2023
Conference article
Open Access
Demo: an interactive visualization combining rule-based and feature importance explanations
Cappuccio E., Fadda D., Lanzilotti R., Rinzivillo S.
The Human-Computer Interaction (HCI) community has long stressed the need for a more user-centered approach to Explainable Artificial Intelligence (XAI), a research area that aims at defining algorithms and tools to illustrate the predictions of the so-called black-box models. This approach can benefit from the fields of user-interface, user experience, and visual analytics. In this demo, we propose a visual-based tool, "F.I.P.E.R.", that shows interactive explanations combining rules and feature importance.Source: CHItaly 2023: 15th Biannual Conference of the Italian SIGCHI Chapter, Torino, Italy, 20-22/09/2023
DOI: 10.1145/3605390.3610811
Project(s): XAI
Metrics:
Cappuccio E., Fadda D., Lanzilotti R., Rinzivillo S.
The Human-Computer Interaction (HCI) community has long stressed the need for a more user-centered approach to Explainable Artificial Intelligence (XAI), a research area that aims at defining algorithms and tools to illustrate the predictions of the so-called black-box models. This approach can benefit from the fields of user-interface, user experience, and visual analytics. In this demo, we propose a visual-based tool, "F.I.P.E.R.", that shows interactive explanations combining rules and feature importance.Source: CHItaly 2023: 15th Biannual Conference of the Italian SIGCHI Chapter, Torino, Italy, 20-22/09/2023
DOI: 10.1145/3605390.3610811
Project(s): XAI
Metrics:
See at:
ISTI Repository 
| dl.acm.org 
| CNR ExploRA
2023
Journal article
Open Access
Benchmarking and survey of explanation methods for black box models
Bodria F., Giannotti F., Guidotti R., Naretto F., Pedreschi D., Rinzivillo S.
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics.Source: Data mining and knowledge discovery (2023): 1719–1778. doi:10.1007/s10618-023-00933-9
DOI: 10.1007/s10618-023-00933-9
DOI: 10.48550/arxiv.2102.13076
Project(s): TAILOR, HumanE-AI-Net , XAI , SoBigData-PlusPlus
Metrics:
Bodria F., Giannotti F., Guidotti R., Naretto F., Pedreschi D., Rinzivillo S.
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics.Source: Data mining and knowledge discovery (2023): 1719–1778. doi:10.1007/s10618-023-00933-9
DOI: 10.1007/s10618-023-00933-9
DOI: 10.48550/arxiv.2102.13076
Project(s): TAILOR
, HumanE-AI-Net , XAI , SoBigData-PlusPlus Metrics:
See at:
Data Mining and Knowledge Discovery | link.springer.com | ISTI Repository | doi.org | CNR ExploRA
2023
Journal article
Open Access
Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning
Metta C., Beretta A., Guidotti R., Yin Y., Gallinari P., Rinzivillo S., Giannotti F.
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.Source: International Journal of Data Science and Analytics (Print) (2023). doi:10.1007/s41060-023-00401-z
DOI: 10.1007/s41060-023-00401-z
Project(s): TAILOR, HumanE-AI-Net , XAI , SoBigData-PlusPlus
Metrics:
Metta C., Beretta A., Guidotti R., Yin Y., Gallinari P., Rinzivillo S., Giannotti F.
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.Source: International Journal of Data Science and Analytics (Print) (2023). doi:10.1007/s41060-023-00401-z
DOI: 10.1007/s41060-023-00401-z
Project(s): TAILOR
, HumanE-AI-Net , XAI , SoBigData-PlusPlus Metrics:
See at:
International Journal of Data Science and Analytics | link.springer.com | ISTI Repository | CNR ExploRA
2023
Contribution to journal
Open Access
Explainable AI. Introduction to the Special Theme
Veerappa M., Rinzivillo S.
Source: ERCIM news online edition 134 (2023): 8–9.
Veerappa M., Rinzivillo S.
Source: ERCIM news online edition 134 (2023): 8–9.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Trustworthy AI at KDD Lab
Giannotti F., Guidotti R., Monreale A., Pappalardo L., Pedreschi D., Pellungrini R., Pratesi F., Rinzivillo S., Ruggieri S., Setzu M., Deluca R.
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: Ital-IA 2023, pp. 388–393, Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
Giannotti F., Guidotti R., Monreale A., Pappalardo L., Pedreschi D., Pellungrini R., Pratesi F., Rinzivillo S., Ruggieri S., Setzu M., Deluca R.
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: Ital-IA 2023, pp. 388–393, Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
EXPHLOT: explainable privacy assessment for human location trajectories
Naretto F., Pellungrini R., Rinzivillo S., Fadda D.
