2023
Conference article
Open Access
A general methodology for building multiple aspect trajectories
Lettich F., Pugliese C., Renso C., Pinelli F.
The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.Source: SAC 2023 - 38th ACM/SIGAPP Symposium on Applied Computing, pp. 515–517, Tallinn, Estonia, 27-31/03/2023
DOI: 10.1145/3555776.3577832
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus
Metrics:
Lettich F., Pugliese C., Renso C., Pinelli F.
The massive use of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. Such data can be enriched with several semantic dimensions (or aspects), i.e., contextual and heterogeneous information captured in the surrounding environment, leading to the creation of multiple aspect trajectories (MATs). In this work, we present how the MAT-Builder system can be used for the semantic enrichment processing of movement data while being agnostic to aspects and external semantic data sources. This is achieved by integrating MAT-Builder into a methodology which encompasses three design principles and a uniform representation formalism for enriched data based on the Resource Description Framework (RDF) format. An example scenario involving the generation and querying of a dataset of MATs gives a glimpse of the possibilities that our methodology can open up.Source: SAC 2023 - 38th ACM/SIGAPP Symposium on Applied Computing, pp. 515–517, Tallinn, Estonia, 27-31/03/2023
DOI: 10.1145/3555776.3577832
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository 
| dl.acm.org 
| CNR ExploRA
2023
Journal article
Open Access
Semantic enrichment of mobility data: a comprehensive methodology and the MAT-BUILDER system
Lettich F., Pugliese C., Renso C., Pinelli F.
The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensions, or aspects, that provide contextual and heterogeneous information about the surrounding environment, resulting in the creation of multiple aspect trajectories (MATs). Common examples of aspects can be points of interest, user photos, transportation means, weather conditions, social media posts, and many more. However, the literature does not currently provide a consensus on how to semantically enrich mobility data with aspects, particularly in dynamic scenarios where semantic information is extracted from numerous and heterogeneous external data sources. In this work, we aim to address this issue by presenting a comprehensive methodology to facilitate end users in instantiating their semantic enrichment processes of movement data. The methodology is agnostic to semantic aspects and external semantic data sources. The vision behind our methodology rests on three pillars: (1) three design principles which we argue are necessary for designing systems capable of instantiating arbitrary semantic enrichment processes; (2) the MAT-Builder system, which embodies these principles; (3) the use of an RDF knowledge graph-based representation to store MATs datasets, thereby enabling uniform querying and analysis of enriched movement data. We qualitatively evaluate the methodology in two complementary example scenarios, where we show both the potential in generating interesting and useful semantically enriched mobility datasets, and the expressive power in querying the resulting RDF trajectories with SPARQL.Source: IEEE access 11 (2023): 90857–90875. doi:10.1109/ACCESS.2023.3307824
DOI: 10.1109/access.2023.3307824
Project(s): MobiDataLab, MASTER , SoBigData-PlusPlus
Metrics:
Lettich F., Pugliese C., Renso C., Pinelli F.
