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2021 Contribution to conference Open Access OPEN

Cloud and Data Federation in MobiDataLab
Carlini E., Dazzi P., Lettich F., Perego R., Renso C.
Today's innovative digital services dealing with the mobility of per- sons and goods produce huge amount of data. To propose advanced and efficient mobility services, the collection and aggregation of new sources of data from various producers are necessary. The overall objective of the MobiDataLab H2020 project is to propose to the mobility stakeholders (transport organising authorities, operators, industry, government and innovators) reproducible methodologies and sustainable tools that foster the development of a data-sharing culture in Europe and beyond. This short paper introduces the key concepts driving the design and definition of the Cloud and Data Federation that stands at the basis of MobiDataLab.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463819
Project(s): ACCORDION via OpenAIRE

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2020 Journal article Restricted

Leveraging feature selection to detect potential tax fraudsters
Matos T., Macedo J. A., Lettich F., Monteiro J. M., Renso C., Perego R., Nardini F. M.
Tax evasion is any act that knowingly or unknowingly, legally or unlawfully, leads to non-payment or underpayment of tax due. Enforcing the correct payment of taxes by taxpayers is fundamental in maintaining investments that are necessary and benefits a society as a whole. Indeed, without taxes it is not possible to guarantee basic services such as health-care, education, sanitation, transportation, infrastructure, among other services essential to the population. This issue is especially relevant in developing countries such as Brazil. In this work we consider a real-world case study involving the Treasury Office of the State of Ceará (SEFAZ-CE, Brazil), the agency in charge of supervising more than 300,000 active taxpayers companies. SEFAZ-CE maintains a very large database containing vast amounts of information concerning such companies. Its enforcement team struggles to perform thorough inspections on taxpayers accounts as the underlying traditional human-based inspection processes involve the evaluation of countless fraud indicators (i.e., binary features), thus requiring burdensome amounts of time and being potentially prone to human errors. On the other hand, the vast amount of taxpayer information collected by fiscal agencies opens up the possibility of devising novel techniques able to tackle fiscal evasion much more effectively than traditional approaches. In this work we address the problem of using feature selection to select the most relevant binary features to improve the classification of potential tax fraudsters. Finding out possible fraudsters from taxpayer data with binary features presents several challenges. First, taxpayer data typically have features with low linear correlation between themselves. Also, tax frauds may originate from intricate illicit tactics, which in turn requires to uncover non-linear relationships between multiple features. Finally, few features may be correlated with the targeted class. In this work we propose Alicia, a new feature selection method based on association rules and propositional logic with a carefully crafted graph centrality measure that attempts to tackle the above challenges while, at the same time, being agnostic to specific classification techniques. Alicia is structured in three phases: first, it generates a set of relevant association rules from a set of fraud indicators (features). Subsequently, from such association rules Alicia builds a graph, which structure is then used to determine the most relevant features. To achieve this Alicia applies a novel centrality measure we call the Feature Topological Importance. We perform an extensive experimental evaluation to assess the validity of our proposal on four different real-world datasets, where we compare our solution with eight other feature selection methods. The results show that Alicia achieves F-measure scores up to 76.88%, and consistently outperforms its competitors.Source: Expert systems with applications 145 (2020). doi:10.1016/j.eswa.2019.113128
DOI: 10.1016/j.eswa.2019.113128

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2020 Journal article Open Access OPEN

MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings
May Petry L., Leite Da Silva C., Esuli A., Renso C., Bogorny V.
The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.Source: International journal of geographical information science (Print) 34 (2020): 1428–1450. doi:10.1080/13658816.2019.1707835
DOI: 10.1080/13658816.2019.1707835
Project(s): MASTER via OpenAIRE

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2020 Contribution to conference Open Access OPEN

Preface
Tserpes K., Renso C., Matwin S.
Preface of the proceedings of the First International Workshop, MASTER 2019 Held in Conjunction with ECML-PKDD 2019 Würzburg, Germany, September 16, 2019 ProceedingsProject(s): MASTER via OpenAIRE