Human mobility data play a crucial role in understand- ing mobility patterns and developing analytical services across various domains such as urban planning, transportation, and public health. How- ever, due to the sensitive nature of this data, accurately identifying pri- vacy risks is essential before deciding to release it to the public. Recent work has proposed the use of machine learning models for predicting privacy risk on raw mobility trajectories and the use of shap for risk explanation. However, applying shap to mobility data results in expla- nations that are of limited use both for privacy experts and end-users. In this work, we present a novel version of the Expert privacy risk predic- tion and explanation framework specifically tailored for human mobility data. We leverage state-of-the-art algorithms in time series classification, as Rocket and InceptionTime, to improve risk prediction while reduc- ing computation time. Additionally, we address two key issues with shap explanation on mobility data: first, we devise an entropy-based mask to efficiently compute shap values for privacy risk in mobility data; second, we develop a module for interactive analysis and visualization of shap values over a map, empowering users with an intuitive understanding of shap values and privacy risk.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 325–340, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_22
Project(s): TAILOR, HumanE-AI-Net , XAI , SoBigData-PlusPlus
Metrics:
Naretto F., Pellungrini R., Rinzivillo S., Fadda D.
Human mobility data play a crucial role in understand- ing mobility patterns and developing analytical services across various domains such as urban planning, transportation, and public health. How- ever, due to the sensitive nature of this data, accurately identifying pri- vacy risks is essential before deciding to release it to the public. Recent work has proposed the use of machine learning models for predicting privacy risk on raw mobility trajectories and the use of shap for risk explanation. However, applying shap to mobility data results in expla- nations that are of limited use both for privacy experts and end-users. In this work, we present a novel version of the Expert privacy risk predic- tion and explanation framework specifically tailored for human mobility data. We leverage state-of-the-art algorithms in time series classification, as Rocket and InceptionTime, to improve risk prediction while reduc- ing computation time. Additionally, we address two key issues with shap explanation on mobility data: first, we devise an entropy-based mask to efficiently compute shap values for privacy risk in mobility data; second, we develop a module for interactive analysis and visualization of shap values over a map, empowering users with an intuitive understanding of shap values and privacy risk.Source: DS 2023 - 26th International Conference on Discovery Science, pp. 325–340, Porto, Portugal, 09-11/10/2023
DOI: 10.1007/978-3-031-45275-8_22
Project(s): TAILOR
, HumanE-AI-Net , XAI , SoBigData-PlusPlus Metrics:
See at:
doi.org | link.springer.com | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Co-design of human-centered, explainable AI for clinical decision support
Panigutti C., Beretta A., Fadda D., Giannotti F., Pedreschi D., Perotti A., Rinzivillo S.
eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback with a two-fold outcome: First, we obtain evidence that explanations increase users' trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so we can re-design a better, more human-centered explanation interface.Source: ACM transactions on interactive intelligent systems (Online) 13 (2023). doi:10.1145/3587271
DOI: 10.1145/3587271
Project(s): HumanE-AI-Net, XAI
Metrics:
Panigutti C., Beretta A., Fadda D., Giannotti F., Pedreschi D., Perotti A., Rinzivillo S.
eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback with a two-fold outcome: First, we obtain evidence that explanations increase users' trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so we can re-design a better, more human-centered explanation interface.Source: ACM transactions on interactive intelligent systems (Online) 13 (2023). doi:10.1145/3587271
DOI: 10.1145/3587271
Project(s): HumanE-AI-Net
, XAI Metrics:
See at:
dl.acm.org | Archivio istituzionale della Ricerca - Scuola Normale Superiore | ISTI Repository | ACM Transactions on Interactive Intelligent Systems | CNR ExploRA
2022
Conference article
Open Access
Exemplars and counterexemplars explanations for skin lesion classifiers
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.Source: HHAI2022 - Augmenting Human Intellect, pp. 258–260, Amsterdam, The Netherlands, 13-17/07/2022
DOI: 10.3233/faia220209
Project(s): HumanE-AI-Net
Metrics:
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.Source: HHAI2022 - Augmenting Human Intellect, pp. 258–260, Amsterdam, The Netherlands, 13-17/07/2022
DOI: 10.3233/faia220209
Project(s): HumanE-AI-Net
Metrics:
See at:
ebooks.iospress.nl | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087
DOI: 10.1371/journal.pcbi.1009087
Metrics:
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: PLoS computational biology 17 (2021). doi:10.1371/journal.pcbi.1009087
DOI: 10.1371/journal.pcbi.1009087
Metrics:
See at:
