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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
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
Semantic-aware building and summarization of multiple aspect trajectories
Pugliese C.
The proliferation of motion sensors has signi?cantly contributed to the availability of mobility data. An important line of research focuses on augmenting these datasets with diverse semantic information, referred to as aspects, thereby yielding multiple aspect trajectories (MATs). However, a notable gap in the existing literature pertains to the absence of methodologies for obtaining MATs and the scarcity of real-world datasets. To address this gap, we introduce MAT?B??????, an innovative system designed to facilitate the customization of semantic enrichment of trajectories through the use of arbitrary aspects and external data sources. Notably, the richness of information endowed by MAT?B?????? may introduce challenges in terms of data management and storage. Consequently, we propose MAT?S??, an approach tailored to summarize trajectories while preserving their semantic information.Source: SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3628363
Metrics:
Pugliese C.
The proliferation of motion sensors has signi?cantly contributed to the availability of mobility data. An important line of research focuses on augmenting these datasets with diverse semantic information, referred to as aspects, thereby yielding multiple aspect trajectories (MATs). However, a notable gap in the existing literature pertains to the absence of methodologies for obtaining MATs and the scarcity of real-world datasets. To address this gap, we introduce MAT?B??????, an innovative system designed to facilitate the customization of semantic enrichment of trajectories through the use of arbitrary aspects and external data sources. Notably, the richness of information endowed by MAT?B?????? may introduce challenges in terms of data management and storage. Consequently, we propose MAT?S??, an approach tailored to summarize trajectories while preserving their semantic information.Source: SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023
DOI: 10.1145/3589132.3628363
Metrics:
See at:
dl.acm.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
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