2022 – 2025
Unit WP Leader for SoBigData.it: Strengthening the Italian RI for Social Mining and Big Data Analytics.
Some related scientific publications are given below.
1.
Franco Cicirelli Irfanullah Khan, Emilio Greco
Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings Journal Article
In: Internet Of Things, 2024, ISSN: 2542-6605.
@article{Khan2024,
title = {Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings},
author = {Irfanullah Khan, Franco Cicirelli, Emilio Greco, Antonio Guerrieri, Carlo Mastroianni, Luigi Scarcello, Giandomenico Spezzano, Andrea Vinci},
doi = {10.1016/j.iot.2024.101181},
issn = {2542-6605},
year = {2024},
date = {2024-04-07},
urldate = {2024-04-07},
journal = {Internet Of Things},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2.
Cesario, Eugenio; Lindia, Paolo; Vinci, Andrea
A scalable multi-density clustering approach to detect city hotspots in a smart city Journal Article
In: Future Generation Computer Systems, vol. 157, pp. 226-236, 2024, ISSN: 0167-739X.
@article{CESARIO2024,
title = {A scalable multi-density clustering approach to detect city hotspots in a smart city},
author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24001122},
doi = {https://doi.org/10.1016/j.future.2024.03.042},
issn = {0167-739X},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Future Generation Computer Systems},
volume = {157},
pages = {226-236},
abstract = {In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.
3.
Mastroianni, Carlo; Plastina, Francesco; Scarcello, Luigi; Settino, Jacopo; Vinci, Andrea
Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community Journal Article
In: IEEE Trans. Smart Grid, vol. 15, no. 1, pp. 444–456, 2024, ISSN: 1949-3061.
@article{Mastroianni2024,
title = {Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community},
author = {Carlo Mastroianni and Francesco Plastina and Luigi Scarcello and Jacopo Settino and Andrea Vinci},
doi = {10.1109/tsg.2023.3286106},
issn = {1949-3061},
year = {2024},
date = {2024-01-00},
urldate = {2024-01-00},
journal = {IEEE Trans. Smart Grid},
volume = {15},
number = {1},
pages = {444–456},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
Cesario, Eugenio; Lindia, Paolo; Vinci, Andrea
Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications Journal Article
In: Big Data and Cognitive Computing, vol. 7, no. 1, 2023, ISSN: 2504-2289.
@article{Cesario2023,
title = {Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications},
author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},
doi = {10.3390/bdcc7010029},
issn = {2504-2289},
year = {2023},
date = {2023-03-00},
urldate = {2023-03-00},
journal = {Big Data and Cognitive Computing},
volume = {7},
number = {1},
publisher = {MDPI AG},
abstract = {<jats:p>Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.</jats:p>