journals.plos.org | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comande G., Conti M., Cote M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D. M., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Ethics and information technology 23 (2021). doi:10.1007/s10676-020-09572-w
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comande G., Conti M., Cote M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D. M., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates--if and when they want and for specific aims--with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Ethics and information technology 23 (2021). doi:10.1007/s10676-020-09572-w
DOI: 10.1007/s10676-020-09572-w
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Aaltodoc Publication Archive | Aaltodoc Publication Archive | Ethics and Information Technology | Ethics and Information Technology | Recolector de Ciencia Abierta, RECOLECTA | Archivio Istituzionale | link.springer.com | Ethics and Information Technology | City Research Online | ISTI Repository | Online Research Database In Technology | NARCIS | NARCIS | Digitala Vetenskapliga Arkivet - Academic Archive On-line | Publikationer från Umeå universitet | NARCIS | kclpure.kcl.ac.uk | Fraunhofer-ePrints | Fraunhofer-ePrints | publons.com | www.scopus.com | CNR ExploRA
2021
Conference article
Open Access
Exemplars and counterexemplars explanations for image classifiers, targeting skin lesion labeling
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The latter is able to offer the practitioner exemplars and counterexemplars for the classification diagnosis thus allowing the physician to interact with the automatic diagnosis system. The exemplars are generated via an adversarial autoencoder. We illustrate the behavior of the system on representative examples.Source: ISCC 2021 - IEEE Symposium on Computers and Communications, Athens, Greece, 5-8/09/2021
DOI: 10.1109/iscc53001.2021.9631485
Project(s): AI4EU, TAILOR , HumanE-AI-Net
Metrics:
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The latter is able to offer the practitioner exemplars and counterexemplars for the classification diagnosis thus allowing the physician to interact with the automatic diagnosis system. The exemplars are generated via an adversarial autoencoder. We illustrate the behavior of the system on representative examples.Source: ISCC 2021 - IEEE Symposium on Computers and Communications, Athens, Greece, 5-8/09/2021
DOI: 10.1109/iscc53001.2021.9631485
Project(s): AI4EU
, TAILOR , HumanE-AI-Net Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Report
Open Access
Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020
DOI: 10.32079/isti-tr-2020/005
Metrics:
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020
DOI: 10.32079/isti-tr-2020/005
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comandé G., Conti M., Coté M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Van Den Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Transactions on data privacy 13 (2020): 61–66.
Nanni M., Andrienko G., Barabasi A. -L., Boldrini C., Bonchi F., Cattuto C., Chiaromonte F., Comandé G., Conti M., Coté M., Dignum F., Dignum V., Domingo-Ferrer J., Ferragina P., Giannotti F., Guidotti R., Helbing D., Kaski K., Kertesz J., Lehmann S., Lepri B., Lukowicz P., Matwin S., Jimenez D., Monreale A., Morik K., Oliver N., Passarella A., Passerini A., Pedreschi D., Pentland A., Pianesi F., Pratesi F., Rinzivillo S., Ruggieri S., Siebes A., Torra V., Trasarti R., Van Den Hoven J., Vespignani A.
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.Source: Transactions on data privacy 13 (2020): 61–66.
See at:
ISTI Repository | www.tdp.cat | CNR ExploRA
2020
Report
Open Access
The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Cintia P., Fadda D., Giannotti F., Pappalardo L., Rossetti G., Pedreschi D., Rinzivillo S., Bonato P., Fabbri F., Penone F., Bavarese M., Checchi D., Chiaromonte F., Vineis P., Gazzetta G., Riccardo F., Marziano V., Poletti P., Trentini F., Bella A., Xanthi A., Del Manso M., Fabiani M., Bellino S., Boros S., Urdiales A. M., Vescia M. F., Brusaferro S., Rezza G., Pezzotti P., Ajelli M., Merler S.
We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.Project(s): SoBigData
Cintia P., Fadda D., Giannotti F., Pappalardo L., Rossetti G., Pedreschi D., Rinzivillo S., Bonato P., Fabbri F., Penone F., Bavarese M., Checchi D., Chiaromonte F., Vineis P., Gazzetta G., Riccardo F., Marziano V., Poletti P., Trentini F., Bella A., Xanthi A., Del Manso M., Fabiani M., Bellino S., Boros S., Urdiales A. M., Vescia M. F., Brusaferro S., Rezza G., Pezzotti P., Ajelli M., Merler S.
We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.Project(s): SoBigData
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2020
Report
Open Access
Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/009, 2020
DOI: 10.32079/isti-tr-2020/009
Project(s): SoBigData-PlusPlus
Metrics:
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/009, 2020
DOI: 10.32079/isti-tr-2020/009
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
2019
Report
Open Access
ISTI Young Researcher Award "Matteo Dellepiane" - Edition 2019
Barsocchi P., Candela L., Crivello A., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Malomo L., Moroni D., Nardini F. M., Pappalardo L., Rinzivillo S., Rossetti G., Robol L.
