Iskanje po katalogu digitalne knjižnice

Opis: One possible way to gain access to competitively relevant sets of Big Data is toapply the essential facilities doctrine. However, the European Commission and theEuropean Court of Justice have established several different criteria for applying thedoctrine. Since neither institution has yet applied the doctrine in Big Data accesscases, it is not clear which of the criteria applies in such positions. This paper attemptsto analyze the impact of the “objective test” and the requirement that the controllingcompany be active in the downstream market (which are included in all assessmentcriteria) in Big Data access cases, with the goal of answering the research question,“Do the application of the “objective test” and the requirement that the controllingcompany be active in the downstream market impede the effectiveness of the doctrinein Big Data access cases under EU competition law, and if so, how should they bechanged?” The conclusion is that in Big Data access cases, the “objective test” shouldbe mitigated and replaced by the “subjective test” or the “average company test” andthe requirement that the controlling company be active in the downstream marketshould be discarded altogether in order for the doctrine to be an effective tool foraccessing competitively relevant sets of Big Data.
Ključne besede: essential facilities, doctrine, big data, mandated data access, Bronner ruling
Objavljeno v DKUM: 29.08.2025; Ogledov: 0; Prenosov: 10
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Opis: This paper investigates emerging technological trends in web commerce and examines user perceptions through a pilot survey. Key innovations include IoT, AI, ML, chatbots, social and voice commerce, as well as immersive technologies like AR and VR. These tools enhance personalization, automate services, and support better consumer decision-making. The pilot study, based on an online questionnaire, explores user behaviour, expectations, and attitudes toward these developments. Results show that while users are familiar with technologies like chatbots and personalization, awareness of AR and VR remains low. Concerns about security and data privacy significantly influence user trust. The findings highlight the need for seamless technological integration, increased user education, and transparent communication to build trust and improve the digital shopping experience.
Ključne besede: web commerce, e-commerce trends, internet of things (IoT), artificial intelligence (AI), machine learning (ML), chatbots, augmented reality (AR), virtual reality (VR), voice commerce, user perception, digital transformation, data privacy
Objavljeno v DKUM: 29.08.2025; Ogledov: 0; Prenosov: 7
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Opis: Current trends suggest a future in which big data can be used to predict individual health risks and outcomes with exceptional accuracy. By analyzing large datasets that include various health indicators, algorithms can predict the onset of disease, allowing for early intervention and personalized treatment plans. However, the legal and ethical implications of predictive health analytics, such as potential misuse of predictive information or unfair discrimination based on health risks, require careful consideration. In terms of global health surveillance, the COVID-19 pandemic has highlighted the potential of big data to track the spread of disease and inform public health responses.
Ključne besede: COVID-19, data ownership, GDPR, medical data, privacy law
Objavljeno v DKUM: 28.08.2025; Ogledov: 0; Prenosov: 2
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Opis: : The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where they occur. This research presents a structured approach to developing a taxonomy for open government data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset of 697 OGD use cases, we employed various machine learning techniques—including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP)—to extract thematic categories and construct a structured taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health, Infrastructure, Education, Innovation, Governance, and Environment, each with specific subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond governance and transparency, influencing education, sustainability, and public services. Our approach provides a scalable and data-driven methodology for categorizing OGD impact areas compared to previous research that relies on predefined classifications or manual taxonomies. However, the study has limitations, including a relatively small dataset, brief use cases, and the inherent subjectivity of taxonomic classification, which requires further validation by domain experts. This research contributes to the systematic assessment of OGD initiatives and provides a foundational framework for policymakers and researchers aiming to maximize the benefits of open data.
Ključne besede: open government data, topic modeling, taxonomy development, machine learning
Objavljeno v DKUM: 28.08.2025; Ogledov: 0; Prenosov: 4
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Opis: Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender equality endorsement, collectivistic attitudes, personal history of pathogenic diseases, relationship quality, jealousy, personal involvement in sexual and/or emotional infidelity, relational mobility, mate preferences, and acceptance of sugar relationships. The resulting dataset provides a rich resource for investigating patterns within, and associations across, a broad range of variables relevant to romantic relationships, with extensive opportunities to analyze individual experiences worldwide.
Ključne besede: cross-cultural data, romantic love, mate preferences
Objavljeno v DKUM: 27.08.2025; Ogledov: 0; Prenosov: 7
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