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This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture's advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture's capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL. [ABSTRACT FROM AUTHOR]

Implementing smart microgrids for Non-Interconnected Zones (NIZs) has become an alternative solution to provide electrical energy by taking advantage of the resources available through the generation of renewable energy within these isolated areas. Within this context, in this study, the challenges related to microgrids and data analysis are presented, and different relevant data architectures described in the literature are compared. This paper focuses on the design of a data architecture for a smart microgrid for NIZs whose microgrid contains two 260 W solar panels, a 480 W inverter, and two 260 Ah batteries. Regarding the Colombian context, this paper describes the limitations (connectivity, isolation, appropriation of technologies) and opportunities (low demand, access to natural resources, state interest) from which the functional and non-functional requirements for the architecture are established. Finally, a data architecture is proposed and implemented in a NIZ in Colombia, and this paper also includes a description of the architecture, its characteristics, its associated opportunities and challenges, and discussions regarding its implementation. [ABSTRACT FROM AUTHOR]

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today's growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in "coopetition". This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning. [ABSTRACT FROM AUTHOR]

With the wide acceptance of open CNC systems and the rapid development of network technology, end-users can easily integrate control strategies or intelligent algorithm modules into open CNC systems. The openness performance, real-time performance, and expandability of the open CNC system are facing great challenges due to the increase of algorithm modules and control modules which will cause high system loads. Although updating hardware can ease loads in the system, the cost and the maintenance cycle also significantly increase. Instead, taking the cost of time and resource consumption into account, reasonable and highly adaptable data architecture is an effective method to optimize the real-time performance, data accuracy, and response time under high system loads. Hence, this paper firstly presents a hierarchical data architecture of the SERCOS (Serial Real-Time Communication Specification)-based open CNC system, which is composed of a perceptual layer, a decision layer, and an executive layer. Then, a data filter is developed to divide the processes accessing the critical section into different groups to reduce memory fragments and the length of the reader waiting queue. What is more, an alternate synchronization algorithm based on the writer priority synchronization model is presented to avoid data packet loss and improve real-time performance. A verification experiment in full-featured open CNC system is conducted to valid the feasibility of the proposed optimized data structure. Besides, a comparison experiment in three open CNC systems, the proposed open CNC system with optimized data interaction structure, the embedded CNC system, and the typical open CNC system, is also carried out to verify the response time, memory fragments, and data accuracy under high system loads. The case study validates that the proposed open CNC system with optimized data interaction structure obtains the best result in the response time and memory fragments. Furthermore, the accuracy of the data and the execution efficiency of the algorithm modules are also guaranteed. [ABSTRACT FROM AUTHOR]

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This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture’s advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture’s capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.

Abstract The panorama of data is ever evolving, and big data has emerged to become one of the most hyped terms in the industry. Today, users are the perpetual producers of data that if gleaned and crunched, have the potential to reveal game-changing patterns. This has introduced an important shift regarding the role of data in organizations and many strive to harness to power of this new material. Howbeit, institutionalizing data is not an easy task and requires the absorption of a great deal of complexity. According to the literature, it is estimated that only 13% of organizations succeeded in delivering on their data strategy. Among the root challenges, big data system development and data architecture are prominent. To this end, this study aims to facilitate data architecture and big data system development by applying well-established patterns of microservices architecture to big data systems. This objective is achieved by two systematic literature reviews, and infusion of results through thematic synthesis. The result of this work is a series of theories that explicates how microservices patterns could be useful for big data systems. These theories are then validated through expert opinion gathering with 7 experts from the industry. The findings emerged from this study indicates that big data architectures can benefit from many principles and patterns of microservices architecture.

The construction of systems supporting spatial data has experienced great enthusiasm in the past, due to the richness of this type of data and their semantics, which can be used in the decision-making process in various fields. Thus, the problem of integrating spatial data into existing databases and information systems has been addressed by creating spatial extensions to relational tables or by creating spatial data warehouses, while arranging data structures and query languages by making them more spatially-aware. With the advent of Big Data, these conventional storage and spatial representation structures are becoming increasingly outdated, and required a new organization of spatial data. Approaches based on distributed storage and data lakes have been proposed, to integrate the complexity of spatial data, with operational and analytical systems which unfortunately quickly showed their limits. Recently the concept of lakehouse was introduced in order to integrate, among other things, the notion of reliability and ACID properties to the volume of data to be managed. This new data architecture is a combination of governed and reliable Data Warehouses and flexible, scalable and cost-effective Data Lakes. In this paper, we present how traditional approaches of spatial data management in the context of spatial big data have quickly shown their limits. We present a literature overview of these approaches, and how they led to the Data LakeHouse. We detail how the Lakehouse paradigm can be used and extended for managing spatial big data, by giving the different components and best practices for building a spatial data LakeHouse architecture optimized for the storage and computing over spatial big data. • Limitations of Data Warehouses and Data Lakes for Spatial Big Data. • Characteristics and Architecture of the Data LakeHouse. • Overview of Three Major Open Source LakeHouse Systems. • Challenges in Using Data LakeHouse for Spatial Big Data. • Optimized Data LakeHouse Architecture for Spatial Big Data. [ABSTRACT FROM AUTHOR]

