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One factor for the achievement of the objectives of the concept of learning in the management of modern education is to utilize information technology that is supported by a reliable computer network architecture. In this research, the method of designing a computer network architecture is based on the 4 stages of the TOGAF-ADM Framework, namely vision architecture, business architecture, information systems architecture (data architecture and application architecture) and technology architecture. Then the system implementation uses the Network Development Life Cycle (NDLC) process which consists of analysis, design, simulation prototyping, implementation, monitoring, management and ending with an evaluation of computer network development. From the trial results, it is obtained that by using load balance technology and microtic device support, sending and receiving data packets is now faster and more stable because the communication lines have been segmented and the data collision in the network is resolved. The new network implementation successfully stabilized upload and download data speeds, overcoming limited connections and 0% packet loss.

Information technology is increasingly developing and has a positive impact on the world of education. The implementation of student counseling with a Knowledge Management System is one of the contributions of information technology in the world of education. Many benefits provided by the Student Counseling Knowledge Management System, in managing the knowledge needed by the counselor section to document student consultations. The development and implementation of a KMS counseling system costs more to employ professional staff to maintain and improve; KMS student counseling application; For this reason, it is necessary to design a Cloud Computing-based Student Counseling Knowledge Management System. The research method carried out in the first stage is to collect data and information about Knowledge Management and Student Counseling, and how to use it to create a Knowledge Management System Application, Student Counseling Based on cloud computing. Furthermore, conducting a literature study and literature review, system design is in the form of data architecture compounding, process design, network design, and user interface design. The design results of this system can facilitate educational institutions in conducting online cloud computing-based student counseling

This study introduces the database architecture that manages data in the main physical memory using columnar format. It proposes a conceptual framework for applying the in-memory columnar database systems to support high-speed continuous audit analytics. To evaluate the proposed framework, this study develops a prototype and conducts simulation tests. The test results show high computational efficiency and effectiveness of the in-memory columnar database relative to the conventional ERP system. Furthermore, the deployment of the in-memory columnar database to the cloud shows the promise of scaling up the in-memory columnar database for continuous audit analytics.

In Episode 354 of “Software Engineering Radio,” guest Avi Kivity, chief technology officer of ScyllaDB, talks with host Ninchant Suneja about ScyllaDB and what makes it a high-performance version of Cassandra, a distributed key-value data store. To hear the full interview, visit http://www.se-radio.net or access our archives via RSS at http:// feeds.feedburner.com/se-radio.

The present paper has the goal to document the usage of the Romans 1by1, a database which records the people attested in the epigraphic sources coming from the Roman provinces; so far, we have registered Dacia, Moesia Inferior and Moesia Superior in full and are currently working on the epigraphy of Pannonia. The information started to be collected in 2015, and it is still in progress, the intention being to extend the geographical area as much as possible. The database represents an extremely useful resource in the study of ancient population, as well as a very user- friendly research tool, which allows primary browsing and selective, advanced searches alike.

Event organizer is a company which engaged in event organizing, PR, and advertising. A suitable information systems that fit company's business needs are required so the company can adapt themselves in this globalization era. Information systems had an important role in order to support the company’s business process and its performance. An example of applied information systems that have been widely used by companies is enterprise architecture. Currently, enterprise architecture has been used by many enterprises to be able connect between the planning and the technology implementation to the current business in the company. Enterprise Architecture Planning (EAP) is a method in enterprise architecture which can provides alignment between business and information technology by defining company’s needs. Main components of EAP are data architecture, applications, and technology. The framework that will be used to develop EAP on this research is TOGAF ADM with phases starting from preliminary phase, requirements management, architecture vision, business architecture, information systems architectures, technology architecture, opportunities & solutions, and migration planning. A result on this research will be a blueprint of enterprise architecture model that an event organizer can use in supporting its business. The blueprint contains the current used information systems and the ideal information systems planned by the authors.

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin – the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as ‘revolutionary’ and ‘disruptive’, is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is ‘fit-for-purpose’ is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.

Being an interdisciplinary area, Internet of Things presents great challenges to learning. However, it already is and will continue to be part of the daily life and thus requires qualified professionals to advance projects in this area. Apart from acquiring theoretical concepts, students need to put knowledge into practice. This practical learning aims to provide a means of easy assimilation to the student and that can mirror real situations of implementation. This work presents an Internet of Things learning methodology based on the development of environments that enable the student to put theoretical knowledge into practice in a scenario of easy assimilation. It is expected that the student will be able to understand the process of developing Internet of Things projects and the technologies involved in it. The proposed methodology is composed of 5 steps. The student analyzes the development environment, defines the type of implementation to be carried out, develops the hardware, the software and documents of the project. The data architecture together with the methodology allow the student to use and propose various types of development environments, controllers and web applications, being very flexible for learning. The implementation of temperature control was carried out in an aquarium environment. The proposed methodology proved to be efficient for the development of this project, so it can be applied in Internet of Things learning in educational institutions.

