. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/exploring-biverse-wise-2022-221102143507-35d90d2d-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Web and social media are the environments where people post their content, opinions, activities, and resources. Therefore, a considerable amount of user-generated content is produced every day for a wide variety of purposes. On the other side, people live their everyday life immersed in the physical world, where society, economy, politics and personal relations continuously evolve. These two opposite and complementary environment are today fully integrated: they reflect each other and they interact with each other in a stronger and stronger way. Exploring and studying content and data coming from both environments offers a great opportunity to understand the ever evolving modern society, in terms of topics of interest, events, relations, and behaviour. In this speech I will discuss through business cases and socio-political scenarios how we can extract insights and understand reality by combining and analyzing data from the digital and physical world, so as to reach a better overall picture of reality itself. Along this path, we need to keep into account that reality is complex and varies in time, space and along many other dimensions, including societal and economic variables. The speech highlights the main challenges that need to be addressed and outlines some data science strategies that can be applied to tackle these specific challenges. This slide deck has been presented as a keynote speech at WISE 2022 in Biarritz, France.

Exploring the Bi-verse. A trip across the digital and physical ecospheres from Marco Brambilla

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0 Conversation graphs in Online Social Media /slideshow/conversation-graphs-in-online-social-media/248352054 conversationgraphsinonlinesocialmedia-210519152005
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.]]>
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.]]> Wed, 19 May 2021 15:20:05 GMT /slideshow/conversation-graphs-in-online-social-media/248352054 mbrambil@slideshare.net(mbrambil) Conversation graphs in Online Social Media mbrambil In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/conversationgraphsinonlinesocialmedia-210519152005-thumbnail.jpg?width=120&height=120&fit=bounds" /> In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.

Conversation graphs in Online Social Media from Marco Brambilla

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0 Trigger.eu: Cocteau game for policy making - introduction and demo /slideshow/triggereu-cocteau-game-for-policy-making-introduction-and-demo/241567606 cocteau-trigger-demo-210119230719
COCTEAU stands for "Co-Creating the European Union". It's a project supported by the European Union whose objective is to involve citizens to cooperate alongside policy makers, contributing to build a better future.]]>
COCTEAU stands for "Co-Creating the European Union". It's a project supported by the European Union whose objective is to involve citizens to cooperate alongside policy makers, contributing to build a better future.]]> Tue, 19 Jan 2021 23:07:18 GMT /slideshow/triggereu-cocteau-game-for-policy-making-introduction-and-demo/241567606 mbrambil@slideshare.net(mbrambil) Trigger.eu: Cocteau game for policy making - introduction and demo mbrambil COCTEAU stands for "Co-Creating the European Union". It's a project supported by the European Union whose objective is to involve citizens to cooperate alongside policy makers, contributing to build a better future. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cocteau-trigger-demo-210119230719-thumbnail.jpg?width=120&height=120&fit=bounds" /> COCTEAU stands for "Co-Creating the European Union". It's a project supported by the European Union whose objective is to involve citizens to cooperate alongside policy makers, contributing to build a better future.

Trigger.eu: Cocteau game for policy making - introduction and demo from Marco Brambilla

