Knowledge graphs (KGs) have emerged as powerful tools for biomedical research, enabling the integration and analysis of heterogeneous data sources. This study explores how a KG, combined with the Neo4j graph database, supports drug analysis and the extraction of biomedical insights. First, data from DailyMed, Purple Book and Orange Book are collected and standardized to ensure interoperability. Second, Named Entity Recognition is applied to address inconsistencies across sources. A hierarchical approach is used to link drugs to Disease Ontology, Orphanet, DrugBank, and ChEBI or active ingredient-based IDs, ensuring data accuracy. The constructed KG facilitates diverse analytical tasks, including the identification of drug-disease associations, longitudinal analysis of drug approval trends, and characterization of common routes of administration. Our results reveal complex interconnections between 561 drugs and 176 diseases, identifying significant regulatory hubs and therapeutic clusters. Temporal analysis demonstrated an acceleration in regulatory activity in recent years, while bipartite network analysis of administration routes revealed predominant delivery methods. Furthermore, graph algorithms provided by Neo4j allow advanced analyses such as finding the shortest paths between drugs based on their regulatory and therapeutic properties, revealing clusters of similar medications, and uncovering candidates for drug repurposing. The findings highlight the potential of KG methodologies in pharmaceutical research, offering a scalable approach to complex biomedical data analysis.

In the dynamic realm of machine learning, achieving transparency and understandability is crucial for fostering trust and facilitating broader adoption. This study presents an enhanced version of the Ensemble Feature Ranking algorithm, tailored to optimize feature selection in machine learning models. This paper proposes the use of an interactive dashboard application, as part of learning environment, designed to provide users with a visually intuitive platform for exploring the algorithm's internal metrics and rankings. The dashboard facilitates a deeper understanding of feature importance and algorithm behaviour, bridging the gap between complex algorithms and user comprehension. By combining advanced algorithmic techniques with a user-centric interface, our approach promotes transparency, accountability and increased user engagement in the explanation of machine learning models.

The rise of e-commerce, greatly accelerated by the COVID-19 pandemic, has created a need for more efficient and dependable delivery methods. As a result, alternative delivery points, such as parcel lockers, are being explored as effective solutions for distributing e-commerce products. Examining the social context, distribution, and density of parcel lockers is crucial in highlighting their importance. Previous research has identified the factors that influence the selection of delivery locations, assessed environmental risks and compared delivery methods. This study confidently analyses locker usage patterns, by studying 750 lockers and more than 200,000 parcels, as well as load and turnover rates, by delving into the demographic characteristics of Portuguese parishes, focusing on age, education, and employment status. Real-world data from a prominent Portuguese parcel locker provider reveals that education and employment status significantly impact the selection of parcel locker locations.

The increasing volume of user-generated content across various online platforms has created vast datasets in multiple domains, including healthcare. This article explores the significant roles of data visualisation and sentiment analysis within the healthcare sector using the UCI ML Drug Review dataset. Our study highlights the value of exploratory data analysis and sentiment analysis in comprehending patient feedback, enriching insights from the dataset. Data visualisation effectively elucidates the data's distribution and key characteristics, while sentiment analysis, performed using TextBlob and VADER, categorises the emotional tone of patient reviews. Our methodology aims to provide a deeper understanding of patient satisfaction and medication efficacy based on user-generated content.

Anxiety disorders refer to mental health conditions characterized by excessive and persistent worry, fear or dread, which can interfere with daily life. These disorders are pervasive worldwide but can be treated with psychotherapy, medication, or both. Detecting them in time is key to avoid further severe development after an initial crisis. In this paper, we present a solution based on self-monitoring, using wearables, smartphones and Machine Learning (ML) to assess users' anxiety and panic levels. This is the first paper to embrace and present a visualisation system for mental health focused on patients. User studies support the solution's quality. With this system, patients are empowered to control their situation better, helping the medical staff to get more insight into when and where a crisis occurs.

In the field of asset management, a Work Order refers to a document that outlines the necessary steps to carry out a maintenance operation on a specific physical asset. The text on this Work orders providing details about the problem and the actions required are open-ended, not normalized, and Technician’ dependant, presenting challenges for automating asset management Work Order processing. To address the issue of automating the analysis of Work Orders, Natural Language Processing techniques are employed to process the content of these documents. The aim is to identify and extract relevant information related to actions and components within the sentences. This paper presents the Reliability Centred Maintenance for Assets solution, which utilizes a semi-automatic, human-in-the-loop approach to determine a standardised and condensed set of actions and components. The results indicate a significant increase in the number of annotations, reaching a ratio of 1:14. By implementing this solution, the manual workload associated with analysing Work Orders can be reduced, thereby improving decision support and analytical processing of the data contained within these documents.