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2020 Journal article Restricted

A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems
Melo Junior L., Nardini F. M. Renso C., Trani R., Macedo J. A.
Lenders, such as banks and credit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. The profitability of the banks thus highly depends on the models used to decide on the customer's loans. State-of-the-art credit scoring models are based on machine learning and statistical methods. One of the major problems of this field is that lenders often deal with imbalanced datasets that usually contain many paid loans but very few not paid ones (called defaults). Recently, dynamic selection methods combined with ensemble methods and preprocessing techniques have been evaluated to improve classification models in imbalanced datasets presenting advantages over the static machine learning methods. In a dynamic selection technique, samples in the neighborhood of each query sample are used to compute the local competence of each base classifier. Then, the technique selects only competent classifiers to predict the query sample. In this paper, we evaluate the suitability of dynamic selection techniques for credit scoring problem, and we present Reduced Minority k-Nearest Neighbors (RMkNN), an approach that enhances state of the art in defining the local region of dynamic selection techniques for imbalanced credit scoring datasets. This proposed technique has a superior prediction performance in imbalanced credit scoring datasets compared to state of the art. Furthermore, RMkNN does not need any preprocessing or sampling method to generate the dynamic selection dataset (called DSEL). Additionally, we observe an equivalence between dynamic selection and static selection classification. We conduct a comprehensive evaluation of the proposed technique against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that RMkNN improves the classification performance of the evaluated datasets regarding AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall.Source: Expert systems with applications 152 (2020). doi:10.1016/j.eswa.2020.113351
DOI: 10.1016/j.eswa.2020.113351
Project(s): MC2020 via OpenAIRE, BigDataGrapes via OpenAIRE, MASTER via OpenAIRE

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2019 Journal article Open Access OPEN

Analytics Everywhere: Generating Insights from the Internet of Things
Cao H., Wachowicz M., Renso C., Carlini E.
The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks utilizing edge, fog, and cloud computing that are designed to support an Analytics Everywhere framework. The aim is to integrate a variety of computational resources and analytical capabilities according to a data life-cycle. We demonstrate the proposed framework using an application in smart transit.Source: IEEE access 7 (2019): 71749–71769. doi:10.1109/ACCESS.2019.2919514
DOI: 10.1109/access.2019.2919514

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2019 Conference article Open Access OPEN

On combining dynamic selection, sampling, and pool generators for credit scoring
Melo Junior L., Nardini F. M., Renso C., Fernandes De Macedo J. A.
The profitability of the banks highly depends on the models used to decide on the customer's loans. State of the art credit scoring models are based on machine learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring datasets usually contain many paid loans and few not paid ones (defaults). Recently, dynamic selection approaches combined with pre-processing techniques have been evaluated for imbalanced datasets. However, previous works only evaluate oversampling techniques combined with bagging pool generator ensembles. For this reason, we propose to combine different dynamic selection, preprocessing and pool generation techniques. We assess the prediction performance by using four public real-world credit scoring datasets with different levels of imbalanced ratio and four evaluation measures. Experimental results show that KNORA-Union dynamic selection technique combined with Balanced Random Forest improves the classification performance concerning the static ensemble for all levels of imbalance ratio.Source: Machine Learning and Data Mining in Pattern Recognition, 15th International Conference on Machine Learning and Data Mining, MLDM, pp. 443–457, New York, USA, 18/07/2019, 23/07/2019

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2019 Journal article Embargo

Speed prediction in large and dynamic traffic sensor networks
Magalhaes R. P., Lettich F., Macedo J. A., Nardini F. M., Perego R., Renso C., Trani R.
Smart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from ~1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks.Source: Information systems (Oxf.) 98 (2019). doi:10.1016/j.is.2019.101444
DOI: 10.1016/j.is.2019.101444
Project(s): BigDataGrapes via OpenAIRE, MASTER via OpenAIRE

