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Special Issue "Big Data Analytics for Smart Cities"
Deadline for manuscript submissions: 30 August 2020.
Interests: data science, automated data analytics, transparent data mining, machine learning, text mining, concept drift methodologies, digital cities predictive maintenance
Interests: data management; spatiotemporal information systems; big data and analytics; collaborative and distributed architectures; blockchain technology
Special Issues and Collections in MDPI journals
Special Issue in ISPRS International Journal of Geo-Information: Spatial Data Infrastructure for Distributed Management and Processing
Special Issue in Big Data and Cognitive Computing: Advanced Data Mining Techniques for IoT and Big Data
In the last few years, cities have become engines of wealth creation thanks to the advent of the new information and communication. The capability to both generate and collect the data of public interest within the urban area (e.g., information about social events, public service usage, and mobility) has increased at an unprecedented rate, to such an extent that data rapidly scales towards big (urban) data. Such abundance creates an unprecedented opportunity to understand the way people interact in and with the urban environment, and enables researchers to tackle important and urgent urban challenges (e.g., traffic congestion, air pollution, and energy sustainability) by adding intelligence to the urban environment.
The design and development of innovative services and solutions tailored to smart cities entails the acquisition, integration, and analysis of heterogeneous data (e.g., social network data, urban safety and security perception, mobility data, energy consumption data, and data that may increase citizen awareness on the urban environment). To collect, store, manage, and analyze data, as well as visualize the results of the data analysis process, in order to make them readable and usable by citizens, ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods should be devised.
We invite the submission of high-quality manuscripts reporting relevant research addressing various aspects of urban data analytics. Contributions to this Special Issue should be of interest to a large and varied cross-disciplinary audience of researchers and practitioners, involved or interested in different perspectives of this topic. The Special Issue welcomes the submission of technical, experimental, and methodological papers; application papers, and papers on experience reports in real-life urban settings in one of, although not limited to, the following application scenarios:
- Public safety and security
- Air quality
- Energy consumption awareness
- Citizens' mobility
- User-generated content (such as tweets, micro-blogs, check-ins, and photos)
- Intelligent street furniture
Prof. Tania Cerquitelli
Dr. Sara Migliorini
Prof. SIlvia Chiusano
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- machine learning
- urban data analytics
- citizen-centered perspective
- proactive citizen engagement
- transparent urban analytics
- cross- and inter-disciplinary methodologies
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Explaining Hotel Reviews by means of Open Data Enrichment and Deep Natural Language Processing
Luca Cagliero ([email protected], Politecnico di Torino, Italy), Moreno La Quatra and Daniele Apiletti
Abstract: A large portion of textual user-generated content on the web consists of opinions and reviews on products, services, and places. Many travellers and tourists routinely rely on such content to drive their choices, shaping trips and visits to any place on earth, and specifically to select hotels in large cities. Most works on textual review analysis have focused on integrating, summarizing, and ranking entities with respect to predefined criteria, such as quality, location, overall experience. Given both the textual review and the review scores, the paper describes a novel approach to explaining review scores by means of deep NLP techniques applied to the text of the reviews. To gain insights into user choices of hotel and feedbacks, the outcomes of Deep NLP models are correlated with open data describing the context of the city surrounding each hotel. The experiments performed on public TripAdvisor hotel-review datasets of two popular cities show novel findings in user behavior and hotel features.
On Car Sharing Usage Prediction with Socio-demografic and Open Data
Michele Cocca, Douglas Texiera, Luca Vassio ([email protected], Politecnico di Torino, Italy), Jussara M. Almeida and Marco Mellia
Abstract: Free Floating Car Sharing (FFCS) services are a flexible alternative to car ownership. As other transportation services, FFCS has to deal with dynamic usage patterns: service demands greatly vary across time and across different regions of a city reflecting the influence of demand seasonality, weather and socio-demographic diversity. Therefore, accurate forecast of FFCS demand is of great importance to an adequate provisioning of the service fleet. In this article, we study the problem of predicting FFCS usage patterns to i) forecast the number of rentals over time, and ii) the demand over different zones of the city. We collect 14 months of real rides in Vancouver (Canada), obtaining over 1 million FFCS trips, that we use as our ground truth. To predict the usage patterns, we consider classical time series analysis and machine learning tools like SVM, Random Forests, and Neural Networks. We consider either historical data, or just socio-demographic data that are readily available, even in areas where past data is not present. Our aim is to offer a thorough comparison in term of accuracy, easiness of training, and usage. Results shows that it is possible to predict both the long and short term temporal patterns with mean relative errors down to 11%. When solving the spatial prediction problem, we also investigate which characteristics affect the most the possible FFCS usage, providing interesting insights for the service providers.
Correlating espresso quality with coffe-machine parameters by means of association rule mining
Daniele Apiletti ([email protected], Politecnico di Torino, Italy), Eliana Pastor
Abstract: Coffee is among the most popular beverages in major cities all over the world, being both the core of widespread business activities and a long-standing tradition of recreational and social value. Among the many coffee variants, espresso attracts the interest of many stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries, the quality of the espresso is among the most discussed and investigated issues. The current work analyzes a real-world dataset of espresso brewing by professional coffee-making machines, and identifies correlations among external quality-influencing variables and common thresholds used to determine the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the dose grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights on the espresso quality and its impact on both the life of billions of consumers and the choices of coffee-making industries.
Task-agnostic Object Recognition for Mobile Robots through Few-shot Image Matching
Agnese Chiatti Gianluca Bardaro Enrico Motta, Emanuele Bastianelli, Ilaria Tiddi, Prasenjit Mitra
Corresponding author: Agnese Chiatti <[email protected]>
Abstract: To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario, because they require that: (i) all target object classes are known beforehand, and (ii) a very large number of training examples is provided for each class. This calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here we compare diff erent (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. This configuration allowed us to remain agnostic with respect to both specic end tasks (e.g., stowing, grasping) and product domains (e.g., Amazon products), when assessing the implications of applying these methods to the case of mobile robots. Moreover, we also test the performance of: (i) imprinting the weights of a Convolutional two-branch Network and (ii) applying L2 normalisation to the embeddings of a Convolutional Siamese Network.