Going Beyond Citizen Data Collection with Mapster: A Mobile+Cloud Real-Time Citizen Science Experiment

The paper reports on a case study regarding the integration of heterogeneous data sources, coming from different institutions, needed to support several studies related to the health status of the population of L’Aquila (Italy) after the earthquake of April 6th 2009. In detail, the paper initially describes all the health studies, then the data sources required to support them, finally proposes a simplified federated architecture and a straightforward technological solution to implement it.

Ontologies enable explicit expression of collective concepts and support Machine-to-Machine (M2M) interactions at the semantic level. Ontologies expressed in a standard language, such as the Web Ontology Language (OWL) and exposed on a network offer the potential for unprecedented interoperability solutions since they are semantically rich, computer interpretable and inherently extensible. In this chapter, we describe how we applied ontologies in OWL for rapid enterprise integration of heterogeneous data sources to track objects in a battlespace. We found that once a robust foundational domain ontology is established, it is easy and quick to integrate new data sources and therefore rapidly provide new system capabilities. In particular, we demonstrate how moving tracks can be quickly integrated with intelligence and space events to provide enhanced situational awareness using ontologies. This chapter also describes the overall SEER and SWORIER systems we developed, the latter of which translated OWL ontologies (and RDF instances) and Semantic Web Rule Language (SWRL) rules into Prolog, applied knowledge compilation techniques, and then at runtime, utilized a combined OWL/logic programming reasoned for efficient automated reasoning. We also briefly describe a more recent extension to the prototype and to the ontologies that we made to address more rigorous geospatial rules for unmanned autonomous vehicle (UAV) avoidance. Finally, we consider some issues raised by our work and future lines of research to address these.

Ontologies enable explicit expression of collective concepts and support Machine-to-Machine (M2M) interactions at the semantic level. Ontologies expressed in a standard language, such as the Web Ontology Language (OWL) and exposed on a network offer the potential for unprecedented interoperability solutions since they are semantically rich, computer interpretable and inherently extensible. Rules that operate over ontologies allow for fusion of events in a semantically rich way. In this paper, we describe how we applied ontologies and rules for rapid enterprise integration of heterogeneous data sources and aggregation of events in the battlefield. We found that once a robust foundational domain ontology is established, it is easy and quick to integrate new data sources and therefore rapidly provide new system capabilities. We also found that rules that operate over ontological concepts are useful in agile aggregation of events for enhanced situational awareness.

<p>One of the goals of both WMO and the German Hans Ertel Centre for Weather Research (HErZ) is to “spread the word” about the research on the use of weather and climate services for society, e.g. through training or conferences bringing together national weather services and the research and user communities.. WMO’s Societal and Economic Research Application Working Group and HErZ joined forces to invite the weather community to actively participate in the “1st Weather and Society Conference”. The focus of the Conference was the science for services approach adopted by the WMO to understand, analyse and enhance the value of weather and climate services in society. </p><p>The objectives of the conference were to:</p><ul><li>discuss and promote all aspects of social and economic research applications along the weather forecast value chain</li> <li>bring people together from the operational meteorological and hydrological services, research and forecast user communities</li> <li>identify gaps, needs and challenges to reducing the risks to citizens and society </li> </ul><p> </p><p>To allow a truly global participance, the conference was free of costs to the participants and organised to accommodate as many time zones as possible. Sessions in the first week were held from 0800 to 1100 UTC, while the sessions in the second week ran from 2000 to 2300 UTC. Each two-hour session was convened in WebEx, contained up to six oral presentations and started with a keynote talk that covered a general overview of the theme. GatherTown turned out to be a very useful platform for the following more interactive poster session or for further easy to arrange discussions.  Overall, the conference comprised 157  presentations and welcomed 749 registrations from 104 countries. </p><p>Each session was accompanied with an online survey on the gaps and needs on the topic of the session. One of the interesting results was the differences in motivations to join the f different sessions, measured in 5 dimensions of interests(guidance and methods, meeting experts in the field, challenges and opportunities, networking opportunities, academic knowledge). The selected topics of the conference reflected the weather information value chain (Citizen Science & Observations;   Estimating and improving the socio-economic benefit of weather information;Understanding the communication of weather forecast uncertainty; The First Mile of Weather and Climate Information and Warning;Impact based forecasting and warnings of extreme weather events ; Indigenous and local weather knowledge;  Value chain of Early Warning System; Use of weather information for civil protection, emergency management and humanitarian aid; The last mile challenges of seamless weather and climate information. </p><p>In the presentation we will highlight some of the gaps and needs identified in the presentations, the sessions surveys and from a post conference survey.</p>

A method and software framework for enriching private biomedical sources with data from public online repositories

The multi-national MIDAS (Meaningful Integration of Data Analytics and Services) project is developing a big data platform to facilitate the utilisation of a wide range of health and social care data. The platform will enable the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and bespoke visualisations of actionable epidemiological data. An evaluation framework starting with a logic model and using the principles of realist evaluation developed working with users, and software developers. The tools used are a series of parallel case studies to address the requirements of stakeholder groups at critical time points during the project to ensure IT systems development is in line with user’s requirements. The process includes longitudinal interviews with stakeholders, regular feedback to users and developers, and measurement of stakeholder’s attitudes to the project using Q-methodology.

