Evaluation of a European Data Analytic Framework

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.

BackgroundProtein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge.ResultsIn this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network and multiple heterogeneous data sources. Tested by the yeast data with six heterogeneous feature kernels, the results show our method can further improve the prediction performance by up to 2%, which is very close to an upper bound that is obtained by an Approximate Bayesian Computation based sampling method.ConclusionsThe proposed evolution deep neural network, coupled with regularized Laplacian kernel, is an effective tool in completing sparse and disconnected PPI networks and in facilitating integration of heterogeneous data sources.

Data integration of heterogeneous sources is still an active research problem. This topic is especially crucial on enterprises that deal with new and different kinds of data as new services are provided. However, even though research efforts have been applied to the information integration, there is a lack of practical approaches ready to be implemented for real use cases. In this paper, we present a web-based framework and system built upon ontologies and other semantic techniques for the integration of heterogeneous data sources. The example scenario used to demonstrate the utility of the framework consists of a use case with the actual data of a telecom company.

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

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.

The integration of heterogeneous data sources can be done by using data-driven microservices along with Semantic Web technologies. However, it becomes a challenge in cross-domain scenarios, in which data is described by heterogeneous ontologies. This work discusses data integration problems in the context of microservices and proposes an ontology alignment framework aimed at achieving semantic data-driven microservices composition for data integration in multi-ontology scenarios. The proposed framework is evaluated through an experimental design to obtain a suitable statistical analysis of the effects that may be considered for modeling data-driven microservices.