Salvador Sánchez-Alonso’s research while affiliated with King Juan Carlos University and other places

The growing adoption of wearables is driving the demand for personalized services that leverage unprocessed data, such as biometric and health information, to enhance user experiences and support through software applications. However, several existing use cases involving this information still prioritize traditional schemes, neglecting user privacy. Consequently, the transparency of data transmission paths and the potential for tampering remain ambiguous when users share data with service providers. In this paper, we propose the application of an Internet of Things device-focused distributed ledger as an underlying layer for the transmission of encrypted data using streams. Moreover, our proposal enables data recording for future events and the implementation of multi-subscriber models, allowing client information to be shared securely with different service providers. Through simulation experiments conducted on constrained devices, we demonstrate that our proposed framework efficiently transmits large ciphertexts through streams on a distributed ledger, overcoming the inherent limitations of such networks when dealing with substantial data volumes. Ultimately, the performance metrics presented prove that the proposed model is suitable for real-world applications requiring continuous data collection by wearables and subsequent transmission to service providers.

The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.

In the rapidly evolving digital health landscape, technology plays a pivotal role in transforming the healthcare industry. With the exponential growth of data, uncovering valuable insights has become a daunting task. In today's data-driven world, healthcare businesses must leverage emerging technologies to stay informed about trends in their field. This research article presents a novel approach to deriving business insights in digital health enabled by technology, including artificial intelligence, and other cutting-edge advancements. We propose a methodology that utilizes news mining techniques and the global data on events, location, and tone database as the primary data source. By employing natural language processing, we developed a practical way of extracting relevant insights from vast amounts of public data. We implemented named-entity recognition (NER) enriched with the DBpedia knowledge base and relationship extraction. In addition, we leveraged graph analytics to identify and analyze the most significant concept relationships within the text corpus and their evolution in time. By integrating these advanced techniques, healthcare businesses can extract actionable insights from public datasets, empowering them to stay abreast of emerging trends and advancements in digital health, such as telehealth, precision medicine, or medical imaging.

Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether.

As they swiftly evolve and become widely adopted, new technologies are fundamentally transforming the landscape of business models. Digital transformation has become a top priority for business leaders, and the Covid-19 pandemic has significantly accelerated this trend. As a result, the healthcare industry is also rapidly evolving. The goal of this research is to understand the impact of digital transformation (DX) in the healthcare industry and identify the main trends, opportunities and challenges by leveraging user-generated content through Twitter analytics. The analysis of textual information allows for the generation of insights and prescriptive analytics from aggregated unstructured data. Between January 2017 and December 2021, 96,826 English-language tweets on digitisation and digital transformation in healthcare were collected. The method consisted of a series of experiments of co-occurrence network topic modelling, based on graph analytics and semantic analysis. The results are prescriptive on the development of patient-centric digital health. The results are linked to the development of personalised healthcare, mobile health (mhealth) and efficiencies derived from adopting technology, especially artificial intelligence, machine learning and cloud computing. However, certain challenges must be addressed to implement digital transformation strategies. These challenges include ensuring compliance with data privacy regulations, as well as managing the changes required in legacy systems and processes.

Interest in healthcare has grown significantly worldwide, especially since the Covid-19 outbreak. Digitalisation has allowed users to interact on social networks through platforms like Twitter, collecting user interactions over time, resulting in the proliferation of fake news. This research aims to analyse, evaluate and classify the predictive potential of Twitter analytics in healthcare, identifying the latent knowledge insights and distinguishing them from related rumours and fake news. Thus, a systematic literature review (SLR) is carried out to identify and analyse the existing academic research and applications in Twitter in predicting healthcare. The most important predictive applications are detecting mental health issues and public health emergencies. Covid-19 has been the main topic of most of the studies linked to fake news and misinformation, and this research provides a practical contribution to the use of unstructured data from Twitter and raises awareness of the importance of this content applied to healthcare. Therefore, it is pertinent to focus on the advances offered by these data as a predictive tool in healthcare since it is essential, to this end, to evaluate the veracity of the information shared on Twitter.

Blockchain technologies have demonstrated the potential to build decentralized finance (DeFi) protocols that are composable and interoperable. One of the envisioned applications of blockchains is that of becoming a platform for markets of greenhouse-gas emissions. KlimaDAO is one of the recently launched protocols that attempts to bridge existing voluntary carbon markets to DeFi by means of building incentive mechanisms on top of tokenized carbon assets. That approach may eventually bring benefits to carbon markets in terms of liquidity and transparency and address a wider audience. Here we report the analysis of the early status of that initiative in an attempt to get insights in its actual functioning and value, and on the extent to which they are currently addressing the original goals and potential benefits of this kind of protocols.

Millions of people use Twitter daily, posting thousands of messages and interacting with their peers. This research aims to evaluate and classify the predictive potential of the Twitter social platform through the intelligent analysis of user-generated public big data analytics. A systematic literature review (SLR) covering Web of Science, IEEE, Scopus and other databases identified the gaps and opportunities for developing predictive applications of User-Generated Content (UGC) on Twitter since 2006. Our research is a practical contribution to the use of Twitter data as a predictive system. A wide variety of application domains, highlighting social network analysis and public health, have been identified by applying innovative techniques for conducting a massive SLR, leveraging machine learning and graph analysis. The results give rise to new research lines with implications for both scholars and business leaders.

This chapter discusses prospects for the measurement of quantum software artifacts and processes, describing initial directions and reviewing the scattered and scarce literature on the topic. It examines potential differences and commonalities of classical and quantum software in the context of measurement and identifies future research directions that appear to be more promising to address the specificities of quantum computer–oriented development.

Nowadays, information and communication technologies (ICTs) and virtual training have increased the use of educational resources. This growth use has highlighted educational resource reuse and availability problems. Resource descriptions adapted to particular needs and the lack of metadata enrichment taking advantage of the benefits provided by the Semantic Web are some examples of these problems. The purpose of this paper is to expose an enhanced and interoperable set of metadata elements for describing OER (Open Educational Resources), which takes full advantage of the Openness and data enrichment. In this research, requirements such as data quality dimensions, Linked Data, and mapping into RDF (Resource Description Framework) Graph, have been taken into account to provide well‐described, available, reusable and enriched OER, in addition, to display them as Linked Data. These features contribute to the educational field strengthen the processes of Opening, Availability, Reuse, and Linking OER. In a nutshell, these features are necessary to facilitate innovative educational settings. Finally, this improved OER description can be extrapolated to other countries, serving as a potential Opening and reuse guidelines to publish OER on both applications and LOD (Linked Open Data) Cloud.