Design and implementation of non-intrusive logistics system architecture for data silo integration

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.

Web service-based application development has become a vital component of the modern in-formation technology ecosystem. The implementation of web services facilitates interoperability among diverse systems and platforms. REST API (REpresentational State Transfer Application Programming Interface) is one of the most popular architectures for creating efficient and scalable web services. This study examines the implementation of REST API in the development of web service-based applications. The case study focuses on the creation of a Web service-based Research Proposal Selection System. The REST API implementation method in this study adheres to the system development life cycle (SDLC) to ensure that the developed API meets business needs while being efficient, secure, and reliable. The analysis phase is conducted to gather both functional and non-functional REST API requirements. During the design phase, the REST API architecture is designed in detail, including how data will be transferred and processed, and how the API will interact with databases and other systems. Furthermore, in the implementation phase, the REST API is developed, ensuring that each endpoint operates correctly. In developing the Proposal Selection System, the implementation of web services integrates data from various existing sys-tems, including employee, academic, and research data. The system encompasses various features, such as proposal import, proposal mapping, proposal review, proposal assessment, proposal se-lection, and reporting. The research findings indicate that implementing the REST API simplifies the development of web service applications. The test results reveal that the average response time is relatively stable, measuring 31 ms without iterations and 29 ms with five iterations.

The rapidly increasing complexity of integrated circuit design can only be addressed effectively by a design system architecture that supports very efficient, high-quality sharing of IC design data. Integration of the wide variety of tools required to design and verify the performance and quality of ICs can no longer depend on low-bandwidth file formats. Rather, an application programming interface (API) to a standard information model (IM) that provides access to a shared database is an essential infrastructure component of today's IC design systems. Furthermore, this design data API should be standard across the IC CAD industry so that design systems can efficiently draw from best-of-class applications from all sources in the EDA industry. The tight coupling of all tools through a common IM/API enables rapid design convergence with an earlier emphasis on quality issues, which results in higher quality ICs while meeting aggressive time-to-market demands.

The integration of Internet of Things (IoT) technologies in healthcare has enabled the development of smart hospitals. However, fragmented infrastructures and inefficiencies but lacks concrete examples (e.g., legacy systems causing data silos). Existing architectures fail to unify Clinical Information Systems (CIS) with IoT workflows, creating inefficiencies in critical care. This paper proposes a unified IoT architectural model for a smart hospital centered on the Clinical Information System (CIS) to address these challenges. The architecture comprises five horizontal layers (perception, network, IoT gateway, knowledge, and application) and three vertical aspects (security, management, and cloud platforms). The Knowledge Layer integrates AI and big data analytics for predictive diagnostics and intelligent decision-making. The IoT Gateway Layer uses edge computing to reduce data latency compared to cloud-only systems. Security Framework is aligned with GDPR and EU health data policies (e.g., EHDS), ensuring secure interoperability. The architecture is evaluated using stakeholder-prioritized ATAM scenarios. It demonstrates improved interoperability, reduced unauthorized access, enhanced patient care, and lower costs. Future work focuses on real-world pilots and integration with emerging technologies to validate scalability and adaptability in diverse healthcare environments.

In the changing digital environment, universities face the necessity of integrating heterogeneous information systems. This study focuses on a set-theoretic approach to modeling the integration of these systems. It considers the construction of a model that will effectively facilitate interaction among various information resources within the university, belonging to different platforms and formats. Special attention is paid to optimizing data exchange processes and ensuring information integrity within the common digital space of the university. This work proposes a formal integration model that allows minimizing architectural coupling and ensuring the evolutionary development of the university's digital platform. The set-theoretic apparatus is used to formalize the IT landscape. A decomposition method based on the concept of "architectural quanta" (Architectural Quantum) is applied. Metrics of structural autonomy are proposed to assess effectiveness. A formal model describing the university's information system as a collection of loosely coupled quanta is introduced. An architectural integrity condition is established, prohibiting integration through shared databases. A structural autonomy coefficient (K_sa) is developed to quantitatively assess architectural quality. The model serves as a theoretical basis for implementing automated architecture control systems based on specialized metrics (Fitness Functions) in CI/CD pipelines, which helps reduce technical debt and accelerate the launch of new educational services (Time-to-Market). The main research method employed is a set-theoretic approach to modeling the integration of heterogeneous information systems in the context of digitalization. The scientific novelty lies in applying the set-theoretic apparatus to describe and model the processes of data integration from various sources with differing structures and formats. The developed model provides a formalized representation of the integration relationships between information systems, allowing for the optimization of data exchange processes and enhancing the efficiency of the university's information resources. The research results indicate the high effectiveness of the proposed approach for creating a unified information space within the university, contributing to improved quality of education, scientific research, and managerial processes. Practical implementation of the model will enhance the university’s competitiveness in the digital economy. The feasibility of building a flexible and scalable system capable of adapting to new requirements and changes in the university's information environment is proven.

