Chiara Renso

Italian National Research Council | CNR · Institute of Information Science and Technology "Alessandro Faedo" ISTI

With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present...

Multiple-aspect trajectory (MAT) data mining requires sophisticated tools to handle the complexity and volume of complex data. This paper introduces MAT-Tools, a comprehensive Python framework for MAT data mining. The framework consists of five main packages: mat-data, which supports data preprocessing and synthetic dataset generation; mat-model, o...

An Enterprise Knowledge Graph (EKG) is a robust foundation for knowledge management, data integration, and advanced analytics across organizations. It achieves this by offering a semantic view that semantically integrates various data sources within an organization’s data lake. This paper introduces a novel data design pattern (DDP) aimed at constr...

With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of...

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 repres...

The growing prevalence of location-based devices has resulted in a signicant abundance of location data from various tracking vendors. Nevertheless, a noticeable decit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints. There is a pressin...

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 envir...

Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there is a need for software specifi...

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...

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...

The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by...

Co-clustering is a specific type of clustering that addresses the problem of simultaneously clustering objects and attributes of a data matrix. Although general clustering techniques find non-overlapping co-clusters, finding possible overlaps between co-clusters can reveal embedded patterns in the data that the disjoint clusters cannot discover. Th...

Trajectories of moving objects are usually modeled as sequences of space-time points or, in case of semantic trajectories, as labelled stops and moves. Data analytics methods on these kinds of trajectories tend to discover geometrical and temporal patterns, or simple semantic patterns based on the labels of stops and moves. A recent extension of se...

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 syst...

The workshop will promote the discussion around these critical questions and join forces towards a Web that is truly inclusive, transparent and open. Data is learned from people. Personal data collected from social media and mobile devices, often considered sensitive information, has been extensively used by systems for a number of purposes, includ...

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...

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 dimens...

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...

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 het...

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 futu...

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 relatio...

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 uti...

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 th...

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 compl...

The large amount of semantically rich mobility data becoming available in the era of Big Data has led to the 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 relat...

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 da...

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 th...

This article presents a novel approach to estimate semantic entity similarity using entity features available as Linked Data. The key idea is to exploit ranked lists of features, extracted from Linked Data sources, as a representation of the entities to be compared. The similarity between two entities is then estimated by comparing their ranked lis...

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 v...

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....

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 desc...

A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajecto-ries into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-superv...

This paper presents a novel architecture for data analytics targeting an anticipatory learning process in the context of the Internet of Mobile Things. The architecture is geo-distributed and composed by edge, fog, and cloud resources that operate collectively to support such an anticipatory learning process. We designed the architecture to manage...

An increasing number of people share their thoughts and the images of their lives on social media platforms. People are exposed to food in their everyday lives and share on-line what they are eating by means of photos taken to their dishes. The hashtag #foodporn is constantly among the popular hashtags in Twitter and food photos are the second most...

Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal period...

We propose an analytical framework aimed at investigating different views of the discussions regarding polarized topics which occur in Online Social Networks (OSNs). The framework supports the analysis along multiple dimensions, i.e., time, space and sentiment of the opposite views about a controversial topic emerging in an OSN. To assess its usefu...

Volunteered Geographic Information (VGI) is a process where individuals, supported by enabling technologies, behave like physical sensors to harvest georeferenced content in their surroundings. The value of this, typically heterogeneous, content has been recognized by both researchers and organizations. However, in order to be fruitfully used in va...

Several works have exploited the geographic information of photos through spatial clustering algorithms aiming at the automatic discovery of points of interest (POIs). The assumption is that dense regions in terms of geographically nearby photos are good POI surrogates. However, this approach fails when: (i) nearby photos point to different POIs, a...

Volunteered Geographic Information (VGI) is an approach to crowdsource information about geospatial objects around us, as implemented in Open Street Map, Google Map Maker and WikiMapia projects. The value of this content has been recognized by both researchers and organizations for acquiring free, timely and detailed spatial data versus standard sp...

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Sp...

The fast growing number of datasets available on the Web inspired researchers to propose innovative techniques to combine spatio-temporal data with contextual data. However, as the number of datasets has increased relatively fast, finding the most appropriate datasets for enrichment also became extremely difficult. This paper proposes an innovative...

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge a...

Recent research has pointed out the needs and advantages of the semantic enrichment of movement data, a process where trajectories are partitioned into homogeneous segments that are annotated with contextual information. However, the lack of a comprehensive and well-defined framework for the enrichment makes this process dicult and error-prone. In...

ComeWithMe is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a s...

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge a...

The increasing availability of positioning data fostered a number of new applications where the knowledge about mobility patterns is essential. However, the research conducted so far on mobility analysis focused on the geometric aspect at the expenses of the semantics of the movement. In this paper, we offer a new vision of semantic trajectory data...

This paper addresses the problem of interpreting tweets that describe traffic-related events and that are distributed by government agencies in charge of road networks or by news agencies. Processing such tweets is of interest for two reasons. First, albeit phrased in natural language, such tweets use a much more regular and well-behaved prose than...

Aplicações relacionadas com mobilidade urbana são chave para ajudar pessoas, empresas e órgãos governamentais na gestão de deslocamentos no espaço urbano. Tais aplicações visam prover serviços relacionados com planejamento de rotas, detecção de menores caminhos, consultas de vizinhos mais próximos e outras. Algo comum entre essas aplicações é o fat...

The interest in carpooling is increasing due to the need to reduce traffic and noise pollution. Most of the available approaches and systems are route oriented, where driver and passengers are matched when the destination location is the same. ComeWithMe offers a new perspective: the destination is the intended activity instead of a location. This...

The analysis of movements frequently requires more than just spatio-temporal data. Thus, despite recent progresses in trajectory handling, there is still a gap between movement data and formal semantics. This gap hinders movement analyses benefiting from available knowledge, with well-defined and widely agreed semantics. This article describes the...

The Linked Data initiative promotes the publication of previously isolated databases as interlinked RDF datasets, thereby creating a global scale data space, known as the Web of Data. Linked Data Mashup applications, which consume data from the multiple Linked Data sources in the Web of Data, are confronted with the challenge of obtaining a homogen...

Nowadays, the digital traces of moving objects, such as people, animals, goods, increasingly contain richer information than the mere location history. Traces can report, for example, the places visited by tourists during a touristic journey (e.g., hotels, entertainment spots), the activities performed during such a journey (e.g., shopping, driving...

We present an application where semantically enriched trajectories obtained from crowdsensed data are used to build an advanced system for planning personalized sightseeing tours, called TRIPBUILDER. The interesting feature of TRIPBUILDER is that it uses Wikipedia content and trajectories of previous tourists collected by georeferenced Flickr photo...

ISCOOL is an interactive educational platform that helps users develop their skills for objective text analysis and interpretation. The tools incorporated into ISCOOL bridge various disparate sources, including reference datasets for people, and organizations, as well as gazetteers, dictionaries and collections of historical facts. This data serves...

TripBuilder is an unsupervised system helping tourists to build their own personalized sightseeing tour [1, 3, 2]. Given a target touristic city, the time available for the visit, and the tourist's profile, TripBuilder provides a time-budgeted tour that maximizes tourist's interests and takes into account both the time needed to enjoy the attractio...

We propose TripBuilder, an unsupervised framework for planning personalized sightseeing tours in cities. We collect categorized Points of Interests (PoIs) from Wikipedia and albums of geo-referenced photos from Flickr. By considering the photos as traces revealing the behaviors of tourists during their sightseeing tours, we extract from photo album...