Data Flows Research Articles

Digital health spaces in the city of Lisbon. The role of digitalization in linking boundaryless hospitals and smart health cities

Abstract Digital transformation is reshaping every aspect of society, including healthcare. The concepts of ‘Boundaryless Hospitals’ (BH) and ‘Smart Health Cities’ (SHC) represent a forward-thinking approach to healthcare that leverages digital technologies to enhance service delivery, operational efficiency and patient outcomes, including healthy mobility. This session delves into the complex role of digitalization in creating interconnected health and spatial environments where the flow of data and resources across physical and virtual barriers is optimized. BH embody a healthcare model where digital tools, health information systems and AI-powered diagnostics are integrated to provide care that transcends traditional hospital walls into the city. This approach not only facilitates greater accessibility and flexibility in patient care but also enhances the capacity for healthcare providers to offer personalized and timely interventions, embracing public health altogether. SHC integrates health technology into urban planning and public health services to create ecosystems that actively contribute to the health and wellbeing of their populations. Using big data analytics, IoT and other digital infrastructures, these cities support sustainable health practices, disease prevention, and efficient crisis response, promoting an overall healthier populace. This session will explore how digital health spaces can bridge the gap between these concepts. Discussions will focus on public health challenges and opportunities, examining the case of the city of Lisbon on how digital platforms can facilitate interactions between healthcare providers, patients, and urban environments. Best practices from pioneering projects around the globe will be highlighted. Participants will engage in comprehensive analyses of Lisbon case that demonstrate successful integration of digital health technologies in various contexts, contributing to the resilience and adaptability of healthcare infrastructures.

Data governance challenges on smart home involving multiple research sites

Abstract Background The development of Ambient Assisted Living (AAL) technology has great potential for ubiquitous health monitoring through collaboration between multiple research sites, but its use poses significant challenges to data governance. This study aims to investigate data governance challenges arising when smart home data flows across different research sites. Methods A four-analytic-dimension method was used to identify potential data-related issues that may occur when heterogeneous smart home data moves around multiple research sites. These dimensions include data domain, stakeholders, value, and governance goal. Results Data domain has difficulty in categorizing new data types generated by analytical models without a specified agreement, and determining the regulations for varying levels of anonymization and privacy protection is unclear in the AAL research field. Stakeholders in the AAL project may hold have different norms and practices for data use. Although some common values may be shared, such as improving the quality of homecare, conflicts may still arise and need to be regulated by data governance. Shifting AAL data between stakeholders may reduce its value, as different stakeholders may have varying interests. For instance, removing identifiable information from data to preserve privacy may reduce its value for researchers exploring individuals’ activity patterns. Governance goals are objectives for maintaining and facilitating data. While some governance goals can be widely acknowledged and shared, such as ensuring data security and privacy, conflicts may still exist over others, such as data access. Conclusions The big data generated by smart home technology highlights the need to develop proper data governance models to regulate data. This study’s implications include a better understanding of potential data-related challenges when data flows around multiple research sites, helping policymakers set up suitable data governance models. Key messages • AAL tech for health monitoring poses data governance challenges due to varying stakeholder norms and values. • Proper data governance models need to be developed for AAL data as it remains challenges in data domain, stakeholders, value, and governance goal.

L-GraphSAGE: A Graph Neural Network-Based Approach for IoV Application Encrypted Traffic Identification

Recently, with a crucial role in developing smart transportation systems, the Internet of Vehicles (IoV), with all kinds of in-vehicle devices, has undergone significant advancement for autonomous driving, in-vehicle infotainment, etc. With the development of these IoV devices, the complexity and volume of in-vehicle data flows within information communication have increased dramatically. To adapt these changes to secure and smart transportation, encrypted communication realization, real-time decision-making, traffic management enhancement, and overall transportation efficiency improvement are essential. However, the security of a traffic system under encrypted communication is still inadequate, as attackers can identify in-vehicle devices through fingerprinting attacks, causing potential privacy breaches. Nevertheless, existing IoV traffic application models for encrypted traffic identification are weak and often exhibit poor generalization in some dynamic scenarios, where route switching and TCP congestion occur frequently. In this paper, we propose LineGraph-GraphSAGE (L-GraphSAGE), a graph neural network (GNN) model designed to improve the generalization ability of the IoV application of traffic identification in these dynamic scenarios. L-GraphSAGE utilizes node features, including text attributes, node context information, and node degree, to learn hyperparameters that can be transferred to unknown nodes. Our model demonstrates promising results in both UNSW Sydney public datasets and real-world environments. In public IoV datasets, we achieve an accuracy of 94.23%(↑0.23%). Furthermore, our model achieves an F1 change rate of 0.20%(↑96.92%) in α train, β infer, and 0.60%(↑75.00%) in β train, α infer when evaluated on a dataset consisting of five classes of data collected from real-world environments. These results highlight the effectiveness of our proposed approach in enhancing IoV application identification in dynamic network scenarios.