Intense competition drives the need for dynamic and flexible integration of dispersed and heterogeneous information systems. This paper proposes a semantic gateway architecture for system integration in networked manufacturing to solve the semantic heterogeneity problems. The system integration mode based on semantic gateway is set up to enable unified expression and analysis of the information to be integrated, so that the flexibility and transparency of integration between heterogeneous systems are significantly improved.

Aiming at the complex decision-making problems in intelligent manufacturing, a set of intelligent decision-making optimization systems is constructed. Based on the microservice architecture, the system function modules are designed, and the decision-making model is built using deep learning algorithms, realizing intelligent decision-making for equipment scheduling, capacity planning, and quality control. The system verification results show that the decision-making accuracy rate reaches 93.8 %, the average response time is 78 ms, and it supports 1000 concurrent users. In the actual application of 6 months, the system completes 1 million decision-making tasks, processing 2.8TB of data, significantly improving the intelligence level of the manufacturing process.

In this paper, a methodology for integrating heterogeneous industrial information systems is presented. The methodology is strongly based on the extensive reuse of already-made components and is conceptually divided in three levels, one for each kind of designer that is typically involved in this type of projects. To accomplish a better integration of the activities and tools necessary to develop industrial information systems with the proposed methodology, three appropriate organizational configurations are adopted.

Hematology Patient Explorer - an Informatics Infrastructure and Web Application Empowering Clinicians and Researchers to Use Electronic Health Record Information to Identify Clinical Cohorts of Interest

As enterprises undergo rapid digital transformation, the volume, velocity, and variety of data have grown exponentially, leading to unprecedented challenges in data management, security, and compliance. Traditional, manual data governance frameworks are no longer sufficient to handle the scale and complexity of modern data ecosystems, often resulting in data silos, poor data quality, and regulatory non-compliance. This paper proposes a comprehensive AI-Driven Unified Data Governance Framework designed for modern enterprise platforms. By integrating artificial intelligence (AI) and machine learning (ML) at the core of the data governance lifecycle, the proposed framework automates critical functions such as data discovery, classification, quality assurance, lineage tracking, and policy enforcement. We present a layered architectural model that seamlessly operates across hybrid and multi-cloud environments, supporting paradigms like data mesh and data fabric. Through four detailed enterprise case studies spanning financial services, healthcare, global retail, and smart manufacturing, we demonstrate the empirical benefits of AI-driven governance, including up to 72% reduction in compliance reporting time and significant improvements in data quality and operational efficiency. Furthermore, we provide a comparative analysis of leading enterprise governance platforms and introduce a six-level AI Governance Maturity Model. Finally, the paper explores future trends, including the integration of Large Language Models (LLMs), autonomous self-healing data pipelines, and federated learning, providing a strategic technology roadmap for the next decade of enterprise data governance.

The increasing complexity of fraud in large enterprises, particularly in financial and transactional ecosystems, necessitates a scalable, agile, and distributed detection approach. Traditional centralized fraud detection architectures struggle to keep pace with the real-time requirements, data silos, and domain-specific fraud patterns that arise across organizational units. This paper explores the application of Data Mesh architecture as a decentralized and domain-oriented paradigm for enhancing fraud detection in large enterprises. Data Mesh shifts the ownership of data from centralized teams to domain-specific teams, treating data as a product and enabling better scalability, autonomy, and responsiveness. We propose a Data Mesh-based fraud detection model wherein each business domain—such as sales, finance, customer relations, and operations—operates as a semi-autonomous node capable of detecting fraud patterns locally while contributing to an enterprise-wide fraud intelligence network. Leveraging a federated governance model, the architecture facilitates standardized yet decentralized policy enforcement, model deployment, and cross-domain collaboration. This paper examines how data product thinking, domain-driven design, self-serve data platforms, and federated computational governance work together to create a resilient and adaptable architecture for fraud detection. The proposed methodology utilizes distributed anomaly detection algorithms, local event-driven stream processing (e.g., Apache Kafka and Flink), and inter-domain feedback loops for continuous model retraining and behavior correlation. Experimental simulations conducted on synthetic multi-domain enterprise data reveal improved time-to-detection, reduced false positives, and enhanced fraud detection in low-signal data scenarios compared to centralized models. Furthermore, the architecture demonstrates superior scalability and flexibility when integrating new domains and updating detection logic. The findings of this research indicate that Data Mesh not only democratizes access to fraud-related data but also enhances detection capabilities by aligning technical solutions with organizational complexity. This paper contributes to the growing body of decentralized AI applications in enterprises and offers actionable design patterns for implementing domain-centric fraud analytics in large organizations. Future work includes extending this architecture to incorporate privacy-preserving technologies such as federated learning and exploring its applicability in regulatory compliance frameworks