major at consumer service. Information system Growth and Information Technology will affect to competition that growing competitive, this condition goes into effect also in the world of education especially for college that managed by society (private sector), claim the party of organizer to develop and develop information system in helping actifity business, reach organization target and service for stake holder especially related to data, information, technology and application. This Development and information system development must in harmony with instruct organization strategy (enterprise), found such cases of information system management experiences of failure in achieving target (objective) this organization because utilization disagree with collegepurpose and direction (organization). Enterprise Architecture Planning (EAP) as method or frame of reference to develop an information architecture. EAP is an architecture planning methods that orientation at business need that consist of data architecture, application and technology and implementation planning from architecture that has been made to support business activity to attainment of organization mission. EAP explains data architecture, application and technology that required to support organization business. Much developments IS experience of failure because need development IS are made base certain need without planning beforehand. Politeknik Piksi Ganesha, as one of higher education organizer that managed by private sector have strong comitment for information system utilization to support its business activity until must plan architecture enterprise. Architecture enterprise that will be modeled at Politeknik Piksi Ganesha, consist of : Data Architecture, Application, Technology Architecture, Roadmap implementation plan..Architecture enterprise modeling relate to processes that already commonly use in business higher education management in Politeknik Piksi Ganesha Bandung, where as discussion scope is limited at area Academic, area Administrations and Finance, area HRD and General.

Returnable container networks have caught the eye of those companies that aim to reduce waste generation and environmental impact. The literature already includes studies on the environmental impact (i.e. Life Cycle Assessment, LCA) of these networks. However, the major part is based on secondary data since the collection of primary data is complex and time-intensive. This paper proposes an object-relational database dedicated to the storage of data from a closed-loop reusable plastic crates (RPC) networks for fruits and vegetables. The goal is supporting scholars and managers during the LCA through a user-friendly data architecture, while suggesting structured guidelines for the primary data collection. Each node of the RPC network is characterized by a similar set of entity types, such as machines, which allows to process the RPCs with respect to specific cycles. Each entity, process and cycle are therefore reflected in the database by objects that are connected with relations.

LAZISMU as an amil zakat institution has an important role in collecting and managing zakat for public welfare. The existence of Law No. 23 of 2011 and the Minister of Religion Regulation No. 333 of 2015 on Guidelines for Granting Permits for the Establishment of Zakat Institutions requires that the management of zakat, infaq, and sadaq (ZIS) funds be transparent, accountable and prioritize professional principles. For this reason, there needs to be support from information systems and information technology to achieve these business goals. Information systems and technology will be utilized optimally if the development is aligned with the business objectives of the organization. Enterprise Architecture (EA) is a bridge to harmonize the business needs of an organization with Information Systems and Information Technology. Enterprise architecture planning at the LAZ has been designed using The Open Group Architecture Framework (TOGAF) with Architecture Development Method (ADM). Data collection is done by interviewing and making direct observations of the organization. Analysis tools used are the Unified Modelling Language (UML) method and the Value Chain method. The results of this study are blue print business architecture, data architecture, application architecture so that it can support the business processes that exist in LAZISMU.

Существующие в настоящее время методы оценки качества пищевой продукции далеки от совершенства, поскольку эта оценка осуществляется в лабораториях предприятий и, чаще всего, органолептическим путем. Успешное решение этой задачи возможно при внедрении в производственный процесс интеллектуальных экспертных систем контроля и прогнозирования в потоке показателей качества пищевых продуктов с использованием интеллектуальных технологий. Одной из важнейших составных частей экспертной системы является база данных. В статье проработаны методологические основы создания баз данных при построения интеллектуальной экспертной системы контроля и прогнозирования показателей качества пищевой продукции в процессе производства. Выделены отличительные особенности баз данных. Исследована и предложена методика разработки баз данных. Исследованы и проанализированы основные этапы разработки баз данных, а именно: проведена постановка задачи на проектирование базы данных предметной области. Разработана интегрированная концептуальная модель базы данных предметной области интегрированной экспертной системы. Разработаны: дерево целей базы данных, состав основных элементов системы базы данных интегрированной экспертной системы контроля качества пищевой продукции. Проработаны основные фазы и этапы проектирования базы данных интегрированный экспертной системы контроля качества пищевой продукции. Представлена VAD-диаграммы потока данных. Разработано описание семантики предметной области в виде системы функциональных и многозначных зависимостей между атрибутами концептуальной модели базы данных предметной области. Разработана автоматизированная системы обработки данных. Созданы приложения базы данных, в которые в соответствии с основными функциями и содержимым базы данных входят: программы обработки и передачи запросов, создание и обработка отчетов, программа управления приложениями. Разработана архитектура базы данных. Разработанная специализированная база данных интегрированной экспертной системы контроля показателей качества пищевой продукции является единой информационной средой, осуществляющей информационное обеспечение контроля органолептических показателей качества пищевой продукции.

This publication covers the highlights and interviews at the Strata Data Conference held in New York on September 11–13, 2018.