RCEP rules on cross-border data flows: Asian characteristics and implications for developing countries

ABSTRACT Data, as an essential production factor and a fundamental strategic resource in the digital economy era, have profoundly transformed production methods, lifestyles, and social governance, becoming one of the critical factors influencing the global competitive landscape. The United States and the European Union are actively making comprehensive arrangements around data flow issues, integrating their interests and values into data governance rules to influence other countries. Against the backdrop of increasingly intense global rule-making competition in digital trade, the Regional Comprehensive Economic Partnership (RCEP), led by the Association of Southeast Asian Nations (ASEAN) and actively promoted by China, covering 15 countries in the Asia-Pacific region, is sending a strong and powerful Asian voice regarding cross-border data flow rules. This article attempts to highlight the Asian characteristics of the data cross-border flow rules within RCEP through a horizontal comparison of regional trade agreements. These characteristics primarily include the principle of free cross-border data flow; fully protecting the host country’s national security through exceptional clauses; relatively lenient regulation of personal information protection; and adhering to the principle of non-reciprocity to respect and embrace countries at different development levels. Consequently, it is concluded that RCEP has constructed an Asian paradigm for cross-border flow of data that matches the development status of the Asia-Pacific countries and is worthy of emulation by other developing nations.

De- and recoding algorithmic systems: The case of fact checkers and fact checked users

With the recent development of debunking on social media as a dominant agenda, fact checkers have increasingly used machine learning (ML) to identify, verify and correct factual claims, as ML promises the scaling of fact checking practices. However, it also places a new actor in between the fact checkers and the fact checked users. In this paper, we conducted a contrasted analysis of how fact checkers and fact checked users understand, evaluate and act towards the algorithmic system and the data flows in Meta’s Third-Party Fact-Checking Program: We did ethnographic fieldwork in the fact checking newsroom and interviewed and did protocol analysis with the fact checked. For both professional users and end users, the algorithmic system is experienced as a black box in which they have limited insight, and their sense-making practices happen based on the data and metrics that are made visible to them. In the paper, we draw on and expanded theory on decoding algorithms by not only exploring how the two user groups engage in decoding the algorithmic system, but also actively engage in forms of recoding by attempting to adapt or modify the algorithmic system to better fit within their cultural and social context, which is characterised by both varying epistemic cultures and societal positions. While the fact checkers from their hegemonic (sometimes negotiated) position understand the program as a (sometimes stupid) tool and primarily engage in passive acts of recoding, fact checked users, from their oppositional position, understand the program as an unpredictable censoring machine and engage primarily in more active acts of recoding. Based on the analysis, we end the paper with a discussion in which we argued for understanding data reflexivity as highly relational and processual.

SensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.

Make data flow: Understanding the (re)usability of research data

Opening data and making datasets reusable is an extremely important and urgent need, especially as we move towards more data-driven studies worldwide. Various stakeholders are involved in the life cycle of data (re)use, including data providers/sharers, data (re)users, data managers/curators, and funding agencies. In response to the divergent demands of these stakeholders, data repositories have been established to bridge their needs and facilitate the cycle of data sharing and (re)use. However, recent studies have shown that data is being shared and (re)used through open data repositories at much lower levels than expected. This underscores a fundamental and often overlooked challenge in research data management, inviting a deeper and more comparative examination of the perspectives of various stakeholders on data (re)use. Using a mixed-method approach that includes metadata analysis, interview-based qualitative studies, and quantitative bibliometric analysis, this project first delves into the facilitating and inhibiting factors affecting a dataset's potential for (re)use, considering the perspectives of both data (re)users and data curators. It will then identify and elucidate the disparities between data (re)users and data curators, proposing suggestions for future data curation efforts. The findings will prompt data curators and data repositories to re-examine their current metadata frameworks and data curation practices, aligning them more closely with the actual needs of data (re)users. Moreover, this effort will make the flow of research data moves more smoothly within our academic communities, and contribute to the building of a more inclusive, equitable, open, and user-friendly research infrastructure.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