In recent years, research on digital twins in the construction sector has grown significantly, particularly since 2018. However, most studies focus on isolated lifecycle phases and give limited attention to developing practices that address all stages of the building lifecycle. Current applications of digital twins in construction are largely concentrated on design and engineering, where Building Information Modeling (BIM) serves as the primary input. This dominance stems from BIM's role as an existing digital representation of built assets, which can be extended into a digital twin. One of the major barriers to wider implementation lies in data integration and interoperability—specifically, the challenge of consolidating information from diverse sources, formats, and lifecycle stages. Future research should prioritize the standardization of data formats, protocols, and application programming interfaces (APIs) to facilitate seamless exchange across digital twin systems. Additionally, the development of advanced data governance frameworks is essential to ensure accuracy, completeness, and security of information. Greater exploration into semantic data modeling and ontologies will also support the integration of heterogeneous data sources. Beyond technical issues, it is equally important to investigate the costs, benefits, and potential drawbacks of adopting digital twins across the entire building lifecycle. Expanding the scope of applications beyond design and engineering to include construction, operation and maintenance, as well as demolition and recovery phases, will be crucial. This paper therefore outlines the current state of digital twin research in construction, identifies critical challenges, and highlights key directions for future development.

Businesses find it hard to combine data from different systems, formats, and sources. Some examples of these sources are structured data in relational databases, semi-structured data like JSON or XML, and unstructured data like text or multimedia files. Businesses need to be able to handle and mix all of these different types of data in order to get the most out of them. You can now utilize an architecture to put together data from languages that are distinct. This framework is meant to help you find a way to handle a lot of various types of data and sources without slowing down or losing consistency. Cloud storage, APIs, and machine learning are just a few of the cutting-edge technologies that the framework leverages to make sure that data systems can work together without any hassles. It allows businesses to combine their data while ensuring that all of their systems are accurate and high-quality. The platform also solves the problem of scalability, which means that companies can simply handle more and more data without any issues or delays. This plan helps businesses maintain better track of their data, which makes it easier to put together and less likely to make mistakes. Companies should always be able to see their data in the same way, no matter where it comes from or what format it is in. This makes it easier to choose. The answer also helps with data governance since it allows people ways to keep track of data history, set security standards, and make sure they follow the rules. When you have to interact with diverse kinds of data, the polyglot data integration framework is an excellent method to deal with problems that come up. In a world where data is king, it helps businesses get the most out of their data, keep things running smoothly, and remain ahead of the competition

In an era where data is the cornerstone of decision-making, organizations face the challenge of integrating diverse data sources from heterogeneous environments. Traditional Extract, Transform, and Load (ETL) processes focus primarily on syntactic transformations, ensuring compatibility between disparate datasets. However, these approaches fall short in addressing semantic inconsistencies that arise from differences in meaning, context, and relationships among data [6][7]. This paper proposes a semantic-aware ETL framework that integrates ontology-based reasoning and dynamic rule generation to bridge this gap. The framework enhances data coherence and alignment by leveraging cutting-edge semantic technologies, including Apache Jena for ontology management, SPARQL for semantic querying, and Protégé for ontology design. Detailed analysis, design, and evaluation of the framework showcase its ability to revolutionize ETL processes in data warehousing, enabling robust integration of heterogeneous data sources.

The convergence of distributed cloud computing, real-time analytics, and advanced machine-learning architectures is reshaping the landscape of wealth management and robo-advisory services. As financial institutions increasingly face client expectations for personalization, transparency, and continuous portfolio optimization, traditional advisory models built on static risk profiles and infrequent rebalancing cycles are proving insufficient. Distributed cloud intelligence offers an architectural breakthrough by enabling scalable data processing, cross-platform orchestration, and secure integration of heterogeneous data sources, including behavioral, transactional, and market-microstructure signals. At a broad level, these capabilities allow wealth-management platforms to generate more adaptive, context-aware investment recommendations aligned with rapidly shifting market conditions and client life events. Narrowing the focus, federated learning, serverless architectures, and edge-intelligence layers enable hyper-personalized advisory engines capable of executing micro-segmented portfolio strategies without compromising data privacy or regulatory compliance. These systems fuse real-time risk modeling, sentiment analysis, and multi-scenario forecasting to tailor asset allocation dynamically, optimizing for liquidity needs, volatility preferences, sustainability mandates, or tax-efficient structuring. Distributed cloud frameworks also support continuous rebalancing workflows, anomaly detection, and personalized investment nudges delivered through conversational interfaces, digital advisers, or autonomous execution mechanisms. This article proposes an integrated design framework for leveraging distributed cloud intelligence to advance next-generation wealth-management solutions. It outlines the architectural requirements, model-governance controls, privacy-preservation techniques, and orchestration patterns necessary for building compliant, resilient, and explainable hyper-personalized robo-advisory systems. By aligning distributed analytics with modern regulatory expectations and client-centric portfolio strategies, financial institutions can deliver richer insights, operational flexibility, and transformative personalization in an increasingly competitive advisory landscape.

