An automated real-time integration and interoperability framework for bioinformatics

The enormous volumes of data created and maintained by industries, research institutions are on the verge of outgrowing its infrastructure. The advancements in the organization's work flow include data storage, data management, data maintenance, data integration, and data interoperability. Among these levels, data integration and data interoperability can be the two major focus areas for the organizations which tend to implement advancements in their workflow. Overall, data integration and data interoperability influence the organization's performance. The data integration and data interoperability are complex challenges for the organizations deploying big data architectures due to the heterogeneous nature of data used by them. Therefore, it requires a comprehensive approach to negotiate the challenges in integration and interoperability. This paper focuses on the challenges of data integration and data interoperability in big data.

At present, enterprises have been developing more and more quickly in China. In order to strengthen the systems of enterprise management, manufacturing execution, equipment and so on in each link of management, many enterprises have started to use data integration to construct an Informationization management system. Based on the needs of the development of enterprises, this paper analyzes the pattern formation of data integration and its advantages and explores its role in enterprise informationization construction.

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials.

<p class="p1">The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national <em>supply chain interdependent critical infrastructure systems</em> (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection of SCICI data is necessary. The aim of this paper is to review what data are required for model construction, the accessibility of these data, and their integration with each other. While a review of publically available data reveals a dearth of real-time data to assist modeling long-term recovery following an extreme event, a significant amount of static data does exist and these data can be used to model the complex interdependencies needed. For the sake of illustration, a particular SCICI (transportation) is used to highlight the challenges of determining the interdependencies and creating models capable of describing the complexity of an urban environment with the data publically available. Integration of such data as is derived from public domain sources is readily achieved in a geospatial environment, after all geospatial infrastructure data are the most abundant data source and while significant quantities of data can be acquired through public sources, a significant effort is still required to gather, develop, and integrate these data from multiple sources to build a complete model. Therefore, while continued availability of high quality, public information is essential for modeling efforts in academic as well as government communities, a more streamlined approach to a real-time acquisition and integration of these data is essential.

This study aims to analyze the implementation of ERP SAP HR in improving the effectiveness of employee training management in the HR and General Division of DAOP III Cirebon. The study used a qualitative approach with purposive sampling techniques to select eight informants who were directly involved in the planning, implementation, and evaluation of training as well as users of SAP/RAILEO ERP. Data was collected through in-depth interviews, observations, and documentation, then analyzed using NVivo 12 software to identify patterns, themes, subthemes, and linkages between aspects of training management. The results show that SAP ERP supports administrative efficiency, data integration, participant monitoring, and data-driven decision-making. However, the effectiveness of training management is not optimal because there are still manual processes that cause delays in information and gaps between system data and field practice. The Training Center/Training Center has full authority to maintain the security and uniformity of national training, but the limited flexibility for DAOP III hinders responsiveness to local needs. Granting limited access to SAP TEM, reducing manual mechanisms, improving SAP modules, strengthening user capacity, and implementing system integrity dashboards are recommended to improve the effectiveness and efficiency of training management. Follow-up research is recommended to explore the long-term impact of ERP SAP on its competencies, performance, and integration with other HR development programs at PT KAI.