Building a Big Data Platform for a Financial Management Company: Preparing Data for Reporting with Automated Data Flow

This paper focuses on how financial management companies can use big data platforms to automate the flow of data for accurate and timely financial reporting. By leveraging automation, we can reduce human intervention and errors in the reporting process. This paper explains how data is ingested, processed, and transformed automatically, and how automation tools like Apache Airflow can help manage the data flow. We also provide diagrams, flowcharts, and pseudocode to give a clear understanding of the design and processes involved.

Health Care Workers' Perspectives on Collecting Sexual Orientation and Gender Identity in the Adult Primary Care Setting.

Despite numerous calls for standardized collection of sexual orientation and gender identity (SOGI) data in clinical settings, uptake of this practice still lags. This study conducted a preimplementation assessment of staff attitudes toward SOGI data collection within an adult primary care practice in an urban academic medical center in the northeastern United States. We created a process map of the flow of patient data from the point of registration to the clinical encounter to identify all staff roles associated with registration and patient demographic data collection. We purposively sampled staff members across these roles and conducted semistructured virtual interviews between November 2021 and February 2022. The research team used deductive and inductive coding and conducted a thematic analysis to identify barriers and facilitators to implementation. Nine clinical staff and eleven nonclinical staff were interviewed. Participants were asked about their general experiences with lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients, their perspectives on collecting this data, and potential barriers and facilitators to incorporating this into the workflow. The main themes that emerged were the relevance of SOGI data to the clinical practice; concerns about patient acceptability; the prevalence of cis-gender, heteronormative assumptions; and concerns about linguistic, cultural, and generational differences. Differences were noted between clinical and nonclinical staff. Greater education is needed to help both clinical and nonclinical staff understand how patients' SOGI demographics can be used to provide affirming, patient-centered care. Implementation strategies can be tailored to address specific barriers at the individual, organizational, and social levels.

Fluctuations and extreme events in the public attention on Italian legislative elections

The share of social media attention to political candidates was shown to be a good predictor of election outcomes in several studies. This attention to individual candidates fluctuates due to incoming daily news and sometimes reflects long-term trends. By analyzing Twitter data in the 2013 and 2022 election campaign we observe that, on short timescales, the dynamics can be effectively characterized by a mean-reverting diffusion process on a logarithmic scale. This implies that the response to news and the exchange of opinions on Twitter lead to attention fluctuations spanning orders of magnitudes. However, these fluctuations remain centered around certain average levels of popularity, which change slowly in contrast to the rapid daily and hourly variations driven by Twitter trends and news. In particular, on our 2013 data we are able to estimate the dominant timescale of fluctuations at around three hours. Finally, by considering the extreme data points in the tail of the attention variation distribution, we could identify critical events in the electoral campaign period and extract useful information from the flow of data.

Encyclopædia Inflationaris

The current flow of high-accuracy astrophysical data, among which are the Cosmic Microwave Background (CMB) measurements by the Planck satellite, offers an unprecedented opportunity to constrain the inflationary theory. This is however a challenging project given the size of the inflationary landscape which contains hundreds of different scenarios. Given that there is currently no observational evidence for primordial non-Gaussianities, isocurvature perturbations or any other non-minimal extension of the inflationary paradigm, a reasonable approach is to consider the simplest models first, namely the slow-roll single-field models with minimal kinetic terms. This still leaves us with a very populated landscape, the exploration of which requires new and efficient strategies. It has been customary to tackle this problem by means of approximate model-independent methods while a more ambitious alternative is to study the inflationary scenarios one by one. We have developed the publicly available runtime library ASPIC11https://github.com/cosmicinflation/aspic. to implement this last approach. The ASPIC code provides all routines needed to quickly derive reheating-consistent observable predictions within this class of scenarios. ASPIC has been designed as an evolutive code which presently supports 118 different models. In this paper, for each of the ASPIC models, we present and collect new results in a systematic manner, thereby constituting the first Encyclopædia Inflationaris.