The agro-food industry is one of the largest parts of the European Union's economy and faces economic and environmental stresses. While food traceability systems (FTSs) inform supply chain actors of product and logistical attributes, large scale implementations are scarce and are do not support active decision making. We present a framework developed for FUTUREMED project used to perform a data-driven analysis that considers both micro and macro aspects of a food supply chain (FSC). With its comprehensive multiple-depth data architecture incorporated within a tailored decision-support platform, this framework and the resulting decision-support tool is the first to move beyond simple traceability implementation to the sustainable planning of food logistics, bridging the gap between research techniques and real-world data availability. We define KPIs that measure a subset of economic and environmental factors to quantify the impact of logistical decisions. We validate the framework with the case study of an Italian fruit trader that is considering opening a new warehouse. We conclude by suggesting that this framework be applied to more complex case studies and be enhanced through including more dimensions of sustainability.

In attempt to improve stormwater management and compliance the Ministry of Environment Conservation and Parks (MECP) has proposed a multi-site Environmental Compliance Approval (ECA) system for managing stormwater compliance at the subwatershed level. This research focuses on the identification and selection of stormwater objectives, criteria, targets, and thresholds to be included as part of the approval. This research also identifies and recommends traditional and alternative monitoring techniques for inclusion in a multi-site permit. Recommendations are provided for database architecture including storage, manipulation, and viewing of monitoring data. The selected stormwater objectives, criteria, targets, thresholds, monitoring techniques, and frequencies were compiled in a multi-site stormwater ECA framework to assist with the MECP with the implementation of multi-site stormwater ECAs within Ontario. The framework serves as an overview of important parameters that can be effectively monitored within a multi-site stormwater ECA.

Blockchain, or distributed ledger, continues to be deployed in the healthcare, energy, manufacturing, financial services sectors, and retail. Blockchain advocates argue that this technology constitutes a new architectural foundation for building trust based on its unique technological characteristics (Glaser 2017). However, cybersecurity is not a function of technological architecture but, in fact, is critically bound by human elements, processes, and actors. Therefore, all emerging technologies have cyber risk. Given the disruptive nature of this technology, there is a strong business need to understand cyber risk associated with blockchain. With this in mind, this investigation posed the following research questions: What are the risks associated with blockchain? Can a risk-based approach be validated or extended by exploring cases of blockchain deployment and highlighting risk? Multiple qualitative methods were used to analyze the data and to identify trends. This analysis identified cyber risk, in the context of blockchain, using the simple and generally accepted definition of cyber security as confidentiality, integrity, and availability (CIA). Based on the results, a cyber-physical risk-based approach was developed and blockchain deployment was examined in specific cases to identify and analyze the breaches according to this framework. The results demonstrate that beyond technological and data architecture there a strong need to further identify and analyze the cybercriminal actors and behavior in order to address cyber risk.

The technology of current electrical engineering enterprise ‘s architecture modeling is studied in a form which is convenient for analyzing the architecture’s state and determining its break with the target architecture which must be possessed by the enterprise during transformation into Industry 4.0. Instruments that are necessary for enterprise architecture designing and analyzing are still in status of development. Analyzing of current enterprise architecture demands methods of complex description of its layers and single elements. In this context enterprise architecture includes following: enterprise business architecture, application, data architecture, and technical system architecture. Aiming to find the best architectural solution, limits of resources, which will be available for building the enterprise architecture, as well as a set of metrics for evaluating and comparing project decisions, must be identified at each level. At the business architecture level, the business processes parameters and their relationship with other elements of this layer - organizational structure, management models, conceptual data model, as well as with elements of other layers of architecture are being analyzed. The applied methodologies will make it possible to objectify and concretize the concepts of enterprise architecture building, which will allow us to formulate a set of functional requirements for the software module for the analysis and evaluation of the optimal architecture. ArchiMate language allows to model an architecture in the most full and comprehensive way. An approach to the search for the optimal architectural solution based on significant criterion, which affects the architecture of the enterprise as a whole, is suggested. The proposed it contributes to the effective achievement of the goals of the enterprise within the framework of the adopted strategies. The set of enterprise business architecture has been developed with the use of the ArchiMate language. This approach is applicable to modeling the architecture of an electrical engeneeting enterprise, which provides support for modern technologies as the basis for its transformation into Industry 4.0.

International audience; Hundreds of articles containing heterogeneous data describe D variants or add to the knowledge of known alleles. Data can be difficult to find despite existing online blood group resources and genetic and literature databases. We have developed a modern, elaborate database for D variants, thanks to an extensive literature search with meticulous curation of 387 peer-reviewed articles and 80 abstracts from major conferences and other sources. RHeference contains entries for 710 RHD alleles, 11 RHCE alleles, 30 phenotype descriptions (preventing data loss from historical sources), 35 partly characterized alleles, 3 haplotypes, and 16 miscellaneous entries. The entries include molecular, phenotypic, serological, alloimmunization, haplotype, geographical, and other data, detailed for each source. The main characteristics are summarized for each entry. The sources for all information are included and easily accessible through doi and PMID links. Overall, the database contains more than 10,000 individual pieces of data. We have set up the database architecture based on our previous expertise on database setup and biocuration for other topics, using modern technologies such as the Django framework, BioPython, Bootstrap, and Jquery. This architecture allows an easy access to data and enables simple and complex queries: combining multiple mutations, keywords, or any of the characteristics included in the database. RHeference provides a complement to existing resources and will continue to grow as our knowledge expands and new articles are published. The database url is http://www.rheference.org/.