The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensions, or aspects, that provide contextual and heterogeneous information about the surrounding environment, resulting in the creation of multiple aspect trajectories (MATs). Common examples of aspects can be points of interest, user photos, transportation means, weather conditions, social media posts, and many more. However, the literature does not currently provide a consensus on how to semantically enrich mobility data with aspects, particularly in dynamic scenarios where semantic information is extracted from numerous and heterogeneous external data sources. In this work, we aim to address this issue by presenting a comprehensive methodology to facilitate end users in instantiating their semantic enrichment processes of movement data. The methodology is agnostic to semantic aspects and external semantic data sources. The vision behind our methodology rests on three pillars: (1) three design principles which we argue are necessary for designing systems capable of instantiating arbitrary semantic enrichment processes; (2) the MAT-Builder system, which embodies these principles; (3) the use of an RDF knowledge graph-based representation to store MATs datasets, thereby enabling uniform querying and analysis of enriched movement data. We qualitatively evaluate the methodology in two complementary example scenarios, where we show both the potential in generating interesting and useful semantically enriched mobility datasets, and the expressive power in querying the resulting RDF trajectories with SPARQL.Source: IEEE access 11 (2023): 90857–90875. doi:10.1109/ACCESS.2023.3307824
DOI: 10.1109/access.2023.3307824
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
ieeexplore.ieee.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Predicting EV parking behaviour in shared premises
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: BMDA 2023 - 5th International Workshop on Big Mobility Data Analytics co-located with EDBT/ICDT 2023 Joint Conference, Ioannina, Greece, 28/03/2023
Project(s): ERANet SmartGridPlus
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: BMDA 2023 - 5th International Workshop on Big Mobility Data Analytics co-located with EDBT/ICDT 2023 Joint Conference, Ioannina, Greece, 28/03/2023
Project(s): ERANet SmartGridPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Contribution to book
Open Access
Message from the PC and General co-Chairs SSTD 2023
Baihua Z., Mokbel M., Nascimento M. A., Renso C., Zeitouni K. Züfle A.
Preface of the conference SSTD 2023.Source: , pp. vi–vii. New York: ACM Press, 2023
Baihua Z., Mokbel M., Nascimento M. A., Renso C., Zeitouni K. Züfle A.
Preface of the conference SSTD 2023.Source: , pp. vi–vii. New York: ACM Press, 2023
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
MAT-CA: a tool for Multiple Aspect Trajectory Clustering Analysis
Santos Y., Giuliani R., Portela T., Renso C., Carvalho J.
Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.Source: EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 40–43, Hamburg, Germany, 13/11/2023
DOI: 10.1145/3615885.3628009
Project(s): MASTER
Metrics:
Santos Y., Giuliani R., Portela T., Renso C., Carvalho J.
Multiple aspect trajectory (MAT) is a relevant concept that enables mining interesting patterns moving objects for di!erent applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. The high dimensionality and heterogeneity of these data makes clustering a very challenging task both in terms of e"ciency and quality. The present demo o!ers a tool, called MAT-CA, to support the user in the clustering task of MATs, speci#cally for identifying and visualizing the hidden patterns. The MAT-CA join into the same tool a multiple aspects trajectories clustering method and visual analysis of the results. We illustrate the use of the tool for o!ering both clustering output visualization and statistics.Source: EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 40–43, Hamburg, Germany, 13/11/2023
DOI: 10.1145/3615885.3628009
Project(s): MASTER
Metrics:
See at: CNR ExploRA
2023
Conference article
Open Access
A data augmentation algorithm for trajectory data
Haranwala Y. J., Spadon G., Renso C., Soares A.
The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this !eld. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in !elds where few labeled trajectory data are available for training machine learning models.Source: EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 25–29, Hamburg, Germany, 13/11/2023
DOI: 10.1145/3615885.3628008
Project(s): MASTER
Metrics:
Haranwala Y. J., Spadon G., Renso C., Soares A.
The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressing need for expansive datasets to advance machine learning techniques in this domain. The absence of such resources currently represents a substantial hindrance to research progress in this !eld. Data augmentation is emerging as a popular technique to mitigate this issue in several domains. However, applying state-of-the-art techniques as-is proves challenging when dealing with trajectory data due to the intricate spatio-temporal dependencies inherent to such data. In this work, we propose a novel strategy for augmenting trajectory data that applies a geographical perturbation on trajectory points along a trajectory. Such a perturbation results in controlled changes in the raw trajectory and, consequently, causes changes in the trajectory feature space. We test our strategy in two trajectory datasets and show a performance improvement of approximately 20% when contrasted with the baseline. We believe this strategy will pave the way for a more comprehensive framework for trajectory data augmentation that can be used in !elds where few labeled trajectory data are available for training machine learning models.Source: EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives, pp. 25–29, Hamburg, Germany, 13/11/2023
DOI: 10.1145/3615885.3628008
Project(s): MASTER
Metrics:
See at: CNR ExploRA
2023
Conference article
Open Access
Summarizing trajectories using semantically enriched geographical context
Pugliese C., Lettich F., Pinelli F., Renso C.