The rapid and dynamic development of information technology in today's digital era has a significant impact on organizations in managing information and data. This also applies to universities that need to optimize data and information management in order to improve service quality and organizational performance. Therefore, it is necessary to design an appropriate and integrated strategic information system, which is able to support the decisionmaking process effectively and efficiently, as well as integrate various functions and processes in various organizational units on campus. Enterprise is a set of organizations that have several general goals / principles and / or a baseline. Higher education is one of the enterprises engaged in education. The increasing need for data and information in business functions carried out by a university is one of the drivers of the use of information systems in universities. Therefore, in this study will be designed on 3 (three) architectural models, namely data architecture, architecture, applications and technology architectures using the Enterprise Architecture Planning (EAP) method. [ABSTRACT FROM AUTHOR]

The panorama of data is ever evolving, and big data has emerged to become one of the most hyped terms in the industry. Today, users are the perpetual producers of data that if gleaned and crunched, have the potential to reveal game-changing patterns. This has introduced an important shift regarding the role of data in organizations and many strive to harness to power of this new material. Howbeit, institutionalizing data is not an easy task and requires the absorption of a great deal of complexity. According to the literature, it is estimated that only 13% of organizations succeeded in delivering on their data strategy. Among the root challenges, big data system development and data architecture are prominent. To this end, this study aims to facilitate data architecture and big data system development by applying well-established patterns of microservices architecture to big data systems. This objective is achieved by two systematic literature reviews, and infusion of results through thematic synthesis. The result of this work is a series of theories that explicates how microservices patterns could be useful for big data systems. These theories are then validated through expert opinion gathering with 7 experts from the industry. The findings emerged from this study indicates that big data architectures can benefit from many principles and patterns of microservices architecture. [ABSTRACT FROM AUTHOR]

Implementing smart microgrids for Non-Interconnected Zones (NIZs) has become an alternative solution to provide electrical energy by taking advantage of the resources available through the generation of renewable energy within these isolated areas. Within this context, in this study, the challenges related to microgrids and data analysis are presented, and different relevant data architectures described in the literature are compared. This paper focuses on the design of a data architecture for a smart microgrid for NIZs whose microgrid contains two 260 W solar panels, a 480 W inverter, and two 260 Ah batteries. Regarding the Colombian context, this paper describes the limitations (connectivity, isolation, appropriation of technologies) and opportunities (low demand, access to natural resources, state interest) from which the functional and non-functional requirements for the architecture are established. Finally, a data architecture is proposed and implemented in a NIZ in Colombia, and this paper also includes a description of the architecture, its characteristics, its associated opportunities and challenges, and discussions regarding its implementation.

Organizations are facing increasing challenges in effectively managing and utilizing data as a strategic asset within the rapidly evolving data landscape. Traditional monolithic data architectures are struggling to keep pace with the heterogeneous nature and high velocity of generated data, making it necessary to explore alternative paradigms. This article investigates the potential of data virtualization as a data integration pattern, discussing its core capabilities and features, as well as its role in enabling distributed data architectures such as data fabric and data mesh. Furthermore, the article provides a comparison of common data integration patterns and highlights the advantages of data virtualization especially in the distributed data architecture. [ABSTRACT FROM AUTHOR]

Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs). Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating digital twins. Moreover, societal trends such as worldwide urbanization and growing emphasis on sustainability highlight competing goals that must be reflected not just in cooperating but also competing digital twins, often even interacting in “coopetition”. This paper argues for multi-agent systems (MASs) to address this challenge, using the example of embedding industrial digital twins into an urban planning context. We provide a technical discussion of suitable MAS frameworks and interaction protocols; data architecture options for efficient data supply from heterogeneous sensor streams and sovereignty in data sharing; and strategic analysis for scoping a digital twin systems design among domain experts and decision makers. To illustrate the way still in front of research and practice, the paper reviews some success stories of MASs in Industrie/Logistics 4.0 settings and sketches a comprehensive vision for digital twin-based holistic urban planning.