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0 Generation of Realistic Navigation Paths for Web Site Testing using RNNs and GANs /slideshow/generation-of-realistic-navigation-paths-for-web-site-testing-using-rnns-and-gans/235415400 silvio-pavanetto-presentation-short-no-audio-200611141557
A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs . Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed. The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely Recurrent Neural Network, which are very well suited to temporally evolving data Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years. We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good with respect to the two evaluation metrics adopted (BLEU and Human evaluation). Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site.]]>
A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs . Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed. The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely Recurrent Neural Network, which are very well suited to temporally evolving data Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years. We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good with respect to the two evaluation metrics adopted (BLEU and Human evaluation). Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site.]]> Thu, 11 Jun 2020 14:15:57 GMT /slideshow/generation-of-realistic-navigation-paths-for-web-site-testing-using-rnns-and-gans/235415400 mbrambil@slideshare.net(mbrambil) Generation of Realistic Navigation Paths for Web Site Testing using RNNs and GANs mbrambil A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs . Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed. The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely Recurrent Neural Network, which are very well suited to temporally evolving data Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years. We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good with respect to the two evaluation metrics adopted (BLEU and Human evaluation). Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/silvio-pavanetto-presentation-short-no-audio-200611141557-thumbnail.jpg?width=120&height=120&fit=bounds" /> A large audience of users and typically a long time frame are needed to produce sensible and useful log data, making it an expensive task. To address this limit, we propose a method that focuses on the generation of REALISTIC NAVIGATIONAL PATHS, i.e., web logs . Our approach is extremely relevant because it can at the same time tackle the problem of lack of publicly available data about web navigation logs, and also be adopted in industry for AUTOMATIC GENERATION OF REALISTIC TEST SETTINGS of Web sites yet to be deployed. The generation has been implemented using deep learning methods for generating more realistic navigation activities, namely Recurrent Neural Network, which are very well suited to temporally evolving data Generative Adversarial Network: neural networks aimed at generating new data, such as images or text, very similar to the original ones and sometimes indistinguishable from them, that have become increasingly popular in recent years. We run experiments using open data sets of weblogs as training, and we run tests for assessing the performance of the methods. Results in generating new weblog data are quite good with respect to the two evaluation metrics adopted (BLEU and Human evaluation). Our study is described in detail in the paper published at ICWE 2020 – International Conference on Web Engineering with DOI: 10.1007/978-3-030-50578-3. It’s available online on the Springer Web site.

Generation of Realistic Navigation Paths for Web Site Testing using RNNs and GANs from Marco Brambilla

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0 Analyzing rich club behavior in open source projects /slideshow/analyzing-rich-club-behavior-in-open-source-projects/166848120 analyzingrich-clubbehaviorinopensourceprojects-190827145202
The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis. ]]>
The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis. ]]> Tue, 27 Aug 2019 14:52:02 GMT /slideshow/analyzing-rich-club-behavior-in-open-source-projects/166848120 mbrambil@slideshare.net(mbrambil) Analyzing rich club behavior in open source projects mbrambil The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/analyzingrich-clubbehaviorinopensourceprojects-190827145202-thumbnail.jpg?width=120&height=120&fit=bounds" /> The network of collaborations in an open source project can reveal relevant emergent properties that influence its prospects of success. In this work, we analyze open source projects to determine whether they exhibit a rich-club behavior, i.e., a phenomenon where contributors with a high number of collaborations (i.e., strongly connected within the collaboration network) are likely to cooperate with other well-connected individuals. The presence or absence of a rich-club has an impact on the sustainability and robustness of the project. For this analysis, we build and study a dataset with the 100 most popular projects in GitHub, exploiting connectivity patterns in the graph structure of collaborations that arise from commits, issues and pull requests. Results show that rich-club behavior is present in all the projects, but only few of them have an evident club structure. We compute coefficients both for single source graphs and the overall interaction graph, showing that rich-club behavior varies across different layers of software development. We provide possible explanations of our results, as well as implications for further analysis.

Analyzing rich club behavior in open source projects from Marco Brambilla

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0 Analysis of On-line Debate �on Long-Running Political Phenomena.�The Brexit Case /slideshow/analysis-of-online-debate-on-longrunning-political-phenomenathe-brexit-case/156447928 brexit-ic2s2-190719121711
In this study, we demonstrate that the computational social science is important to understand people behavior in political phenomena, and based on the long-running Brexit debate analysis on Twitter, we predict the public stance, discussion topics, and we measure the involvement of automated accounts and politicians’ social media accounts.]]>
In this study, we demonstrate that the computational social science is important to understand people behavior in political phenomena, and based on the long-running Brexit debate analysis on Twitter, we predict the public stance, discussion topics, and we measure the involvement of automated accounts and politicians’ social media accounts.]]> Fri, 19 Jul 2019 12:17:11 GMT /slideshow/analysis-of-online-debate-on-longrunning-political-phenomenathe-brexit-case/156447928 mbrambil@slideshare.net(mbrambil) Analysis of On-line Debate �on Long-Running Political Phenomena.�The Brexit Case mbrambil In this study, we demonstrate that the computational social science is important to understand people behavior in political phenomena, and based on the long-running Brexit debate analysis on Twitter, we predict the public stance, discussion topics, and we measure the involvement of automated accounts and politicians’ social media accounts. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brexit-ic2s2-190719121711-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this study, we demonstrate that the computational social science is important to understand people behavior in political phenomena, and based on the long-running Brexit debate analysis on Twitter, we predict the public stance, discussion topics, and we measure the involvement of automated accounts and politicians’ social media accounts.

Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case from Marco Brambilla

]]> 451 4 https://cdn.slidesharecdn.com/ss_thumbnails/brexit-ic2s2-190719121711-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Community analysis using graph representation learning on social networks /slideshow/community-analysis-using-graph-representation-learning-on-social-networks/140439030 communityanalysisusinggraphrepresentationlearningonsocialnetworks-190411111132
In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.We exploit the network that builds around the brands by encoding it into a graph model.We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks.]]>
In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.We exploit the network that builds around the brands by encoding it into a graph model.We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks.]]> Thu, 11 Apr 2019 11:11:32 GMT /slideshow/community-analysis-using-graph-representation-learning-on-social-networks/140439030 mbrambil@slideshare.net(mbrambil) Community analysis using graph representation learning on social networks mbrambil In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.We exploit the network that builds around the brands by encoding it into a graph model.We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/communityanalysisusinggraphrepresentationlearningonsocialnetworks-190411111132-thumbnail.jpg?width=120&height=120&fit=bounds" /> In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people. This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.We exploit the network that builds around the brands by encoding it into a graph model.We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to. In the paper, we report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks.

Community analysis using graph representation learning on social networks from Marco Brambilla

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0 Available Data Science M.Sc. Thesis Proposals /slideshow/available-data-science-msc-thesis-proposals/133615547 brambilla-datascience-thesis-proposals-feb2019-190227221103
Possible thesis topics available at the Data Science Lab at Politecnico di Milano, DEIB department.]]>
Possible thesis topics available at the Data Science Lab at Politecnico di Milano, DEIB department.]]> Wed, 27 Feb 2019 22:11:03 GMT /slideshow/available-data-science-msc-thesis-proposals/133615547 mbrambil@slideshare.net(mbrambil) Available Data Science M.Sc. Thesis Proposals mbrambil Possible thesis topics available at the Data Science Lab at Politecnico di Milano, DEIB department. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brambilla-datascience-thesis-proposals-feb2019-190227221103-thumbnail.jpg?width=120&height=120&fit=bounds" /> Possible thesis topics available at the Data Science Lab at Politecnico di Milano, DEIB department.

Available Data Science M.Sc. Thesis Proposals from Marco Brambilla

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0 Data Cleaning for social media knowledge extraction /slideshow/data-cleaning-for-social-media-knowledge-extraction/101101050 kdweb2018-datacleaning-v3-180607091825
Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities. However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results. We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest. We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues. For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies.]]>
Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities. However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results. We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest. We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues. For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies.]]> Thu, 07 Jun 2018 09:18:25 GMT /slideshow/data-cleaning-for-social-media-knowledge-extraction/101101050 mbrambil@slideshare.net(mbrambil) Data Cleaning for social media knowledge extraction mbrambil Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities. However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results. We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest. We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues. For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kdweb2018-datacleaning-v3-180607091825-thumbnail.jpg?width=120&height=120&fit=bounds" /> Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities. However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results. We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest. We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues. For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies.

Data Cleaning for social media knowledge extraction from Marco Brambilla

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0 Iterative knowledge extraction from social networks. The Web Conference 2018 /mbrambil/iterative-knowledge-extraction-from-social-networks-the-web-conference-2018 iterativeknowledgeextractionfromsocialnetworkswww2018msmsocialmediaske-180426094454
Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?. This work was presented at The Web Conference 2018, MSM workshop.]]>
Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?. This work was presented at The Web Conference 2018, MSM workshop.]]> Thu, 26 Apr 2018 09:44:54 GMT /mbrambil/iterative-knowledge-extraction-from-social-networks-the-web-conference-2018 mbrambil@slideshare.net(mbrambil) Iterative knowledge extraction from social networks. The Web Conference 2018 mbrambil Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?. This work was presented at The Web Conference 2018, MSM workshop. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iterativeknowledgeextractionfromsocialnetworkswww2018msmsocialmediaske-180426094454-thumbnail.jpg?width=120&height=120&fit=bounds" /> Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?. This work was presented at The Web Conference 2018, MSM workshop.