The proliferation of tracking sensors in today's devices has led to the generation of high-frequency, high-volume streams of mobility data capturing the movements of various objects. These movement data can be enriched with semantic contextual information, such as activities, events, user preferences, and more, generating semantically enriched trajectories. Creating and managing these types of trajectories presents challenges due to the massive data volume and the heterogeneous, complex semantic dimensions. To address these issues, we introduce a novel approach, MAT-Sum, which uses a location-centric enrichment perspective to summarize massive volumes of mobility data while preserving essential semantic information. Our approach enriches geographical areas with semantic aspects to provide the underlying context for trajectories, enabling effective data reduction through trajectory summarization. In the experimental evaluation, we show that MAT-Sum effectively minimizes trajectory volume while retaining a good level of semantic quality, thus presenting a viable solution to the relevant issue of managing massive mobility data.Source: SIGSPATIAL 2023 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625587
Project(s): MobiDataLab, MASTER , SoBigData-PlusPlus
Metrics:
Pugliese C., Lettich F., Pinelli F., Renso C.
The proliferation of tracking sensors in today's devices has led to the generation of high-frequency, high-volume streams of mobility data capturing the movements of various objects. These movement data can be enriched with semantic contextual information, such as activities, events, user preferences, and more, generating semantically enriched trajectories. Creating and managing these types of trajectories presents challenges due to the massive data volume and the heterogeneous, complex semantic dimensions. To address these issues, we introduce a novel approach, MAT-Sum, which uses a location-centric enrichment perspective to summarize massive volumes of mobility data while preserving essential semantic information. Our approach enriches geographical areas with semantic aspects to provide the underlying context for trajectories, enabling effective data reduction through trajectory summarization. In the experimental evaluation, we show that MAT-Sum effectively minimizes trajectory volume while retaining a good level of semantic quality, thus presenting a viable solution to the relevant issue of managing massive mobility data.Source: SIGSPATIAL 2023 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3625587
Project(s): MobiDataLab
, MASTER , SoBigData-PlusPlus Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
TrajParquet: a trajectory-oriented column file format for mobility data lakes
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Towards a representativeness measure for summarized trajectories with multiple aspects
Lago Machado V., Tortelli Portela T., Renso C., Dos Santos Mello R.
Large trajectory datasets have led to the development of summarization methods. However, evaluating the efficacy of these techniques can be complex due to the lack of a suitable representativeness measure. In the context of multi-aspect trajectories, current summarization lacks evaluation methods. To address this, we introduce RMMAT, a novel representativeness measure that combines similarity metrics and covered information to offer adaptability to diverse data and analysis needs. Our innovation simplifies summarization technique evaluation and enables deeper insights from extensive trajectory data. Our evaluation of real-world trajectory data demonstrates RMMAT as a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects.Source: GEOINFO 2023 - XXIV Brazilian Symposium on GeoInformatics, pp. 37–48, Sao José dos Campos, Brazil, 4-6/12/2023
Project(s): MASTER, SoBigData-PlusPlus
Lago Machado V., Tortelli Portela T., Renso C., Dos Santos Mello R.
Large trajectory datasets have led to the development of summarization methods. However, evaluating the efficacy of these techniques can be complex due to the lack of a suitable representativeness measure. In the context of multi-aspect trajectories, current summarization lacks evaluation methods. To address this, we introduce RMMAT, a novel representativeness measure that combines similarity metrics and covered information to offer adaptability to diverse data and analysis needs. Our innovation simplifies summarization technique evaluation and enables deeper insights from extensive trajectory data. Our evaluation of real-world trajectory data demonstrates RMMAT as a robust Representativeness Measure for Summarized Trajectories with Multiple Aspects.Source: GEOINFO 2023 - XXIV Brazilian Symposium on GeoInformatics, pp. 37–48, Sao José dos Campos, Brazil, 4-6/12/2023
Project(s): MASTER
, SoBigData-PlusPlus
See at:
ISTI Repository | www.geoinfo.info | CNR ExploRA
2022
Journal article
Open Access
MAT-Index: an index for fast multiple aspect trajectory similarity measuring
De Souza A. P. R., Renso C., Perego R., Bogorny V.