Intelligent Resource Monitoring and Control Method in Vehicular Ad-hoc Networks for Electric Vehicle Enabled Microgrids

Software-Defined Networks (SDN) and Cloud Radio Access Networks (CRANs) are added to vehicle ad hoc networks (VANETs). The goals include making data transfer and resource sharing more efficient, achieving the shortest possible wait and response times, and ensuring the network is reliable even when conditions change. The usual ways of handling and grouping loads in Electric Vehicles (EVs), which are made for stable environments, need to be changed to work with VANETs, which are constantly evolving. To regularly meet these high service levels, focus on more adaptable and durable solutions. This study shows a software-defined EV fog computing design that improves VANETs' resource sharing. The suggested design uses smart controls placed strategically in the network to make the flow of data and use of resources as efficient as possible. The system uses parallel processing to split up computing tasks among EV stations. This makes the network more mobile and lessens the chance of jams. Simulations and real-world tests of the model show that it makes the network much more efficient. The study found that compared to traditional methods, the average response time went up by 29%, network delay went down by 23%, and it took 27% less time to get to the best assets spread.

The Impact of the Digital Economy on International Trade Models: Opportunities and Challenges

With the rapid development of the digital economy, international trade models are undergoing profound changes. Digital technology has reshaped traditional trade structures, driving the reorganization of global value chains and the rise of intangible assets. This paper explores the new characteristics of international trade in the context of the digital economy, including the transformation of trade structures driven by digitalization, the increasing importance of intangible assets in trade, and the influence of digital ecosystems. The digital economy has made trade processes more convenient and efficient, offering new globalization opportunities for emerging markets and small and medium-sized enterprises (SMEs), and promoting the growth of cross-border services and digital products. However, challenges remain, such as disparities in digital infrastructure, conflicts between digital governance and cross-border data flows, and the diversity of digital trade rules. This paper summarizes these opportunities and challenges and suggests future research directions to help policymakers, businesses, and technology providers navigate the complex landscape brought about by the digital economy.

Edge-featured multi-hop attention graph neural network for intrusion detection system

With the development of the Internet, the application of computer technology has rapidly become widespread, driving the progress of Internet of Things (IoT) technology. The attacks present on networks have become more complex and stealthy. However, traditional network intrusion detection systems with singular functions are no longer sufficient to meet current demands. While some machine learning-based network intrusion detection systems have emerged, traditional machine learning methods cannot effectively respond to the complex and dynamic nature of network attacks. Intrusion detection systems utilizing deep learning can better enhance detection capabilities through diverse data learning and training. To capture the topological relationships in network data, using graph neural networks (GNNs) is most suitable. Most existing GNNs for intrusion detection use multi-layer network training, which may lead to over-smoothing issues. Additionally, current intrusion detection solutions often lack efficiency. To mitigate the issues mentioned above, this paper proposes an Edge-featured Multi-hop Attention Graph Neural Network for Intrusion Detection System (EMA-IDS), aiming to improve detection performance by capturing more features from data flows. Our method enhances computational efficiency through attention propagation and integrates node and edge features, fully leveraging data characteristics. We carried out experiments on four public datasets, which are NF-CSE-CIC-IDS2018-v2, NF-UNSW-NB15-v2, NF-BoT-IoT, and NF-ToN-IoT. Compared with existing models, our method demonstrated superior performance.

Optimization of LightGBM for Song Suggestion Based on Users’ Preferences

Undoubtedly, music possesses the transformative ability to instantly influence an individual's mood. In the era of the incessant flow of substantial data, novel music compositions surface on an hourly basis. It is impossible to know for an individual whether he/she will like the song or not before listening. Moreover, an individual cannot keep up with this flow. However, with the help of Machine Learning (ML) techniques, this process can be eased. In this study, a novel dataset is presented, and song suggestion problem was treated as a binary classification problem. Unlike other datasets, the presented dataset is solely based on users' preferences, indicating the likeness of a song as specified by the user. The LightGBM algorithm, along with two other ML algorithms, Extra Tree and Random Forest, is selected for comparison. These algorithms were optimized using three swarm-based optimization algorithms: Grey Wolf, Whale, and Particle Swarm optimizers. Results indicated that the attributes of the new dataset effectively discriminated the likeness of songs. Furthermore, the LightGBM algorithm demonstrated superior performance compared to the other ML algorithms employed in this study.