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2019 Journal article Open Access OPEN

Event attendance classification in social media
De Lira V. M., Macdonald C., Ounis I., Perego R., Renso C., Cesario Times V.
Popular events are well reflected on social media, where people share their feelings and discuss their experiences. In this paper, we investigate the novel problem of exploiting the content of non-geotagged posts on social media to infer the users' attendance of large events in three temporal periods: before, during and after an event. We detail the features used to train event attendance classifiers and report on experiments conducted on data from two large music festivals in the UK, namely the VFestival and Creamfields events. Our classifiers attain very high accuracy with the highest result observed for the Creamfields festival ( similar to 91% accuracy at classifying users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Finally, we discuss an illustrative application of the methodology in the field of transportation.Source: Information processing & management 56 (2019): 687–703. doi:10.1016/j.ipm.2018.11.001
DOI: 10.1016/j.ipm.2018.11.001

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2019 Journal article Open Access OPEN

A comprehensive reputation assessment framework for volunteered geographic information in crowdsensing applications
Jabeur N., Karam R., Melchiori M., Renso C.
Volunteered geographic information (VGI) is the result of activities where individuals, supported by enabling technologies, behave like physical sensors by harvesting and organizing georeferenced content, usually in their surroundings. Both researchers and organizations have recognized the value of VGI content, however this content is typically heterogeneous in quality and spatial coverage. As a consequence, in order for applications to benefit from it, its quality and reliability need to be assessed in advance. This may not be easy since, typically, it is unknown how the process of collecting and organizing the VGI content has been conducted and by whom. In the literature, various proposals focus on an indirect process of quality assessment based on reputation scores. Following this perspective, the present paper provides as main contributions: (i) a multi-layer architecture for VGI which supports a process of reputation evaluation; (ii) a new comprehensive model for computing reputation scores for both VGI data and contributors, based on direct and indirect evaluations expressed by users, and including the concept of data aging; (iii) a variety of experiments evaluating the accuracy of the model. Finally, the relevance of adopting this framework is discussed via an applicative scenario for recommending tourist itineraries.Source: Personal and ubiquitous computing (Print) 23 (2019): 669–685. doi:10.1007/s00779-018-1122-9
DOI: 10.1007/s00779-018-1122-9

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2019 Journal article Open Access OPEN

MASTER: A multiple aspect view on trajectories
Mello R. D. S., Bogorny V., Alvares L. O., Santana L. H. Z., Ferrero C. A., Frozza A. A., Schreiner G. A., Renso C.
For many years trajectory data have been treated as sequences of space-time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all this information with trajectories. In this article we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vast range of applications, and, differently from state-of-the-art methods, we propose a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against the state-of-the-art spatio-temporal database SECONDO extension for symbolic trajectories.Source: Transactions in GIS (Print) 23 (2019): 805–822. doi:10.1111/tgis.12526
DOI: 10.1111/tgis.12526
Project(s): MASTER via OpenAIRE

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2019 Journal article Open Access OPEN

Towards semantic-aware multiple-aspect trajectory similarity measuring
Petry L. M., Ferrero C. A., Alvares L. O., Renso C., Bogorny V.
The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple-aspect trajectories, where mobility data are enriched with several semantic dimensions, current state-of-the-art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple-aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple-aspect trajectories.Source: Transactions in GIS (Print) 23 (2019): 960–975. doi:10.1111/tgis.12542
DOI: 10.1111/tgis.12542

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2019 Conference article Restricted