Iterative knowledge extraction from social networks. The Web Conference 2018 from Marco Brambilla

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0 Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information. Comparison of three unsupervised approaches /mbrambil/driving-style-behaviour-analysis-comparison-of-unsupervised-techniques-hmm-hdp-dpmeans-ieee-bigdata drivingstylebehaviouranalysiscomparisonofunsupervisedtechniqueshmmhdpdpmeansieeebigdata-171212153449
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.]]>
Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.]]> Tue, 12 Dec 2017 15:34:49 GMT /mbrambil/driving-style-behaviour-analysis-comparison-of-unsupervised-techniques-hmm-hdp-dpmeans-ieee-bigdata mbrambil@slideshare.net(mbrambil) Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information. Comparison of three unsupervised approaches mbrambil Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/drivingstylebehaviouranalysiscomparisonofunsupervisedtechniqueshmmhdpdpmeansieeebigdata-171212153449-thumbnail.jpg?width=120&height=120&fit=bounds" /> Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage ``better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information. Comparison of three unsupervised approaches from Marco Brambilla

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0 Myths and challenges in knowledge extraction and analysis from human-generated content /slideshow/myths-and-challenges-in-knowledge-extraction-and-analysis-from-human-generated-content-brambilla/80015494 mythsandchallengesinknowledgeextractionandanalysisfromhuman-generatedcontentbrambilla-170921115827
For centuries, science (in German "Wissenschaft") has aimed to create ("schaften") new knowledge ("Wissen") from the observation of physical phenomena, their modelling, and empirical validation. Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is once again to capture and create knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to. The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.]]>
For centuries, science (in German "Wissenschaft") has aimed to create ("schaften") new knowledge ("Wissen") from the observation of physical phenomena, their modelling, and empirical validation. Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is once again to capture and create knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to. The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.]]> Thu, 21 Sep 2017 11:58:27 GMT /slideshow/myths-and-challenges-in-knowledge-extraction-and-analysis-from-human-generated-content-brambilla/80015494 mbrambil@slideshare.net(mbrambil) Myths and challenges in knowledge extraction and analysis from human-generated content mbrambil For centuries, science (in German "Wissenschaft") has aimed to create ("schaften") new knowledge ("Wissen") from the observation of physical phenomena, their modelling, and empirical validation. Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is once again to capture and create knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to. The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mythsandchallengesinknowledgeextractionandanalysisfromhuman-generatedcontentbrambilla-170921115827-thumbnail.jpg?width=120&height=120&fit=bounds" /> For centuries, science (in German "Wissenschaft") has aimed to create ("schaften") new knowledge ("Wissen") from the observation of physical phenomena, their modelling, and empirical validation. Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is once again to capture and create knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to. The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.

Myths and challenges in knowledge extraction and analysis from human-generated content from Marco Brambilla

]]> 2009 5 https://cdn.slidesharecdn.com/ss_thumbnails/mythsandchallengesinknowledgeextractionandanalysisfromhuman-generatedcontentbrambilla-170921115827-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Harvesting Knowledge from Social Networks: Extracting Typed Relationships among Entities /slideshow/harvesting-knowledge-from-social-networks-extracting-typed-relationships-among-entities/76657158 icwe2017-sowemine2017-brambilla-170605140026
Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for ex- ample social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relation- ships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora.]]>
Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for ex- ample social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relation- ships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora.]]> Mon, 05 Jun 2017 14:00:26 GMT /slideshow/harvesting-knowledge-from-social-networks-extracting-typed-relationships-among-entities/76657158 mbrambil@slideshare.net(mbrambil) Harvesting Knowledge from Social Networks: Extracting Typed Relationships among Entities mbrambil Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for ex- ample social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relation- ships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icwe2017-sowemine2017-brambilla-170605140026-thumbnail.jpg?width=120&height=120&fit=bounds" /> Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for ex- ample social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relation- ships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora.