EuMAR stakeholder engagement: an analysis of medically assisted reproduction (MAR) data collection practices in EU countries†.

What are the current national medically assisted reproduction (MAR) data collection systems across EU Member States, and how can these countries contribute to a unique, cycle-by-cycle registry for the European Monitoring of Medically Assisted Reproduction (EuMAR) project? The study identified significant variation in MAR data collection practices across Member States, with differences in data types, collection methods, and reporting requirements; the EuMAR project emerges as an opportunity to enhance data standardization and improve MAR data collection in the EU. There is a need for new approaches in MAR data collection that include long-term and cross border follow-up. The EuMAR project intends to establish a unified, cycle-by-cycle registry of data on MAR treatments in EU countries, from which accurate cumulative outcomes can be calculated. This cross-sectional study involved a survey and interviews with stakeholders from 26 EU Member States conducted in 2023 over a period of seven months. Representatives from national competent authorities and professional associations involved in MAR data collection in EU countries were invited to complete the survey and interviewed to assess current data flows, information requirements, and their interest in the EuMAR project. Half of the participating countries reported having a national MAR registry with cycle-by-cycle data (n = 13), while 31% reported having a national registry with aggregated data (n = 8) and 19% reported having no national registry (n = 5). Of the countries with a national cycle-by-cycle registry, eight countries collect identifiable data, five countries collect pseudonymized data, and one country collects fully anonymized data. Informed consent is required in 10 countries. The main advantages that participants expected from a European registry like EuMAR were the possibility of obtaining national statistics in the absence of a national registry and improving the calculation of cumulative outcomes. The results of the study are based on self-reported data, which may be subject to bias, however, the validity of the collected information was verified with different means, including follow-up calls for clarifications and sharing final transcript reports. The feasibility of the proposed data flow models will be tested in a pilot study. Despite the heterogeneity of data collection practices across EU countries, the results show that stakeholders have high expectations of the benefits that the EuMAR registry can bring, namely the improvement of data consistency, cross-border comparability, and cumulative live birth rates, leading to better information for patients, health care providers and policy makers. The EuMAR project was co-founded by ESHRE and the European Commission (101079865-EuMAR-EU4H-2021-PJ2). No competing interests were declared. N/A.

Review of Knowledge on the Graphs of Cyber-Attacks by 2024 for IBIMA Publishing Journals

An attack graph is a visual tool used to represent potential attack paths, that a cybercriminal can use to gain access to a computer system or network. It consists of nodes and edges, where Nodes represent resources such as computers, servers, applications or data. Edges represent dependencies between nodes, such as network connections or data flows. Attack graphs are visual tools used to analyse and understand different types of attacks, including cyber-attacks, physical attacks and terrorist attacks. These visualisations help security professionals, analysts and decision makers to identify trends, assess risks and develop defence strategies. Attack graphs are a valuable tool that can help organisations identify, assess and mitigate cyber security risks. They are easy to use and can be customised to suit different needs.

An enhanced Deep-Learning empowered Threat-Hunting Framework for software-defined Internet of Things

The Software-Defined Networking (SDN) powered Internet of Things (IoT) offers a global perspective of the network and facilitates control and access of IoT devices using a centralized high-level network approach called Software Defined-IoT (SD-IoT). However, this integration and high flow of data generated by IoT devices raises serious security issues in the centralized control intelligence of SD-IoT. Motivated by the aforementioned challenges, we present a new Deep-Learning empowered Threat Hunting Framework named DLTHF to protect SD-IoT data and detect (binary and multi-vector) attack vectors. First, an automated unsupervised feature extraction module is designed that combines data perturbation-driven encoding and normalization-driven scaling with the proposed Long Short-Term Memory Contractive Sparse AutoEncoder (LSTMCSAE) method to filter and transform dataset values into the protected format. Second, using the encoded data, a novel Threat Detection System (TDS) using Multi-head Self-attention-based Bidirectional Recurrent Neural Networks (MhSaBiGRNN) is designed to detect cyber threats and their types. In particular, a unique TDS strategy is developed in which each time instances is analyzed and allocated a self-learned weight based on the degree of relevance. Further, we also design a deployment architecture for DLTHF in the SD-IoT network. The framework is rigorously evaluated on two new SD-IoT data sources to show its effectiveness.

On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.