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: Transactions in GIS (Print) (2022). doi:10.1111/tgis.12889
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
De Souza A. P. R., Renso C., Perego R., Bogorny V.
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: Transactions in GIS (Print) (2022). doi:10.1111/tgis.12889
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
See at:
onlinelibrary.wiley.com | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A federated cloud solution for transnational mobility data sharing
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION, MobiDataLab
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION
, MobiDataLab
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
Predicting vehicles parking behaviour for EV recharge optimization
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
MAT-Builder: a system to build semantically enriched trajectories
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 175–182, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MobiDataLab, MASTER
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 175–182, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MobiDataLab
, MASTER
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Report
Open Access
Mobility data mining: from technical to ethical (Dagstuhl Seminar 22022)
Berendt B., Matwin S., Renso C., Meissner F., Pratesi F., Raffaeta A., Rockwell G.
This report documents the program and the outcomes of Dagstuhl Seminar 22022 "Mobility Data Analysis: from Technical to Ethical" that took place fully remote and hosted by Schloss Dagstuhl from 10-12 January 2022. An interdisciplinary team of 23 researchers from Europe, the Americas and Asia in the fields of computer science, ethics and mobility analysis discussed interactions between their topics and fields to bridge the gap between the more technical aspects to the ethics with the objective of laying the foundations of a new Mobility Data Ethics research field.Source: ISTI Research report, pp.35–66, 2022
DOI: 10.4230/dagrep.12.1.35
Project(s): MASTER
Metrics:
Berendt B., Matwin S., Renso C., Meissner F., Pratesi F., Raffaeta A., Rockwell G.
This report documents the program and the outcomes of Dagstuhl Seminar 22022 "Mobility Data Analysis: from Technical to Ethical" that took place fully remote and hosted by Schloss Dagstuhl from 10-12 January 2022. An interdisciplinary team of 23 researchers from Europe, the Americas and Asia in the fields of computer science, ethics and mobility analysis discussed interactions between their topics and fields to bridge the gap between the more technical aspects to the ethics with the objective of laying the foundations of a new Mobility Data Ethics research field.Source: ISTI Research report, pp.35–66, 2022
DOI: 10.4230/dagrep.12.1.35
Project(s): MASTER
Metrics:
See at:
ISTI Repository | doi.org | CNR ExploRA
2022
Conference article
Open Access
AUTOMATISE: multiple aspect trajectory data mining tool library
Tortelli T., Bogorny V., Bernasconi A., Renso C.
With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 282–285, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00060
Project(s): MASTER
Metrics:
Tortelli T., Bogorny V., Bernasconi A., Renso C.
With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 282–285, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00060
Project(s): MASTER
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
MAT-Builder: a system to build semantically enriched trajectories
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect tra-jectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-BUILDER, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-BUILDER has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities to the system and set up their own semantic enrichment process. The demonstration scenario, which will be showcased during the demo session, highlights how MAT-BUILDER's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 274–277, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00058
Project(s): MobiDataLab, MASTER
Metrics:
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect tra-jectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-BUILDER, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-BUILDER has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities to the system and set up their own semantic enrichment process. The demonstration scenario, which will be showcased during the demo session, highlights how MAT-BUILDER's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 274–277, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00058
Project(s): MobiDataLab
, MASTER Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Contribution to journal
Restricted
Big mobility data analytics: recent advances and open problems
Sakr M., Ray C., Renso C.