With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.

Asset management is a branch of facilities management that is responsible for the operation and maintenance of assets. The most common means of managing assets and their life-cycle is through requests and work orders. A request is used to report an occurrence that is detected either by a sensory device, a technician, or non-technical personnel; they are used to pointing out that something is wrong in a given asset, and needs appropriate attention. Depending on the problem, a request can give rise to a work order if the solution is not trivial. Work orders consist in technical reports that specify the asset that needs intervention and has the details about the work to be done or, in the case that the work is unknown from the start, the characteristics of the malfunctioning. Work orders contain a set of words, free text, that are not restricted from a fixed set of vocabulary, making it difficult to automatically analyse them. In this paper, we discuss the application of modern Natural Language Processing techniques to process the work order's description, while presenting a comparison between two Word Embedding models - Word2Vec and Fasttext- through semantic similarity tests between the encoded words, and a visualisation of the vector space through dimensionality reduction of the encoded vectors. The results show a better performance of the Fasttext approach, considering the semantics of the results.

Road traffic inside cities is responsible for noise and pollution, that causes health problems, fuel consumption and waste of time in jams. Mitigation solutions are usually used to soften the impact of this problem in most cities. In particular, the city of Lisbon has taken measures to reduce pollution by closing areas of the city to the most polluting cars - the zero emission zones. However, the city still lacks visual analytics support for traffic decisions in real-time. In this paper we present a traffic flow indicator that can indicate the road traffic fluidity inside a region of interest for a given time frame, and integrated it into a interactive dashboard supported by a predictive model. With this solution, decision makers can analyse historical data and predict short-term traffic behaviour.

Air quality is monitored using data recollected using fixed selected stations in a region, generally a city. Such approach does not support a fine-grained comprehension about the air quality, namely, in areas distant from the collector's stations, specially in residential urban places. In this paper, we describe a platform that will provide to city council decision-makers a visualization of air pollution data, using an interactive map-based dashboard with multiple spatial resolution. The air quality data is collected using low-cost portable sensors. Air pollution data is then integrated with other environmental contextual data and displayed into the dashboard. Such data includes, among other, spatio-temporal mobility data, providing contextual information about air pollution. The solution is tailored to city council decision-makers enabling a better understanding of air quality issues, and acting as a supporting tool for different communities, exploiting synergies to promote the sustainability of the city.

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed.Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns.This paper presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process.The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.

Some phenomena, such as crimes in a city, fires occurred in a country and road accidents can be interpreted as sets of spatio-temporal events. A spatio-temporal event is described by a geographic location, a time instant and other characterising attributes. The cartographic visualisation of spatio-temporal events remains unresolved, due to the challenges related with portraying multiple dimensions simultaneously: the spatial, the temporal and the semantic (zero or more dimensions) phenomenon's components. In this context, this article presents the Attenuation and Accumulation Maps (AA-Maps). The main idea of this visualisation analytic approach consists in showing in a map, the resulting effect of combining attenuation and accumulation, from a temporal reference of observation, given a spatio-temporal Level of Detail (LoD). Imagine the footprints of people crossing a garden in various directions. They leave different traces that summarize the cumulative effect of the footprints on the grass, which is attenuated as time goes by. AA-Maps support different combinations of attenuation and accumulation functions. In addition, this method also enables analysis with different Levels of Detail (LoD), both spatial and temporal. This allows distinct analytic perspectives of the phenomenon while promoting the search for the most suitable parametrization for its characteristics.

The field of Information Visualization seeks to identify the general principles of visualization, and makes use of these principles to propose new forms of visualization for specific types of data. Included in these different data types, it was identified a data type which was not thoroughly explored. The notion of entities where each entity is composed by different attributes, and one of these attributes is composed by one picture which invokes a strong feeling of familiarity to the user. The information that we are attempting to visualize is the most basic type of data structure, a table, where the number of entities to visualize should be higher than we can humanely count, yet smaller than a few thousands. In the field of visualization we identified a niche where the main focus is the image, and despite that it has a vast number of applicable scenarios, it hadn't been properly explored. One of the major attempts at doing so, was by Microsoft Live Labs and it demonstrated limitations that will be addressed by our approach. In order to evaluate the proposed forms of visualization they will be applied and evaluated with the Deloitte Portugal organizational case-study.