The current ad-hoc, organically grown system of digital library inventory management is inefficient, ineffective, and costly, both in dollar terms and in the time and effort of librarians and users. UConn is building a data architecture framework that is integrated in a larger concept of how library data is acquired, managed, and delivered. Collection development policy, data architecture, and information architecture combine to create a strategic framework for collections-based library activities. [ABSTRACT FROM AUTHOR]

Digital transformation requires rapid and profound changes to take advantage of technologies and data in order to make decision-making more effective with agility and self-sustainability. The complexity of data in the modern era and the silos that are generated at big scale drive the emergence of new data management models and architectures that focus on the intrinsic characteristics of digital ecosystems, characterized by the strong interrelationships of various actors through along the value chain, the platforms as the basis for interoperating with each other and the co-evolution of data products that emanate from increasingly heterogeneous sources. This article proposes the design of a reference architecture for data ecosystems based on the data architectures that are best supporting data management in this complex scenario: Data Mesh and Data Fabric, and with the use of knowledge graphs for the integration. As a method, an analysis of the most recent literature on data management and architectures is used to extract the principles and architectural components that are used in the design of such a reference architecture. An abstract representation of the reference architecture of data ecosystems is obtained, whose operational model is theoretically verified. It is the starting point for future research that will be directed towards its implementation in real use cases and organizational modeling related to the roles of the actors involved in the ecosystem reflected in the architecture itself.

As the traditional grid data architecture develops with enterprises, its security risks will become greater and greater, and it will not be able to satisfy customers' differentiated services. Predecessors' research on the application of blockchains and nanomaterials is still insufficient. To solve the various shortcomings of the traditional power grid data governance architecture, a new type of sensor designed using blockchain technology and nanomaterials is designed through new technical means. It is applicable to the current digital grid data management architecture. The study found that the scale of the system gradually increased from 4 nodes to 16 nodes, and the TPS entered the smoothing stage after about 10, 50, 70, and 100 s, and stabilized at a small fluctuation point. We found that for every 4 nodes increase in the cluster size, the throughput drops by about 8%. Clusters of different sizes have entered a state of stable throughput. Although TPS has shown an overall downward trend of 8%, the peak situation of TPS has shown a certain different trend. This shows that the digital grid data governance network framework designed through blockchain technology and nanomaterial technology can meet the various needs of modern digital grid data centers for data center networks, such as: business scalability, reliability, security, etc. [ABSTRACT FROM AUTHOR]

Naval leaders are increasingly demanding resilient maritime systems capable of effectively anticipating, responding to, recovering from, and adapting to a given environmental disruption. For engineers designing these systems, real‐world data, information, and knowledge must be leveraged to operationalize the concept of resilience, particularly during the verification process of the system life cycle. However, such data are commonly unstructured, poorly organized, and lacking in context. Resilience‐defining data may not even exist, necessitating a methodological approach to ensure that relevant data are actually collected during system operation. This paper presents a feasible conceptual data model to improve the process of characterizing a given system's resilient performance. The authors consider the prevailing literature from the systems engineering, resilience engineering, and data management communities, and a scenario involving a hurricane‐impacted ocean monitoring system is presented as a case study of the conceptual data model's utility. Ultimately, by using a resilience‐centric data modeling approach, systems engineers can better derive prescribed resilience measures and metrics for a given system or system‐of‐systems. [ABSTRACT FROM AUTHOR]

Introduction: Management and control of reportable diseases are challenging because these diseases include a large spectrum of infectious conditions that need accurate, precise, and timely reporting. To deal with this problem, an integrated surveillance system using the set of core data architecture principles is crucial to ensure the effective management of data. Therefore, this study aimed to identify the core data architecture requirements for effective management of Coronavirus Disease 2019 (COVID-19), followed by designing a data architecture model. Materials & Methods: This systematic review was conducted in 2020 through searching five databases, including PubMed, Web of Science, Science Direct, and Scopus M, as well as Google scholar search engine to identify metrics for COVID-19 data architecture designing. Moreover, the search formula definition, implication of inclusion and exclusion criteria, search filtering adjustment, and related study identification were performed in this study. Subsequently, the cases identifying the architecture data of the COVID-19 component were systematically extracted and categorized in suitable classes. Finally, the management system of the architecture model of the patient was visualized in this study. Ethics code: IR.ABADANUMS.REC.1399.065 Findings: Out of 398 identified studies, 27 articles met the inclusion criteria. The obtained data were categorized into five classes, including organizations involved in data management (data producer, data users, and decision-makers), data sources, information requirements (11 information classes and 77 data elements), standards (semantic and syntactic), and control quality criteria of the data. Discussions & Conclusions: Implementation of customized data architecture for COVID-19 can increase the potential of the health care systems to prevent the high prevalence of this disease and improve the quality of care through timely and effective health monitoring, accurate epidemiological investigations, clinical decision supports, and health-care policymaking.