Ontology matching is the process of finding semantic relationships between entities of different ontologies. It plays an important role in the integration of heterogeneous data sources. To find semantic correspondences between entities, various matchers can be used. When multiple matchers are integrated into a matching system, the issue of combining these matchers together and determining the importance of every basic matcher arises. In a parallel composition of basic matchers, several matchers are used independently and then their results are aggregated. In this paper, we propose a method for automatically determining weights of basic matchers in their parallel composition and the architecture of the matching system. An evaluation of the method is performed on case study examples concerning the matching of four ontology pairs and a comparison with other aggregation methods is given. Additionally, the architecture of the matching system based on our automatic weighted aggregation method is presented.

Artificial intelligence (AI) has rapidly emerged as a powerful tool in the global effort to prevent and control infectious diseases. With the growing burden of pandemics, antimicrobial resistance (AMR), and travel-related outbreaks, traditional public health responses face significant challenges in speed, scale, and coordination. AI technologies—ranging from machine learning (ML) and natural language processing (NLP) to deep learning and generative models—offer new capabilities across surveillance, early outbreak detection, diagnostics, modeling, vaccine development, public communication, and resource allocation. This review synthesizes the expanding applications of AI in infectious disease prevention and control and highlights its transformative potential in improving preparedness and response. AI enables real-time integration of heterogeneous data sources, supports precision diagnostics in low-resource settings, accelerates vaccine target identification, and enhances public health communication during crises. However, its implementation is not without challenges. These include data quality and bias, privacy and security, technical limitations, transparency and trust, ethical concerns, misinformation, regulatory gaps, cost and infrastructure constraints, and system fragmentation. To maximize AI’s benefits, policy frameworks should promote equitable data access, interdisciplinary collaboration, and inclusive governance mechanisms. Technical development should also prioritize transparency, adaptability, and contextual relevance. AI has the potential to reshape global infectious disease prevention and contr

In this paper we propose an ontological OLAP (on-line analytical processing) framework to integrate distributed energy sensor data. The OLAP data cube in the framework annotated with semantics with other supporting mechanisms can deal with the issues of the schema inconsistency that may result from integration of heterogeneous data sources. The proposed approach provides a way of storing, reusing and composing OLAP cubes in order to increase system usability. A prototype of the proposed framework based on a number of existing tools such as protégé, Jess, and Fuzzy J has been developed to demonstrate its feasibility.

ObjectivesCutaneous leishmaniasis is a vector-borne parasitic disease whose lasting scars can cause stigmatization and depressive symptoms. It is endemic in remote rural areas and its incidence is under-reported, while the effectiveness, as opposed to efficacy, of its treatments is largely unknown. Here we present the data management plan (DMP) of a project which includes mHealth tools to address these knowledge gaps in Colombia. The objectives of the DMP are to specify the tools and procedures for data collection, data transfer, data entry, creation of analysis dataset, monitoring and archiving.ResultsThe DMP includes data from two mobile apps: one implements a clinical prediction rule, and the other is for follow-up and treatment of confirmed cases. A desktop interface integrates these data and facilitates their linkage with other sources which include routine surveillance as well as paper and electronic case report forms. Multiple user and programming interfaces are used, as well as multiple relational and non-relational database engines. This DMP describes the successful integration of heterogeneous data sources and technologies. However the complexity of the project meant that the DMP took longer to develop than expected. We describe lessons learned which could be useful for future mHealth projects.

Large-scale energy infrastructure projects such as oil and gas developments, power generation facilities, and transmission networks are characterized by high hazard intensity, complex socio-technical interactions, and multi-contractor delivery environments. These conditions create persistent challenges for effective occupational safety risk control, particularly when traditional safety management approaches remain reactive, compliance-driven, and reliant on lagging indicators. In response, this paper develops a conceptual model for data-driven occupational safety risk control tailored to the operational and governance realities of large-scale energy projects. The proposed model conceptualizes safety risk as a dynamic and measurable system property that can be proactively managed through the systematic integration of heterogeneous data sources. These include operational process data, workforce and competency records, incident and near-miss reports, environmental conditions, and real-time monitoring technologies. The model is structured around four interrelated layers: (1) risk sensing and data acquisition, (2) analytics and risk interpretation, (3) decision support and governance integration, and (4) risk control and intervention mechanisms. Emphasis is placed on the use of leading indicators, predictive analytics, and feedback loops to enable early identification of emerging hazards and weak signals across project phases. By explicitly linking data-driven insights to decision authority, escalation pathways, and safety governance structures, the model addresses a critical gap between analytical capability and practical risk control. It also highlights the importance of organizational learning, continuous model recalibration, and cross-contractor data integration in complex project ecosystems. The conceptual contribution of this work lies in framing occupational safety management not merely as an operational function, but as an adaptive, intelligence-enabled control system embedded within project governance. The model provides a foundation for future empirical research and offers practical guidance for project owners, EPC contractors, and regulators seeking to enhance safety performance, improve risk predictability, and strengthen resilience in large-scale energy infrastructure delivery.