Concern for the quality of software-based systems has grown as software is integrated into every aspect of our lives. With the increase in the number of users and the data stored, more robust and secure databases are necessary. Quality control testing has progressed systematically from manual to automated applications. The Information Technology for Defense Company has the Center for Quality Standards and Security in charge of certifying and technically evaluating computer products, guaranteeing their quality. Currently the database review process in the SQLite manager is manual, making this process difficult, where there may be a margin of error due to the amount of checks that must be reviewed. The present work aims to develop a data architecture review component for the SQLite management server. For this, a study of a homologous database management system is carried out, the tools for testing existing databases and how a database architecture standard is formed. Functional tests are carried out on the component to verify its correct operation. Finally, a component integrated o the Zeolides framework is obtained, capable of automatically checking the architecture of the databases of the SQLite manager in the computer applications developed in the company., La preocupación por la calidad de los sistemas basados en software ha aumentado a medida que estos se integran en cada aspecto de nuestras vidas. Con el incremento del número de usuarios y de los datos almacenados se hace necesario contar con bases de datos más robustas y seguras. Las pruebas de control de calidad han progresado de manera sistemática desde lo manual hasta las aplicaciones automatizadas. La Empresa de Tecnologías de la Información para la Defensa cuenta con el Centro de Calidad, Estándares y Seguridad encargado de certificar y evaluar técnicamente productos informáticos, garantizando la calidad en los mismos. Actualmente el proceso de revisión de las bases de datos en el gestor SQLite es de forma manual, dificultando dicho proceso, donde puede existir un margen de error debido a la cantidad de comprobaciones que se deben revisar. El presente trabajo tiene como objetivo el desarrollo de un componente de revisión de arquitectura de datos para el servidor de gestión SQLite. Para ello se realiza un estudio de sistemas gestores de bases de datos homólogos, las herramientas de pruebas a las bases de datos existentes y cómo está conformado un estándar de arquitectura de bases de datos. Se realizan pruebas funcionales al componente para verificar su correcto funcionamiento. Finalmente se obtiene un componente integrado al marco de trabajo Zeolides, capaz de comprobar automáticamente la arquitectura de las bases de datos del gestor SQLite en las aplicaciones informáticas desarrolladas en la empresa.

The purpose of this research was follows: 1) synthesis of components of enterprise architecture and 2) to develop a vocational education digital enterprise architecture framework (VEDEAF) 3) evaluate of VEDEAF. Which have been studied from theories, documents and researches related to enterprise architecture, high performance organization, digital organization and operating process within Vocational Education College. The results of the research showed that components of VEDEAF consist of six component which are 1) Business Architecture, 2) Data Architecture, 3) Application Architecture, 4) Infrastructure Architecture, 5) Security Architecture and 6) Human Capital Architecture. Each components also divided into sub-module as follows; Business Architecture divided into 2 submodules which are Mission and Core Processes; Data Architecture divided into 2 sub-modules which are Core Process Data and Supporting Process data; Application Architecture divided into 3 sub-modules which are Instructional Application, Process Application and Integration Application; Infrastructure Architecture divided into 5 sub-modules which are Communication Platform, Operating System, Hardware Platform, Network and Data Communication Platform and Server Platform; Human Capital Architecture divided into 4 sub-modules which are Executive, Teacher, Student and Administration. To evaluation results of VEDEAF, the result found that the appropriate is at very high levels.

Landslide caused significant damage to people and economical properties. Establishing a comprehensive landslide database is a fundamental task to support landslide mitigation. However, numerous challenges such as non-standardized data collection, inefficient data maintenance, discontinuous data update, and insufficient data service have become the major problems to establish a reliable, dynamic, and comprehensive landslide database. After two decades of development, more than 322,000 landslide events have been collected through professional field surveys, routine field inspections, and public reporting into the China national landslide database. Also, an information system, which was primarily built for data dynamic management, field data acquisition and integration, classified data storage, efficient data processing, real-time statistics, and information services have been achieved. In this paper, the China national landslide database and information system, including the development progress, database architecture, data services, and future developments, are introduced in details. The key considerations and experiences obtained through the development of China national landslide database and information system such as the establishment of massive landslide inventory data acquisition standardization process, comprehensive mechanism of data quality control and dynamic maintenance, services of landslide information system could be helpful for any other countries or regions that also greatly suffering from landslides, and contribute to the improvement of landslide risk reduction in the world.