Noninvasive neuromonitoring provides valuable insights into cerebral physiology and function, with the potential to support individualized care for patients who are critically ill. As precision medicine continues to advance within the intensive care unit, the integration of multiple noninvasive monitoring techniques may offer a more comprehensive, real-time understanding of dynamic cerebral pathophysiology while avoiding the risks of invasive procedures. This study aimed to identify the implementation challenges of combining multiple noninvasive neuromonitoring modalities. Six modalities were selected: blind transcranial Doppler ultrasonography, optic nerve sheath diameter measurement, multichannel continuous electroencephalography (cEEG), automated pupillometry, near-infrared spectroscopy (NIRS), and skull extensometer (Brain4Care). To assess the readiness of each modality for integration into a multimodal noninvasive monitoring setup, a group of experts propose the Noninvasive Neuromonitoring Multimodal Readiness (nnMR) score. This scoring system aims to provide a structured evaluation of each modality's multimodal suitability based on criteria such as data continuity and processing, physiological signal representation, spatial and temporal resolution, and software integration in the intensive care unit setting. nnMR scores showed that cEEG and NIRS enable continuous monitoring without manual processing. Blind transcranial Doppler and NIRS offer a good temporal resolution; optic nerve sheath diameter provides good spatial resolution; cEEG and skull extensometer combine both. Only cEEG demonstrates anatomical clarity. Software integration remains limited across all modalities. Key challenges to multimodal implementation include the need for skilled personnel, artifact susceptibility, mismatched temporal and spatial resolutions, and limited long-term signal stability. Integration into unified analytical platforms is further constrained by a lack of standardization, requiring separate manual data input, processing, and quantification. The nnMR score was developed to assess the current readiness of each modality for multimodal, continuous, and long-term clinical application. To validate the proposed nnMR score, engagement with the broader neurocritical care community is essential. Realizing the full potential of multimodal neuromonitoring will require advances in signal validation, software integration, resolution alignment, artifact detection, fixation techniques, and standardization, necessitating close collaboration among researchers, engineers, manufacturers, and clinicians.

Generation and sharing of data have always been critical within the Architecture, Engineering, Construction and Operation (AECO) sector due to its fragmentation and it is well recognized that this led to an inefficient and poor integration of processes along the building lifecycle. Despite the introduction of Building Information Modelling (BIM) that enhances interoperability many issues related to the management of information evolution still persist. Management of vast amount of data is complex especially in change management process during construction phase when a real-time processing, integration and immediate feedback are usually necessary. In order to exploit the potential of digital technologies for process efficiency improvement linking directly to BIM models and defining BIM processes is necessary. Product Life-cycle Management (PLM) and Model-Based Systems Engineering (MBSE), commonly applied to other engineering fields, embody the key approach to complex construction management and model-based information use. The challenge is not only to support the various phases of the building’s life cycle, but also to capture its multidisciplinary nature which has led to the creation of various frameworks. To this aim, “digital twin” with its dynamic digital representation of the physical system continuously updated with latter’s performance, maintenance and health status data can represent a valuable tool. However, current MBSE digital twins face the following challenges: specific domains applications, unrelated modelling theories, different semantic models and uncommon storage representations. All these issues led to seamless integration across disciplines during the whole lifecycle. MBSE toolchain is an emerging technique in the area of systems engineering which is expected to become a next generation approach for supporting complex Cyber-Physical System (CPS) development as the integration system of systems. This paper proposes a framework for development of digital twin integration based on MBSE toolchain technique, providing a semantic integration among layers such as social (integration of stakeholders’ views), process (integration of management models for process control and monitoring), technical (integration of domain specific model for predictive co- simulation), information (integration of data, model and tool operations). The framework has two main purposes: to support toolchain development with a Model Based System Engineering approach; to promote interoperability of the whole developed toolchain through a service-oriented approach where services are provided to the other components by application components, through a communication protocol over a network.

The objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients. This was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing. During the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629). In this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.

The article discusses the use of an intelligent Power Query system for processing data from SAP ERP. When used with SAP ERP data, Power Query can greatly simplify the data analysis process, as well as improve the accuracy and reliability of results. The article provides an overview of the main features of Power Query and how they can be used for data processing in conjunction with the SAP ERP system. It highlights the benefits of using Power Query, including speeding up data processing time, improving data accuracy, and the ability to easily exchange and reuse data processing procedures. Using Power Query to process and analyze bank statements via SAP ERP provides many advantages. Power Query's powerful data processing and transformation capabilities enable organizations to analyze their financial data quickly and accurately, making it easier to identify trends, improve decision-making and stimulate business growth. In addition, the integration of Power Query with SAP ERP provides organizations with a centralized representation of financial data in real time, which allows them to make informed decisions and optimize their activities. Using Power Query to analyze bank statements can also help organizations reduce manual data processing and minimize the risk of errors, increasing the accuracy and reliability of financial information. In general, Power Query offers a powerful solution for organizations seeking to optimize financial analysis processes and ensure business success.