The picture archiving and communication system (PACS) originated as an image management system for improving the efficiency of radiologic practices. It has evolved into a hospital-integrated system that stores information media in many forms, including voice, text, medical records, waveform images, and video recordings. The integration of these various types of information requires the technology of multimedia, including hardware platforms, information systems and databases, communication protocols, display technology, and system interfacing and integration. The purpose of this book is to discuss the conceptual and technologic advances in PACS development for all of these previously mentioned technologies. The book also emphasizes the growing field of PACS-based imaging informatics, where existing PACS resources are used for largescale horizontal and longitudinal clinical service, research, and education applications. The book is intended for those who are involved in a health care environment that manages the everyday practical realities of PACS planning, operation, and maintenance. The book will also serve as excellent curriculum material for the training of PACS information technology personnel because the author provides comprehensive concepts that tie medical imaging, medical physics, information technology, and clinical workflow with enterprise PACS (multiple, hospital-wide PACS). The author, H. K. Huang, has been involved in PACS development since the 1980s and is a leading scientist in the field. The book is divided into five parts and 23 chapters. Part 1, titled “Medical Imaging Principles,” summarizes the history of PACS, the fundamental concepts of digital medical imaging, and the different types of modalities integrated into PACS. Part 2, titled “PACS Fundamentals,” describes in detail the PACS subsystems and industry standards (Digital Imaging and Communications in Medicine and Health Level 7) that are used to integrate PACS with the hospital information system and radiology information system. Part 3, titled “PACS Operation,” describes the practical problems encountered by PACS administrators and users and discusses different techniques to solve these problems, with many examples from installed PACS sites around the world. Because the author developed PACS at the University of California, Los Angeles, in 1991 and the University of California, San Francisco, in 1995, he is able to draw from his expertise on the subject. The author also uses his experience from developing many state-of-theart prototypes (eg, fault tolerant storage system) funded by research grants. Part 4, titled “PACS-based Imaging Informatics,” presents image-processing techniques that can be used to mine the huge image and clinical data resources stored in the PACS archive. The analysis of lung nodules by using temporal computed tomographic and contentbased image indexing are some of the examples of advanced processing applications applied to PACS data mining. Part 5, titled “Enterprise PACS,” describes different models for implementing an enterprise PACS in terms of the architectural design of the system and financial and business models. This new book builds on two previous volumes on PACS that were written by the author. The text, image collections, and schematic diagrams in this current work achieve the author’s goal of providing up-to-date material concerning the technologic advances in PACS and imaging informatics. The text serves as a guide for those who are planning an enterprise PACS and provides teaching material for training PACS developers in industry and health care. The author has used portions of this book as lecture material for graduate courses at leading universities in the United States and abroad. The author also uses many examples to emphasize that the integration of heterogeneous health care information systems is becoming more complex and that, as more radiologists and clinicians expect higher standards in the reliability of the data, a systemic understanding of Digital Imaging and Communications in Medicine, Health Level 7, and Integrating the Healthcare Enterprise becomes ever more important to optimize enterprise workflow. The strength of the book lies in the vast experience that the author, who has implemented PACS at numerous institutions in the United States and abroad, has transmitted into this new book. Huang has conducted first-hand analysis of the various systems from PACS manufacturers that are used in clinical operation at large medical institutions and has performed up-to-date research in data mining of the huge image and data resources stored in the PACS repository.

Sequential configuration is a fundamental pattern that occurs when integrating systems that span domains and levels of abstraction. This task requires the integration of heterogeneous autonomous information systems, processes, and applications. We propose an extensible system for correlating sequential configurations across loosely-coupled systems. Our framework defines fundamental abstractions and interfaces that enable the implementation of domain-specific models. We build upon our framework to provide a suite of tools to support applications that manage the configuration lifecycle. Our implemented prototype application integrates the processes, tools, and data involved in the first two stages of the IT outsourced services contract lifecycle.

Each person interacts with multiple health institution’s systems along their life. These systems are usually developed to fulfill the specific needs of sanitary organizations or Web of Medical Things manufacturers. However, most of the times these information systems aren’t interconnected, making it very difficult to put in common the information of a patient scattered in various systems. Thus, it’s necessary to develop solutions that allow health information systems developers to consult all the information of an user across the multiple health’s information systems it is scattered and to offer this information organized as the Personal Health Trajectory of the user (a succession of Personal Health Records ordered by time). This paper proposes a solution for the integration of heterogeneous health information systems, the processing of their data and its provisioning in a health trajectory perspective through an API. Thus, software developers of healthcare solutions can leverage this API to develop a new generation of person-oriented solutions.

In this paper we sketch an approach to the integration of heterogeneous tourist information systems into database federations. Since many tourist information systems offer WWW-interfaces based on HTML-files and furthermore, many local information systems are based on specific file formats, we have to consider such file associations. For the integration of these file associations, the main problem is the lack of meta-information. Therefore, we have to derive an appropriate local conceptual schema. The derived conceptual schemata and the schemata of other tourist database systems will be integrated into a federated schema. Moreover, we briefly discuss the benefits of a federated tourist information system.