Harvesting Knowledge from Social Networks: Extracting Typed Relationships among Entities from Marco Brambilla

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0 Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns. ICEIS 2017 /slideshow/modeldriven-development-of-user-interfaces-for-iot-via-domainspecific-components-patterns-iceis-2017/75463682 iceis-brambilla-umuhoza-small-170427110836
Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed. For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research. On the contrary, user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters. In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT\ applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML.]]>
Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed. For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research. On the contrary, user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters. In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT\ applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML.]]> Thu, 27 Apr 2017 11:08:36 GMT /slideshow/modeldriven-development-of-user-interfaces-for-iot-via-domainspecific-components-patterns-iceis-2017/75463682 mbrambil@slideshare.net(mbrambil) Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns. ICEIS 2017 mbrambil Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed. For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research. On the contrary, user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters. In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT\ applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iceis-brambilla-umuhoza-small-170427110836-thumbnail.jpg?width=120&height=120&fit=bounds" /> Internet of Things technologies and applications are evolving and continuously gaining traction in all fields and environments, including homes, cities, services, industry and commercial enterprises. However, still many problems need to be addressed. For instance, the IoT vision is mainly focused on the technological and infrastructure aspect, and on the management and analysis of the huge amount of generated data, while so far the development of front-end and user interfaces for IoT has not played a relevant role in research. On the contrary, user interfaces in the IoT ecosystem they can play a key role in the acceptance of solutions by final adopters. In this paper we present a model-driven approach to the design of IoT interfaces, by defining a specific visual design language and design patterns for IoT\ applications, and we show them at work. The language we propose is defined as an extension of the OMG standard language called IFML.

Model-driven Development of User Interfaces for IoT via Domain-specific Components & Patterns. ICEIS 2017 from Marco Brambilla

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0 A Model-Based Method for Seamless Web and Mobile Experience. Splash 2016 conf. /slideshow/a-modelbased-method-for-seamless-web-and-mobile-experience-splash-2016-conf/67911763 mobile-wshop-splash2016-mobml-ifml-161031083714
Consumer-centered software applications nowadays are required to be available both as mobile and desktop versions. However, the app design is frequently made only for one of the two (i.e., mobile first or web first) while missing an appropriate design for the other (which, in turn, simply mimics the interaction of the first one). This results into poor quality of the interaction on one or the other platform. Current solutions would require different designs, to be realized through different design methods and tools, and that may require to double development and maintenance costs. In order to mitigate such an issue, this paper proposes a novel approach that supports the design of both web and mobile applications at once. Starting from a unique requirement and business specification, where web– and mobile–specific aspects are captured through tagging, we derive a platform independent design of the system specified in IFML. This model is subsequently refined and detailed for the two platforms, and used to automatically generate both the web and mobile versions. If more precise interactions are needed for the mobile part, a blending with MobML, a mobile-specific modeling language, is devised. Full traceability of the relations between artifacts is granted.]]>
Consumer-centered software applications nowadays are required to be available both as mobile and desktop versions. However, the app design is frequently made only for one of the two (i.e., mobile first or web first) while missing an appropriate design for the other (which, in turn, simply mimics the interaction of the first one). This results into poor quality of the interaction on one or the other platform. Current solutions would require different designs, to be realized through different design methods and tools, and that may require to double development and maintenance costs. In order to mitigate such an issue, this paper proposes a novel approach that supports the design of both web and mobile applications at once. Starting from a unique requirement and business specification, where web– and mobile–specific aspects are captured through tagging, we derive a platform independent design of the system specified in IFML. This model is subsequently refined and detailed for the two platforms, and used to automatically generate both the web and mobile versions. If more precise interactions are needed for the mobile part, a blending with MobML, a mobile-specific modeling language, is devised. Full traceability of the relations between artifacts is granted.]]> Mon, 31 Oct 2016 08:37:14 GMT /slideshow/a-modelbased-method-for-seamless-web-and-mobile-experience-splash-2016-conf/67911763 mbrambil@slideshare.net(mbrambil) A Model-Based Method for Seamless Web and Mobile Experience. Splash 2016 conf. mbrambil Consumer-centered software applications nowadays are required to be available both as mobile and desktop versions. However, the app design is frequently made only for one of the two (i.e., mobile first or web first) while missing an appropriate design for the other (which, in turn, simply mimics the interaction of the first one). This results into poor quality of the interaction on one or the other platform. Current solutions would require different designs, to be realized through different design methods and tools, and that may require to double development and maintenance costs. In order to mitigate such an issue, this paper proposes a novel approach that supports the design of both web and mobile applications at once. Starting from a unique requirement and business specification, where web– and mobile–specific aspects are captured through tagging, we derive a platform independent design of the system specified in IFML. This model is subsequently refined and detailed for the two platforms, and used to automatically generate both the web and mobile versions. If more precise interactions are needed for the mobile part, a blending with MobML, a mobile-specific modeling language, is devised. Full traceability of the relations between artifacts is granted. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mobile-wshop-splash2016-mobml-ifml-161031083714-thumbnail.jpg?width=120&height=120&fit=bounds" /> Consumer-centered software applications nowadays are required to be available both as mobile and desktop versions. However, the app design is frequently made only for one of the two (i.e., mobile first or web first) while missing an appropriate design for the other (which, in turn, simply mimics the interaction of the first one). This results into poor quality of the interaction on one or the other platform. Current solutions would require different designs, to be realized through different design methods and tools, and that may require to double development and maintenance costs. In order to mitigate such an issue, this paper proposes a novel approach that supports the design of both web and mobile applications at once. Starting from a unique requirement and business specification, where web– and mobile–specific aspects are captured through tagging, we derive a platform independent design of the system specified in IFML. This model is subsequently refined and detailed for the two platforms, and used to automatically generate both the web and mobile versions. If more precise interactions are needed for the mobile part, a blending with MobML, a mobile-specific modeling language, is devised. Full traceability of the relations between artifacts is granted.