The development of new techniques and the emergence of new high- throughput tools have led to a new information revolution. The amount and the diversity of the information that need to be stored and processed have led to the adoption of data integration systems in order to deal with information extraction from disparate sources. The mediation between traditional databases and ontologies has been recognized as a cornerstone issue in bringing in legacy data with formal semantic meaning. However, our knowledge evolves due to the rapid scientific development, so ontologies and schemata need to change in order to capture and accommodate such an evolution. When ontologies change, these changes should somehow be rendered and used by the pre-existing data integration systems, a problem that most of the integration systems seem to ignore. In this paper, we review existing approaches for ontology/schema evolution and examine their applicability in a state-of-the-art, ontology-based data integration setting. Then, we show that changes in schemata differ significantly from changes in ontologies. This strengthens our position that current state of the art systems are not adequate for ontology-based data integration. So, we give the requirements for an ideal data integration system that will enable and exploit ontology evolution.KeywordsOntology EvolutionData IntegrationMappingsEvolution of Mappings

Because of the strong request for data exchange under information environment, the existing information needs to be integrated and shared. And, the popularity of software-as-aservice (SaaS) caused that users need data integration. Traditional approach of data integration cannot accord with the complex information environment and cannot provide SaaS users with efficient data exchange and application integration. The paper proposes data integration as a service for Applications Deployment on the SaaS Platform that can hide heterogeneous data. It highlights the user's capability of accessing data. Compared with the traditional approach of data integration, it has many characteristics, such as expansibility, load balancing. It makes real-time data exchange and application integration possible. It improves the development of information resources and the integration of work flow.

A labeled-tree approach to semantic and structural data interoperability applied in hydrology domain

The Internet of Things (IoT) has recently received considerable interest due to the development of smart technologies in today’s interconnected world. With the rapid advancement in Internet technologies and the proliferation of IoT sensors, myriad systems and applications generate data of a massive volume, variety and velocity which traditional databases and systems are unable to manage effectively. Many organizations need to deal with these massive datasets that encounter different types of data (e.g., IoT streaming data, static data) in different formats (e.g., structured, semi-structured) coming from multiple sources. Several data integration mechanisms have been designed to process mostly static data. Unfortunately, these techniques are not able to deal with and integrate IoT streaming datasets from multiple sources. In this paper, we identify the challenges of IoT Streaming Data Integration (ISDI) and present a formal approach for the real-time integration of such IoT streaming datasets. We address one of the important issues of timing conflict/alignment among streaming data coming from multiple sources. A generic window-based ISDI approach is proposed to deal with IoT data in different formats and algorithms are developed to integrate IoT streaming data from multiple sources. In particular, we extend the basic windowing algorithm for real-time data integration and to deal with the timing alignment issue. We also introduce a de-duplication algorithm to deal with data redundancy and to demonstrate the useful fragments of the integrated data. We conduct several sets of experiments and quantify the performance of our proposed window-based approach. In particular, we compare our local experimental results with a real setup for streaming data, using Apache Spark. The results of the experiments, which are performed on several IoT datasets, show the efficiency of our proposed solution in terms of processing time. The results are also used to provide an integrated data view to the users.

During the era of open systems, healthcare services and related data are in a constant flux caused by digital transformation. The amount, sources and dimensionality of data grow rapidly, and solutions for data governance, integration and interoperability are urgently needed. At the same time, digital data and information technology–enabled healthcare services are offered as a means to empower citizens. The objective is for active citizens to take better care of their own health. It is possible to support empowerment in many ways, such as with easy-to-use information systems (IS) or personal health records (PHR), or by supporting citizens’ participation in health data creation. In this article, we first present the federative approach to data governance with data federation matrixes in order to show how data are made interoperable by combining data from different data storages. Federation matrixes define shared attributes with their technical, information-flow and socio-contextual metadata. We then contemplate how the federative approach can be deployed to citizens’ healthcare data empowerment. We propose that data ontologies, e.g., data federation matrixes, are useful in bridging gaps between the social contexts of citizens and healthcare professionals and, by doing so, to promote citizen empowerment. The present article contributes to research on the federative approach to data governance, its deployment to citizens’ healthcare empowerment, and to the practice-oriented further development of the federation matrix tools for this and other use cases.