Federal agencies, including the Department of Veterans Affairs (VA), have prioritized improved access to scientific data and results collected through federally funded research. Our VA Cooperative Studies Program Epidemiology Center in Durham, North Carolina (CSPEC-Durham) assembled a repository of data and specimens collected through multiple studies on Veteran health issues to facilitate future research in these areas. We developed a single protocol, request process that includes scientific and ethical review of all applications, and a database architecture using metadata (common variable descriptors) to securely store and share data across diverse studies. In addition, we created a mechanism to allow data and specimens collected through older studies in which re-use was not addressed in the study protocol or consent forms to be shared if the future research is within the scope of the original consent. Our CSPEC-Durham Data and Specimen Repository currently includes research data, genomic data, and study specimens (e.g., DNA, blood) for three content areas: colorectal cancer, amyotrophic lateral sclerosis, and Gulf War research. The linking of the study specimens and research data can support additional genetic analyses and related research to improve Veterans' health.

The benefits to data analytics and machine learning have been distributed unevenly across firms around the world. Research on IT productivity points to intangible capital as a key driver of value creation from innovation in computing. We argue that a crucial component of intangible capital is organization-wide technological architecture, which is idiosyncratic and difficult to measure. We use a novel survey instrument to quantify large corporations’ data architecture capabilities by their closeness to “best practices” of frontier digital companies. Using the prevalence of third-party maintenance as a proxy for legacy servers before 2016 and an instrument for data architecture coherence, we find that improving data architecture coherence leads to more intensive machine learning capabilities and substantially higher productivity growth in the subsequent three-year period. Legacy servers reduce data architecture coherence particularly at corporations with complex software systems, consistent with the hypothesis that costs of digital transformation are greater when workers need to develop more complicated co-invention processes to interact with technical systems.

The amount of data that we consume each day is overwhelming. According to Jeff Desjardins, founder and editor of Visual Capitalist, the world has an estimate of 44 zettabytes of data. A zettabyte is 1,000 to the power of 7 or 1 with 21 zeros. Fortunately, most architects do not need to manage data that large, but the size of the data we manage is still staggering.

Persistent identifiers (PID) to identify digital representations of physical specimens in natural science collections (i.e., digital specimens) unambiguously and uniquely on the Internet are one of the mechanisms for digitally transforming collections-based science. Digital Specimen PIDs contribute to building and maintaining long-term community trust in the accuracy and authenticity of the scientific data to be managed and presented by the Distributed System of Scientific Collections (DiSSCo) research infrastructure planned in Europe to commence implementation in 2024. Not only are such PIDs valid over the very long timescales common in the heritage sector but they can also transcend changes in underlying technologies of their implementation. They are part of the mechanism for widening access to natural science collections. DiSSCo technical experts previously selected the Handle System as the choice to meet core PID requirements. Using a two-step approach, this options appraisal captures, characterises and analyses different alternative Handle-based PID schemes and the possible operational modes of use. In a first step a weighting and ranking the options has been applied followed by a structured qualitative assessment of social and technical compliance across several assessment dimensions: levels of scalability, community trust, persistence, governance, appropriateness of the scheme and suitability for future global adoption. The results are discussed in relation to branding, community perceptions and global context to determine a preferred PID scheme for DiSSCo that also has potential for adoption and acceptance globally. DiSSCo will adopt a ‘driven-by DOI’ persistent identifier (PID) scheme customised with natural sciences community characteristics. Establishing a new Registration Agency in collaboration with the International DOI Foundation is a practical way forward to support the FAIR (findable, accessible interoperable, reusable) data architecture of DiSSCo research infrastructure. This approach is compatible with the policies of the European Open Science Cloud (EOSC) and is aligned to existing practices across the global community of natural science collections.

The convergence of Artificial Intelligence and Blockchain is growing very fast in everyday applications and industry. In centralized systems and applications like healthcare, data access and processing in real-time among various information systems is a bottleneck. Blockchain’s decentralized database architecture, secure storage, authentication, and data sharing would offer a solution to this problem. Besides, Artificial Intelligence can live at the top of Blockchain and generate insights from the generated shared data used to make predictions. Blockchain is a high-level cybersecurity technology that forms chains that connect the existing blocks stored in nodes and the new block chronologically by mutual agreements between nodes. Technology convergence accelerates various industries’ growth, such as banking, insurance, cybersecurity, forecasting, medical services, cryptocurrency, etc… The more digital systems are adopted and services provided by these industries, the greater the risk of hacking these systems. Combining blockchain power and artificial intelligence can provide a strong shield against these attacks and security threats. In this chapter, we will study the convergence of AI and Blockchain in cybersecurity. We will expand on their role in securing cyber-physical systems.

AI is increasingly penetrating the production industry. Today, however, AI is still used in a limited way in a production environment, often focusing on a single production step and using out-of-the-box AI algorithms. AI models that use information spanning a complete production line and even larger parts of the product lifecycle could add significant value for production companies. In this paper, we suggest a digital twin architecture to support the complete AI lifecycle (discovering correlations, learning, deploying and validating), based on a knowledge graph that centralizes all information. We show how this digital twin could ease information access to different heterogenous data sources and pose opportunities for a wider application of AI in production industry. We illustrate this approach using a simplified industrial example of a compressor housing production, leading to preliminary results that show how a data scientist can efficiently access, through the knowledge graph, all necessary data for the creation of an AI model.

Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

The COVID-19 vaccine was developed less than a year after the global pandemic first began to spread, but storage at ultralow temperatures and multiple vaccinations are required. The logistics for the successful acquisition of herd immunity are complex and difficult to prepare. Tamba City, Japan, is trying to quickly build a vaccination system by utilizing the existing regional comprehensive care system linked to the basic resident register. For this system to contribute to the suppression of the spread of COVID-19 infection and the efficiency of the treatment system, it is necessary to evolve the architecture, including the data architecture to handle distributed and diversified data.

The modern medical management system is influenced by modern medical concepts (value medicine, predictive medicine), on the one hand, and technologies that provide digital transformation (IoT, Big Data, blockchain, etc.), on the other hand. Digital transformation involves the implementation of fundamental changes in the activities of the organization using digital technology. At the same time, business models, medical organization development strategies, business process systems, IT architecture, services architecture, data architecture, etc., change. The object of the research is medical organizations, being the subject of research the digital transformation process of a medical organization. The hypothesis of research: the application of the architectural approach to managing the digital transformation of a medical organization will allow the formation of a reference architectural model of the upper level, as well as the upper level of the digital transformation roadmap of the medical organization. The main result of the research is the formation of the architectural model of the upper level, as well as the formation of the upper level of the transition plan during the digital transformation of the medical organization.

Данная работа посвящена изучению Ñ‚ÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Big Data и Business intelligence для повышения эффективности анализа Ð´Ð°Ð½Ð½Ñ‹Ñ Ð¿Ñ€Ð¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ñ‚ÐµÐ»Ñ Ñ‚Ñ€Ð°Ð½ÑÐ¿Ð¾Ñ€Ñ‚Ð½Ñ‹Ñ ÑÑ€ÐµÐ´ÑÑ‚Ð². В исследовании поставлены следующие задачи: − изучить автопроизводителя; − проанализировать процесс дистрибуции компании; − изучить теоретические аспекты Ñ‚ÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Big Data; − изучить теоретические аспекты Ñ‚ÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Business Intelligence; − проанализировать рынок BI-Ñ‚ÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹; − принять решение о выборе оптимального решения в области бизнес-аналитики для производителя; − изучить инфраструктуру и Ð°Ñ€Ñ Ð¸Ñ‚ÐµÐºÑ‚ÑƒÑ€Ñƒ Ð´Ð°Ð½Ð½Ñ‹Ñ ÐºÐ¾Ð¼Ð¿Ð°Ð½Ð¸Ð¸, выявить «узкие места» и способы улучшения; − разработать BI-приложение и оценить результат его внедрения. Магистерская диссертация включает в себя несколько методов исследования, Ñ‚Ð°ÐºÐ¸Ñ ÐºÐ°Ðº основные общенаучные методы исследования, сравнительный анализ, Ð°Ñ€Ñ Ð¸Ñ‚ÐµÐºÑ‚ÑƒÑ€Ð½Ð¾Ðµ моделирование, моделирование Ð´Ð°Ð½Ð½Ñ‹Ñ , метод статической оценки эффективности IT-проектов. Основными результатами работы являются: − дан краткий обзор автопроизводителя и процесса дистрибуции; − изучены основные аспекты Big Data и Business Intelligence; − на основе анализа рынка BI было выбрано лучшее BI-решение; − исследована инфраструктура и Ð°Ñ€Ñ Ð¸Ñ‚ÐµÐºÑ‚ÑƒÑ€Ð° Ð´Ð°Ð½Ð½Ñ‹Ñ ÐºÐ¾Ð¼Ð¿Ð°Ð½Ð¸Ð¸, рассмотрены узкие места, приняты решения по улучшению; − разработано BI-приложение, оценен результат его внедрения., The given work is devoted to studying Big Data and Business intelligence technologies to increase the efficiency of data analysis of the vehicle manufacturer. The research set following goals: ‒ investigate a vehicle manufacturer; ‒ analyse the company’s distribution process; ‒ explore theoretical aspects of Big Data technologies; ‒ explore theoretical aspects of Business Intelligence technologies; ‒ analyse a market of Business Inteligence technologies; ‒ decide the best Business Intelligence solution for the manufacturer; ‒ investigate the company’s data infrastructure and architecture and decide how to improve it; ‒ develop a BI application and evaluate the result of its implementation. The master thesis includes several research methods, such as basic general scientific methods of research, comparative analysis, architectural modeling, data modeling, method of static evaluation of the effectiveness of IT-projects. The main results of the paper are: ‒ a brief overview of vehicle manufacturer was given and its distribution process was analysed; ‒ basic Big Data and Business Intelligence aspects were explored; ‒ BI market was analysed; ‒ the best BI solution was decided due to BI market analysis; ‒ company’s data infrastructure and architecture was investigated, bottlenecks were considered, improvements were decided; ‒ BI application was developed, the result of its implementation was evaluated.