A Model-Based Method for Seamless Web and Mobile Experience. Splash 2016 conf. from Marco Brambilla

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0 Big Data and Stream Data Analysis at Politecnico di Milano /slideshow/big-data-and-stream-data-analysis-at-politecnico-di-milano/67491008 bigdataanalysis-brambilla-dellavalle-161021094044
Problems, Techniques, Scenarios and Partners. Covering social media, IoT, smart city and many more cases. By Marco Brambilla and Emanuele Della Valle.]]>
Problems, Techniques, Scenarios and Partners. Covering social media, IoT, smart city and many more cases. By Marco Brambilla and Emanuele Della Valle.]]> Fri, 21 Oct 2016 09:40:44 GMT /slideshow/big-data-and-stream-data-analysis-at-politecnico-di-milano/67491008 mbrambil@slideshare.net(mbrambil) Big Data and Stream Data Analysis at Politecnico di Milano mbrambil Problems, Techniques, Scenarios and Partners. Covering social media, IoT, smart city and many more cases. By Marco Brambilla and Emanuele Della Valle. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdataanalysis-brambilla-dellavalle-161021094044-thumbnail.jpg?width=120&height=120&fit=bounds" /> Problems, Techniques, Scenarios and Partners. Covering social media, IoT, smart city and many more cases. By Marco Brambilla and Emanuele Della Valle.

Big Data and Stream Data Analysis at Politecnico di Milano from Marco Brambilla

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0 https://cdn.slidesharecdn.com/profile-photo-mbrambil-48x48.jpg?cb=1726730407 Marco Brambilla is a full professor at Politecnico di Milano. His two main research tracks are: (1) data science and big data analytics; and (2) model driven engineering and development. On big data analytics, he is running research projects on large-scale data management and data fusion in multiple contexts, spanning social media, smart city, utility and mobility, commercial events, and cultural/art initiatives. On model-driven engineering, he works on MDD, domain-specific languages (DSL), web application design, big data analytics, data science and computational intelligence for social networks, IoT, crowdsourcing, and search engines. He authored the OMG standard IFML. home.dei.polimi.it/mbrambil https://cdn.slidesharecdn.com/ss_thumbnails/smbud-04-data-architectures-240924210026-7919f784-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/essential-concepts-of-data-architectures/271999110 Essential concepts of ... https://cdn.slidesharecdn.com/ss_thumbnails/thesis-proposals-2024-231221073444-c945b49d-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/msc-thesis-topics-and-proposals-polimi-data-science-lab-2024-prof-brambilla-marco/264819563 M.Sc. Thesis Topics an... https://cdn.slidesharecdn.com/ss_thumbnails/thesis-topics-and-proposals-brambilla-2023-231221072715-7bcfb0fb-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/thesis-topics-and-proposals-polimi-data-science-lab-2023-prof-brambilla-marco/264819340 Thesis Topics and Prop...