Source: Geoinformatica (Dordrecht) 26 (2022): 541–549. doi:10.1007/s10707-022-00483-0
DOI: 10.1007/s10707-022-00483-0
Metrics:
Sakr M., Ray C., Renso C.
Source: Geoinformatica (Dordrecht) 26 (2022): 541–549. doi:10.1007/s10707-022-00483-0
DOI: 10.1007/s10707-022-00483-0
Metrics:
See at:
GeoInformatica | CNR ExploRA
2021
Contribution to conference
Open Access
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, pp. 39–40, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463819
Project(s): ACCORDION
Metrics:
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, pp. 39–40, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463819
Project(s): ACCORDION
Metrics:
See at:
ISTI Repository | dl.acm.org | CNR ExploRA
2021
Conference article
Open Access
A novel similarity measure for multiple aspect trajectory clustering
Varlamis I., Sardianos C., Bogorny V., Alvares L. O., Carvalho J. T., Renso C., Perego R., Violos J.
Multiple aspect trajectories (MATs) is an emerging concept in the domain of Geographical Information Systems, where the basic view of semantic trajectories is enhanced with the notion of multiple heterogeneous aspects, characterizing different semantic dimensions related to the pure movement data. Many applications benefit from the analysis of multiple aspects trajectories, ranging from the analysis of people trajectories and the extraction of daily habits to the monitoring of vessel trajectories and the detection of outlying behaviors. This work proposes a novel MAT similarity measure as the core component in a hierarchical clustering algorithm. Despite the many clustering methods in the literature and the recent works on MAT similarity, there are still no works that dig deeper into the MAT clustering task. The current article copes with this issue by introducing TraFoS, a new similarity measure that defines a novel method for comparing MATs. TraFos includes a multi-vector representation of MATs that improves their similarity comparison. TraFos allows us to compare MATs across each aspect and then combine similarities in a single measure. We compared TraFos with other state of the art similarity metrics in Agglomerative clustering. The experimental results show that TraFos outperforms other similarities metrics in terms of internal, external clustering metrics and training time.Source: SAC 21 - 36th Annual ACM Symposium on Applied Computing, pp. 551–558, Online Conference, 22-26/03/2021
DOI: 10.1145/3412841.3441935
Project(s): MASTER
Metrics:
Varlamis I., Sardianos C., Bogorny V., Alvares L. O., Carvalho J. T., Renso C., Perego R., Violos J.
Multiple aspect trajectories (MATs) is an emerging concept in the domain of Geographical Information Systems, where the basic view of semantic trajectories is enhanced with the notion of multiple heterogeneous aspects, characterizing different semantic dimensions related to the pure movement data. Many applications benefit from the analysis of multiple aspects trajectories, ranging from the analysis of people trajectories and the extraction of daily habits to the monitoring of vessel trajectories and the detection of outlying behaviors. This work proposes a novel MAT similarity measure as the core component in a hierarchical clustering algorithm. Despite the many clustering methods in the literature and the recent works on MAT similarity, there are still no works that dig deeper into the MAT clustering task. The current article copes with this issue by introducing TraFoS, a new similarity measure that defines a novel method for comparing MATs. TraFos includes a multi-vector representation of MATs that improves their similarity comparison. TraFos allows us to compare MATs across each aspect and then combine similarities in a single measure. We compared TraFos with other state of the art similarity metrics in Agglomerative clustering. The experimental results show that TraFos outperforms other similarities metrics in terms of internal, external clustering metrics and training time.Source: SAC 21 - 36th Annual ACM Symposium on Applied Computing, pp. 551–558, Online Conference, 22-26/03/2021
DOI: 10.1145/3412841.3441935
Project(s): MASTER
Metrics:
See at:
ISTI Repository | dl.acm.org | CNR ExploRA
2021
Report
Open Access
Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
See at:
arxiv.org | ISTI Repository | CNR ExploRA