This paper presents a middle data layer that is designed and implemented based on the ANSI/ISA-95 industrial standard. The proposed middle data layer extracts key information on manufacturing operations from the control systems as well as the business systems. The proposed model enhances interoperability in Industry 4.0 by using a standardized and formalized data structure. We expand the above idea into three directions. Firstly, we analyze all the layers of the traditional automation hierarchy model of ISA-95 to get an overview of objects and activities that are needed for building a middle data layer and re-structure the traditional hierarchy levels by introducing the ISA-95 based middle data layer. Secondly, we design the data architectures by categorizing the data source and explain the formalized and standardized data models. Finally, we use a Smart Manual Station of a production setup to show how to apply the proposed ISA-95 middle data layer to the real world case. The results indicate that the middle data layer enhances the interoperability of manufacturing systems and creates a universal standardized data structures for systems integration for factory automation.

This chapter first discusses the industry challenges for a financial institution and derives from these the need for resilience and agility for the finance department. Following the changing mandate for finance, the business processes and data architecture are examined. The final section summarizes the value synchronized business processes and data can bring and how to deploy such an undertaking.

Many research institutions are exploring the best practice of model-based system engineering in order to improve the development quality of complex system, reduce the design cost and shorten the development cycle. The collaborative development of complex systems by MBSE is the focus of system engineering research and the foothold of engineering application. This paper proposes a new modeling idea-the MBSE system collaborative design idea based on MSFC data architecture, which used a unify data source and effective transfer of the model for system analysis and multi-professional collaborative design and used MSFC architecture to implement collaborative design for a certain aileron system to verified the feasibility of the theory.This paper verified the relevant requirements in the aileron system analysis process through the system modeling and simulation of the control subsystem, the hydraulic drive subsystem, the actuator subsystem and the rudder surface. The research shows that MSFC architecture was suitable for the collaborative design process of complex system that can be used as the modeling idea of MBSE engineering application.

In this contribution, we studied the deep neural network (DNN) for the control of the cooling crystallization of a model compound system. To this end, firstly the performance of the different neural network architectures in conjunction with the various combination of the time-series process data was tested for the training of the data-based model in order to assess the best fit model-training data architecture. The identified network model, which was trained with the offline process data, was utilized in a predictive control strategy. The objective of the control strategy was to optimize the supersaturation generating/decaying variable in the crystallizer to achieve a target crystal-state property profile throughout the process. The performance of the proposed control strategy was tested in the presence of the process disturbance and benchmarked against a radial basis function (RBF) based control strategy. The results showed that the DNN model was able to approximate the crystallization process input-output relation with R2 ranging between 0.767 and 0.990 and achieve the target profile at the end of the operation with a 22.3 % offset.

The main problem faced by the IT sector is to manage large data of the clients precisely. Multi-tenancy of data and its efficient retrieval is the major concern to optimize. Cloud services become more complex in a heterogeneous computing environment. A simple query efficient database architecture is proposed for the SaaS architecture model of cloud. In the proposed approach, XML attributes are used to store a large number of attributes of the database to the same field and saved in the form of XML. The efficiency of transaction operations is enhanced for insertion, deletion and update queries. The extension table approach (ETA) is used where a single table can hold multiple values of all the consumers/tenants. The proposed approach performs better as compared to existing approaches such as ETA and NTA where the number of attributes is increased up to 50 queries.

Organizations have found a great ally in information technologies to achieve success in their management processes. For universities, for example, information systems and platforms are essential to support the academic and administrative management of their institutional mission. These platforms, whether proprietary or licensed, serve institutions not only as a data repository but also as a scenario for managing and systematizing processes and procedures. The Pascual Bravo University Institution in Medellin, Colombia, has articulated its academic management processes through the University Academic Information and Control System or SICAU (in its Spanish acronym) platform, a proprietary development that perhaps has not achieved a coherent integration between information architecture and interface design. That is, the information experience specified in the backend (data architecture) is not integrated or reflected in the experience of the different users in the frontend (interface design). The objective of this case study is to propose the design of the interface level interaction (graphic elements and feedback) for the SICAU platform in the teaching module at the Pascual Bravo University Institution. It is concluded that the analysis on action maps allows to identify gaps on the information architecture. The interaction analysis model proposed in this research facilitated the design of interactions with the purpose of improving the user experience (UX) and attributes in the platform’s backend.

In the context of modern big data, power grid information technology is a particularly important work for power grid staff. In the process of the in-depth construction of smart power grid, the demand for the integration of monitoring data of marketing operation is also increasing, and the data exchange is developing towards the direction of complexity, which requires the continuous improvement of the real-time performance of data and the risks in performance, security and management risks generated by data integration should be reduced. It is particularly important to create a unified management tool for data interaction. Through the professional R&D team in big data technology, the marketing operation monitoring data sharing can be highlighted, to create an integrated marketing data center. At present, the construction of marketing operation monitoring information is the focus of the work, which can effectively promote the development of big data. Therefore, this paper analyzes the data architecture of the marketing operation monitoring platform under the smart grid for the reference of relevant research.