The increasing threat of emerging and re-emerging pathogens calls for a shared vision toward developing and maintaining global surveillance mechanisms to enable rapid characterization of pathogens, a foundational requirement for effective outbreak response. Efforts establishing new surveillance programs in low- and middle-income countries (LMICs) have repeatedly led to siloed systems that prove unsustainable or ineffective due to narrowly focused approaches, competing priorities, or lack of resourcing. Barriers inherent to LMICs, such as resource limitations, workforce strain, unreliable supply chains, and lack of enduring champions exacerbate implementation and sustainability challenges. In order to improve adoption and endurance of new surveillance programs, more effective design and implementation of programs is needed to adequately reflect stakeholder needs and simultaneously support population-level disease monitoring and clinical decision-making across a range of chronic and acute health issues. At the heart of this cross-sectorial integration between clinical care and public health initiatives are emerging technologies and data modalities, including sequencing data. In this prospective, we propose an implementation strategy for genomics-based surveillance initiatives in LMICs founded on the use of a target operating model. Adoption of a target operating model for the design and implementation of genomic surveillance programs will ensure programs are agile, relevant, and unified across diverse stakeholder communities, thereby increasing their overall impact and sustainability.

In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, the interoperability and flexibility of different components is still an important challenge so as to integrate them in the process and fit all industrial specific needs. Thus, the main contribution of this paper is to propose a database architecture and a data model associated allowing multiple agents to work collaboratively and synchronously to perform high-level tasks. Therefore, it fulfils requirements and needs of Industry 4.0: interoperability, scalability, flexibility and resilience. The proposed architecture and model are implemented on a cyber-physical production system (CPPS) which is used in order to show and discuss several use cases examples. [ABSTRACT FROM AUTHOR]

Abstract Knowledge sharing between different types of organisations, in this case the University-Industry (U-I) relation, contains barriers related to the differentiation of structure and purpose for each organisation. Both the university and industry have to make sure that they have access to data and information. This accessibility enables the value-added process of transforming data into knowledge. The purpose of this paper is the analysis of U-I cooperation– especially under the new online requirements – an illustration of how the knowledge is created and shared in the organisation based on the socialisation, externalisation, combination, and internalisation (SECI) model and how this is aligned to the value proposition. As a knowledge management example case, a database for the Centre for Energy Technologies of Aarhus University was developed that will overtime help the university to increase its value proposition and be the heart of knowledge exchange with the industry in an organised way. The value of this work is intended to pave the way in building structured working relations between the industry and academia, facilitating the means in order to achieve a higher degree of overall efficiency.

openEHR is an open-source technology for e-health, aims to build data models for interoperable Electronic Health Records (EHRs) and to enhance semantic interoperability. openEHR architecture consists of different building blocks, among them is the "template" which consists of different archetypes and aims to collect the data for a specific use-case. In this paper, we created a generic data model for a virtual pancreatic cancer patient, using the openEHR approach and tools, to be used for testing and virtual environments. The data elements for this template were derived from the "Oncology minimal data set" of HiGHmed project. In addition, we generated virtual data profiles for 10 patients using the template. The objective of this exercise is to provide a data model and virtual data profiles for testing and experimenting scenarios within the openEHR environment. Both of the template and the 10 virtual patient profiles are available publicly. [ABSTRACT FROM AUTHOR]

The work is aimed at identifying the data architecture and structuring the types of educational data analyzed on various digital educational platforms. The article presents research materials by the method of genetic analysis of labor actions and professional tasks of a teacher, necessary for a competent organization of development based on data. Study 1 studied 25 digital educational platforms for general education from different countries, including 9 Russian (public, private, and corporate). Study 2 included the method of genetic analysis of labor actions and professional tasks of teachers for organizing the development of children based on the analysis of educational data. The results obtained make it possible to say that in the digital educational environment the range of professional tasks is expanding, and the labor actions of the teacher are transformed in the implementation of developmental activities (as a labor function) based on the analysis of educational data.