A Polyglot Data Integration Framework for Seamless Integration of Heterogeneous Data Sources and Formats

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.

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.

Data governance in a lakehouse architecture is crucial for managing the lifecycle, quality, and security of data across hybrid and scalable environments. The lakehouse model combines the benefits of data lakes and data warehouses, enabling organizations to handle large volumes of structured and unstructured data while ensuring analytical accuracy. However, the seamless integration of diverse data sources introduces challenges related to governance, including compliance, privacy, and data lineage. This paper explores the principles and practices essential for implementing effective data governance in a lakehouse environment. Key components such as metadata management, data cataloging, access control, and policy enforcement are discussed in detail. The role of automation and machine learning in enhancing governance capabilities, such as detecting anomalies and ensuring compliance with regulatory standards, is also examined. Additionally, the lakehouse architecture's unified nature demands a governance framework that ensures data consistency while fostering accessibility for both data scientists and business analysts. Strategies for integrating data governance within the lakehouse model, including role-based access, audit trails, and encryption, are presented to mitigate risks and enhance trust in data usage. By addressing these challenges and opportunities, this paper demonstrates how a robust data governance framework can empower organizations to balance innovation and control. Ultimately, data governance in the lakehouse architecture serves as a foundation for reliable, secure, and actionable insights, driving informed decision-making in complex and dynamic environments.

In the last two decades, unstructured information has become a major challenge in information management. Such challenge is caused by the massive and increasing amount of information resulting from the conversion of almost all daily tasks into digital format. Tools and applications are necessary in organizing unstructured information, which can be found in structured data, such as in relational database management systems (RDBMS). RDBMS has robust and powerful structures for managing, organizing, and retrieving data. However, structured data still contains unstructured information. In this paper, the methods used for managing unstructured data in RDBMS are investigated. In addition, an incremental and dynamic repository for managing unstructured data in relational databases are introduced. The proposed technique organizes unstructured information through linkages among textual data based on semantics. Furthermore, it provides users with a good picture of the unstructured information. The proposed technique can rapidly and easily obtain useful data, and thus, it can be applied in numerous domains, particularly those who deal with textual data, such as news articles.KeywordsRelational databasesUnstructured dataDocument clusteringQuery efficiencyTextual data

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

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.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

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.

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study.

The success of the Semantic Web strongly depends on the proliferation of ontologies, which requires fast and easy engineering of ontologies. The paper analyzes the semantic similarity between relational model and ontology, and proposes a semi-automatic ontology acquisition method(SOAM) based on data in relational database. SOAM tries to ensure the quality of constructed ontology and the automatic degree of acquiring process by balancing the cooperation between user contributions and machine learning. Because OWL is the latest ontology language standard recommended by W3C, the implementation of SOAM is given to acquire OWL ontology automatically as much as possible. Different from existing methods, the implementation method not only can acquire OWL ontology from relational database directly without demanding a middle model, but also can refine obtained ontology according to existing lexical knowledge repositories semi-automatically.

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 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.