An empirical comparison of classification algorithms for imbalanced credit scoring datasets
Soares De Melo Junior L., Nardini F. M., Renso C., Fernandes De Macedo J. A.
The profitability of banks is highly dependent on credit scoring models, which support decision making to approve a loan to a customer. State-of-the-art credit scoring models are based on learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring datasets usually contain mainly paid loans and few defaults (unpaid ones). Recently, new imbalanced learning techniques have been proposed in the literature, and they can improve the credit scoring results. Motivated by this scenario, we evaluate several classification approaches to credit scoring. Besides, we also assess some preprocessing methods to overcome skewed datasets. To achieve it, we use three public real-world credit scoring datasets. In our experiments, we progressively increase the class imbalance in each of these datasets by randomly undersampling the minority class of defaulters to identify how the predictive power is affected. The results indicate that random forest, extreme gradient boosting perform very well in all imbalance levels. We also find that a complete grid search step can increase the prediction power of classification approaches in high imbalanced datasets.Source: ICMLA 2019 - 18th IEEE International Conference on Machine Learning and Applications, pp. 747–754, Boca Raton; United States, 16-19 December, 2019
DOI: 10.1109/icmla.2019.00133
Project(s): BigDataGrapes via OpenAIRE

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2019 Conference article Restricted

KNORA-IU: improving the dynamic selection prediction in imbalanced credit scoring problems
Melo L., Nardini F. M., Renso C., Macedo J. A.
Credit scoring has become a critical tool to discriminate 'bad' applicants from 'good' ones for financial institutions. One common characteristic of the credit dataset is the imbalance between good and bad applicants, with low defaults (no paid loans). Ensemble classification methodology is widely used in this field. However, dynamic ensemble selection approaches to imbalanced datasets have drawn little consideration. This study aims to adapt KNORA-Union, an excellent dynamic selection technique, to imbalanced credit scoring problem, the KNORAImbalanced Union (KNORA-IU). In this approach, we propose a new procedure to evaluate the competence of each base classifier. The results, based on four performance measures, indicate that the performance of the KNORA-IU is superior to the state-of-the-art approaches for moderate imbalanced datasets.Source: ICTAI 2019 - 31st IEEE International Conference on Tools with Artificial Intelligence, pp. 424–431, ortland, United States, 4-6 November, 2019
DOI: 10.1109/ictai.2019.00066
Project(s): BigDataGrapes via OpenAIRE

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2019 Conference article Open Access OPEN

Vista: a visual analytics platform for semantic annotation of trajectories
Soares A., Rose J., Etemad M., Renso C., Matwin S.
Most of the trajectory datasets only record the spatio-temporal position of the moving object, thus lacking semantics and this is due to the fact that this information mainly depends on the domain expert labeling, a time-consuming and complex process. This paper is a contribution in facilitating and supporting the manual annotation of trajectory data thanks to a visual-analytics-based platform named VISTA. VISTA is designed to assist the user in the trajectory annotation process in a multi-role user environment. A session manager creates a tagging session selecting the trajectory data and the semantic contextual information. The VISTA platform also supports the creation of several features that will assist the tagging users in identifying the trajectory segments that will be annotated. A distinctive feature of VISTA is the visual analytics functionalities that support the users in exploring and processing the trajectory data, the associated features and the semantic information for a proper comprehension of how to properly label trajectories.Source: EDBT 2019 - 22nd International Conference on Extending Database Technology, pp. 570–573, Lisbon, Portugal, March 26-29, 2019
DOI: 10.5441/002/edbt.2019.58
Project(s): MASTER via OpenAIRE

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2018 Contribution to book Open Access OPEN

Analysing trajectories of mobile users: from data warehouses to recommender systems
Nardini F. M., Orlando S., Perego R., Raffaetà A., Renso C., Silvestri C.
This chapter discusses a general framework for the analysis of trajectories of moving objects, designed around a Trajectory Data Warehouse (TDW). We argue that data warehouse technologies, combined with geographic visual analytics tools, can play an important role in granting very fast, accurate and understandable analysis of mobility data. We describe how in the last decade the TDW models have changed in order to provide the user with a more suitable model of the reality of interest and we also cope with the challenge of semantic trajectories. As a use case we illustrate how the framework can be instantiated for realizing a recommender system for tourists.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. 407–421, 2018
DOI: 10.1007/978-3-319-61893-7_24

