Leveraging Semantic Technologies in ETL Processes for Data Integration in Heterogeneous Environments

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.

Citizens have always played an important role in emergency management such as urban response. New information and communication technologies such as smart phones and computer-based social networks have great potential to transform the roles of citizens in emergency management. However, current digital citizen science projects are usually limited in three areas: 1) limited one-way citizen participation, 2) no processing and integration of citizens' reports with other existing infrastructure sensing data, 3) no personalized near-real-time spatiotemporal visualization tools for citizens to instantly view aggregated data to gain updated situational awareness. We developed a Mapster application that specifically addresses these issues. First, we leveraged Twitter's geo-referenced tweets functionality to design a customized smart phone application for citizens to report a set of events that have been identified in past urban situations such as basement flooding and powerline down etc. Second, a Cloud-based semantic streaming data harvesting and processing tool was developed to fetch and process both the Twitter feeds and other infrastructure sensing data such as US National Weather Service's radar data. Third, a user can instantly explore the heterogeneous data processed and provided by the Cloud service through a map-based spatiotemporal animation tool on the smart phone to see how all the events evolve before, during, and after a storm. Such a two-way information flow significantly improves citizen participation and their sense of situational awareness. We present our architecture, implementation, and discussion of issues on citizen science data collection platforms, integration of heterogeneous data sources and future work plan.

The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control.

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.

The Internet of things is foreseen as one of the next imminent Internet revolutions, as many devices will seamlessly communicate together to provide new and exciting services to the end users. One of the challenges that the IoT has to face is about both the heterogeneity of the data available and the heterogeneity of the communication. In this paper we focus on the former, by presenting an architecture able to integrate data coming from different sources, including custom made deployments and government data. New services can be deployed directly by the end users, using reliable or unreliable data sources, and new processed data can be gathered by these services and used by others.

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.

In this paper, we showcase the implementation of a semantic information model and a virtual knowledge graph at ZF Friedrichshafen AG company, with two main goals in mind: 1) integration of heterogeneous data sources following a pay-as-you-go approach; and the 2) combination core domain concepts from ZF’s production line with meta-data of its internal data sources. We employ the developed semantic information model in two use cases, defect traceability and customer service, demonstrating and discussing the benefits and opportunities provided by following an agile semantic virtual integration approach.

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.

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.

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

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