The ISTI Young Researcher Award (YRA) selects yearly the best young staff members working at Institute of Information Science and Technologies (ISTI). This award focuses on quality and quantity of the scientific production. In particular, the award is granted to the best young staff members (less than 35 years old) by assessing their scientific production in the year preceding the award. This report documents the selection procedure and the results of the 2019 YRA edition. From the 2019 edition on the award is named as "Matteo Dellepiane", being dedicated to a bright ISTI researcher who prematurely left us and who contributed a lot to the YRA initiative from its early start.Source: ISTI Technical reports, 2019
Barsocchi P., Candela L., Crivello A., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Malomo L., Moroni D., Nardini F. M., Pappalardo L., Rinzivillo S., Rossetti G., Robol L.
The ISTI Young Researcher Award (YRA) selects yearly the best young staff members working at Institute of Information Science and Technologies (ISTI). This award focuses on quality and quantity of the scientific production. In particular, the award is granted to the best young staff members (less than 35 years old) by assessing their scientific production in the year preceding the award. This report documents the selection procedure and the results of the 2019 YRA edition. From the 2019 edition on the award is named as "Matteo Dellepiane", being dedicated to a bright ISTI researcher who prematurely left us and who contributed a lot to the YRA initiative from its early start.Source: ISTI Technical reports, 2019
See at:
ISTI Repository | CNR ExploRA
2019
Contribution to book
Open Access
Analysis and visualization of performance indicators in university admission tests
Natilli M., Fadda D., Rinzivillo S., Pedreschi D., Licari F.
This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the ques- tion or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the compo- sition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.Source: Formal Methods. FM 2019 International Workshops, edited by Emil Sekerinski et al..., pp. 186–199, 2019
DOI: 10.1007/978-3-030-54994-7_14
Metrics:
Natilli M., Fadda D., Rinzivillo S., Pedreschi D., Licari F.
This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the ques- tion or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the compo- sition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.Source: Formal Methods. FM 2019 International Workshops, edited by Emil Sekerinski et al..., pp. 186–199, 2019
DOI: 10.1007/978-3-030-54994-7_14
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2019
Conference article
Open Access
A visual analytics platform to measure performance on university entrance tests
Boncoraglio D., Deri F., Distefano F., Fadda D., Filippi G., Forte G., Licari F., Natilli M., Pedreschi D., Rinzivillo S.
Data visualization dashboards provide an efficient approach that helps to improve the ability to understand the information behind complex databases. It is possible with such tools to create new insights, to represent keys indicators of the activity, to communicate (in real-time) snapshots of the state of the work. In this paper, we present a visual analytics platform created for the exploration and analysis of performance data on entrance tests taken by Italian students when entering the university career. The data is provided by CISIA (Consorzio Interuniversitario Sistemi Integrati per l'Accesso), a non-profit consortium formed exclusively by public universities. With this platform, it is possible to explore the performance of the students along different dimensions, such as gender, high school of provenience, type of test and so on.Source: 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia, Grosseto, Italy (Grosseto), Italy, 16-19 June 2019
Boncoraglio D., Deri F., Distefano F., Fadda D., Filippi G., Forte G., Licari F., Natilli M., Pedreschi D., Rinzivillo S.
Data visualization dashboards provide an efficient approach that helps to improve the ability to understand the information behind complex databases. It is possible with such tools to create new insights, to represent keys indicators of the activity, to communicate (in real-time) snapshots of the state of the work. In this paper, we present a visual analytics platform created for the exploration and analysis of performance data on entrance tests taken by Italian students when entering the university career. The data is provided by CISIA (Consorzio Interuniversitario Sistemi Integrati per l'Accesso), a non-profit consortium formed exclusively by public universities. With this platform, it is possible to explore the performance of the students along different dimensions, such as gender, high school of provenience, type of test and so on.Source: 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia, Grosseto, Italy (Grosseto), Italy, 16-19 June 2019
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2019
Conference article
Open Access
Learning data mining
Guidotti R., Monreale A., Rinzivillo S.
In the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.Source: DSAA, pp. 361–370, Turin, Italy, 1-4/10/2018
DOI: 10.1109/dsaa.2018.00047
Project(s): SoBigData
Metrics:
Guidotti R., Monreale A., Rinzivillo S.
In the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.Source: DSAA, pp. 361–370, Turin, Italy, 1-4/10/2018
DOI: 10.1109/dsaa.2018.00047
Project(s): SoBigData
Metrics:
See at:
arpi.unipi.it | doi.org | ieeexplore.ieee.org | CNR ExploRA
2018
Contribution to book
Open Access
How data mining and machine learning evolved from relational data base to data science
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
See at:
arpi.unipi.it | ISTI Repository | doi.org | link.springer.com | CNR ExploRA