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2018 Journal article Open Access OPEN

Item-driven group formation
Valcarce D., Brilhante I., Macedo J. A., Nardini F. M., Perego R., Renso C.
Several daily activities, such as traveling to a tourist attraction or watching a movie in the cinema, are better enjoyed with a group of friends. However, choosing the best companions may be difficult: we need to consider either the relations among the chosen friends and their interest in the proposed destination/item. In this paper, we address this problem from the perspective of recommender systems: given a user, her social network, and a (recommended) item that is relevant to the user, our User-Item Group Formation (UI-GF) problem aims to find the best group of friends with whom to enjoy such item. This problem differs from traditional group recommendation and group formation tasks since it maximizes two orthogonal aspects: (i) the relevance of the recommended item for every member of the group, and (ii) the intra-group social relationships. We formalize the UI-GF problem and we propose two different approaches to address it. In the first approach, the problem is modeled as the densest k-subgraph problem over a specific instance of the social network of the user, while the second approach is based on a probabilistic collaborative filtering method that exploit relevance-based language models. We perform an extensive assessment of several algorithms solving the two approaches in the domain of location recommendations by exploiting five publicly available Location-Based Social Network (LBSN) datasets. The experimental results achieved confirm the effectiveness and the feasibility of the proposed solutions that outperform strong baselines. Indeed, results reveal interesting and orthogonal properties of the two formulations. The probabilistic collaborative filtering approach is more effective than the graph-based one on datasets with sparse social networks but with more dense check-in data. On the contrary, the graph-based model performs very well on datasets which present high sparsity on the ratings and check-ins but a higher number of links among users.Source: Online social networks and media 8 (2018): 17–31. doi:10.1016/j.osnem.2018.10.002
DOI: 10.1016/j.osnem.2018.10.002
Project(s): SoBigData via OpenAIRE

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2018 Report Open Access OPEN

Traj2User: exploiting embeddings for computing similarity of users mobile behavior
Esuli A., May Petry L., Renso C., Bogorny V.
Semantic trajectories are high level representations of user movements where several aspects related to the movement context are represented as heterogeneous textual labels. With the objective of finding a meaningful similarity measure for semantically enriched trajectories, we propose Traj2User, a Word2Vec-inspired method for the generation of a vector representation of user movements as user embeddings. Traj2User uses simple representations of trajectories and delegates the definition of the similarity model to the learning process of the network. Preliminary results show that Traj2User is able to generate effective user embeddings.Source: Research report, MASTER, 777695, 2018
Project(s): MASTER via OpenAIRE

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2018 Journal article Open Access OPEN

Boosting Ride Sharing With Alternative Destinations
De Lira V. M., Perego R., Renso C., Rinzivillo S., Times V. C.
People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can he performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the topk most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can he saved and how the ranking function can be tuned to enforce pro-environment behaviors.Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 2290–2300. doi:10.1109/TITS.2018.2836395
DOI: 10.1109/tits.2018.2836395
Project(s): MASTER via OpenAIRE

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2017 Conference article Restricted

SELEcTor: Discovering Similar Entities on LinkEd DaTa by Ranking Their Features
Ruback L., Casanova M. A., Renso C., Lucchese C.
Several approaches have been used in the last years to compute similarity between entities. In this paper, we present a novel approach to compute similarity between entities using their features available as Linked Data. The key idea of the proposed framework, called SELEcTor, is to exploit ranked lists of features extracted from Linked Data sources as a representation of the entities we want to compare. The similarity between two entities is thus mapped to the problem of comparing two ranked lists. Our experiments, conducted with museum data from DBpedia, demonstrate that SELEcTor achieves better accuracy than state- of-the-art methods.Source: ICSC 2017 - IEEE 11th International Conference on Semantic Computing, pp. 117–124, San Diego, CA, USA, 30 January-2 February 2017
DOI: 10.1109/icsc.2017.46

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