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2019 Part of book or chapter of book Open Access OPEN

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

See at: ISTI Repository Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | link.springer.com Restricted | Unknown Repository Restricted | CNR ExploRA Restricted


2019 Conference object Open Access OPEN

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

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2018 Conference object Open Access OPEN

Discovering Mobility Functional Areas: A Mobility Data Analysis Approach
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData via OpenAIRE

See at: ISTI Repository Open Access | ISTI Repository Open Access | Unknown Repository Restricted | link.springer.com Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted


2018 Conference object Open Access OPEN

MOBILITY ATLAS BOOKLET: AN URBAN DASHBOARD DESIGN and IMPLEMENTATION
Gabrielli L., Rossi M., Giannotti F., Fadda D., Rinzivillo S.
The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.Source: 3rd International Conference on Smart Data and Smart Cities, SDSC 2018, pp. 51–58, Delft, Netherlands, 04-05/10/2018
DOI: 10.5194/isprs-annals-IV-4-W7-51-2018
DOI: 10.5194/isprs-annals-iv-4-w7-51-2018

See at: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Open Access


2016 Conference object Restricted

Big data and public administration: a case study for Tuscany airports
Furletti B., Nanni M., Fadda D., Piccini L., Lattarulo P.
In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned.Source: SEBD 2016 - 24th Italian Symposium on Advanced Database Systems, pp. 158–165, Ugento, Lecce, 19-22 giugno 2016
Project(s): PETRA via OpenAIRE

See at: CNR ExploRA Restricted


2016 Report Restricted

Database of female scientists at the 6 targeted Departments of UNIPI
Romano V., Natilli M., Fadda D., Rossetti G., Giannotti F.
The experience gained with the several funded European projects allows us to collect data on female careers but also to identify the context (at institutional level) as a crucial factor in defining the phenomenon of gender equality. The usual approach is to perform a survey or to ask the administration in order to understand how many women are employed at the different levels of the institution at a certain time. The institution obtains a snapshot of the gender equality or, if the study is repeated regularly, a sequence of snapshots that allows gender researchers to perform comparisons and better understand the trends. The aim of the Women Scientific Career Database is to integrate the study of gender equality in the structure of the administration of the institution, in order to build a permanent gender monitor that is automatically updated by the administration. This new approach allows a real time analysis of the gender equality within the institution and, as data are continuously updated, makes it easier to verify how different strategies, laws or regulations can modify the status of gender equality. In order to better understand how the career of a researcher evolves within the institution through years, a lot of different events have to be monitored, like the type of contract and its evolution, and scientific production. The analysis provides statistics aggregated at university level, at department level and personal level in order to give a global picture of the university status and to show how different departments present different behaviors with respect to the gender inequalities. The personal level aggregation wants instead to show how real women scientist can have a successful career. This report describes the realization of the Women Scientific Career Database, the data model, the acquisition procedures and the implementation of first family of indicators and their rendering through a navigable web interface.Source: Project report, TRIGGER, Deliverable D1.8, 2016
Project(s): TRIGGER via OpenAIRE

See at: CNR ExploRA Restricted | triggerproject.eu Restricted