Massively open online courses (MOOCs) represent an incumbent technology that, in light of the coronavirus disease (COVID-19) pandemic, are receiving renewed attention, with innovative applications for lifelong learning. Through their scalable virtual delivery, sophisticated data architecture, and focus on emerging digital skills, MOOCs represent an agile foundation on which a modern learning society can be built and scaled to effectively serve a global population. This chapter explores applications of online learning platforms and MOOCs to create a modern learning society through an examination of Coursera’s postpandemic Workforce Recovery initiative, in which the company partnered with over 300 governments and workforce intermediaries to provide access to no-cost digital upskilling for displaced workers across the globe. In a time of economic, technological, and societal disruption, online learning platforms represent a low-cost, scalable approach to delivering customized, timely, and job-relevant training to a multitude of learners. The chapter includes an overview of skills transformation in Asia and the Pacific as viewed through the lens of Coursera’s 70+ million users and its 2020 Global Skills Index. A summary of initial findings from the Workforce Recovery initiative is presented, with highlights from across Asia and the Pacific, as well as a discussion of leading practices and lessons learned to date. The chapter concludes with a forward-looking vision for how online learning could support workforce reskilling on a global scale.

To seriously implement the overall strategy of governing the country according to law and State-owned Assets Supervision and Administration Commission of the State Council state under the rule of law construction requirements, comply with the energy revolution and the trend of the digital revolution, unite in hand, closely around company construction has the Chinese characteristic the international leading energy Internet enterprise strategic target, the company started in 2019 a new generation of information platform construction of rule of law, to build an information management system based on SG-UAP3.0 development platform with leading architecture, business collaboration, information sharing, mobile intelligence and to meet the construction requirements of the state Grid based on the rule of law. In 2019, the Legal Department of the State Grid organized and completed the in-depth investigation work of the headquarters and companies in provinces, and formed the consultation report of the framework design project. In 2020, it plans to build a new generation of information platform for the rule of law. The system architecture of the new generation of rule of law information platform is introduced in detail from four aspects: business architecture, application architecture, data architecture and technology architecture.

The proliferation of IoT devices is ubiquitous. While there is significant ongoing research and development in the area of IoT devices and the associated challenges (Eg: power, space, ...), the end goal of an IoT deployment is often to process the collected data in order to run analytics (i.e., batch) or to react/respond (i.e., real-time). Therefore, the flow of data from individual sensors all the way up to the servers that analyze massive data needs needs to be carefully architected. Further, the flow of data can guide the design choices (CPU, RAM, Storage, Networking) of system at each step of the IoT data flow chain. This paper presents design considerations for this “end-to-end” flow of data from IoT device to server.

In this paper, a five-step approach coupling data science and life-cycle assessment is detailed. The main purposes of the study are to collect and analyse the process data from scientific literature, to characterize clusters, and to help the decision support in the preliminary choice of biomass or valorisation process according to the environmental indicators. The approach is tested on the comparison of rice straw and corn stover pretreatment processes for glucose production. Following the goal and scope, data architecture, life-cycle inventory, and environmental assessment steps, an impact-process matrix is analyzed in the last step using the multidimensional scaling method (MDS). The visualization of the results shows clusters that allow making a first analysis of the processes.

In modern conditions, the solution of the complex tasks of the strategic development of innovative business raises the status of information, shifts the priority in managing information resources to the structural and strategic management levels. Thereby, there is a need to create a proactive corporate information space of the corresponding information and analytical level. A flexible and adaptive management information system is becoming one of its main components. The life cycle of such a corporate information system should correspond to the life cycle of a business. This work considers the questions of formation and actualization of enterprise architecture based on a three-dimensional strategic model of the continuous integration of its components (3D-architecture of enterprise): information technology architecture (data architecture and application architecture) and business architecture. The static structure of the model is made up of a multi-layer enterprise architecture. The dynamic part of the model is built using digital twin information technology (Digital Twin) and the Internet of things (IoT).

The data management discipline has several functions or components with data governance being identified as one of the core components of data management tying together the other data management functions and data initiatives—for example, data architecture management, data modelling and design, master data management, reference data management, data warehousing and business intelligence, data quality management, metadata management, data security management, data storage and operations, document and content management, data integration and interoperability, data migration, big data, and analytics. In this chapter, we discuss the alignment and interaction of data governance in detail with each of these data management functions. The concept of big data, how big data differs from traditional data, and the application of data governance in big data, analytics, and data lakes has also been discussed in a separate section.

The Database field is undergoing significant changes. Although relational systems are still predominant, the interest in NoSQL systems is continuously increasing. In this scenario, polyglot persistence is envisioned as the database architecture to be prevalent in the future. Multi-model database tools normally use a generic or unified metamodel to represent schemas of the data model that they support. Such metamodels facilitate developing utilities, as they can be built on a common representation. Also, the number of mappings required to migrate databases from a data model to another is reduced, and integrability is favored. In this paper, we present the U-Schema unified metamodel able to represent logical schemas for the four most popular NoSQL paradigms (columnar, document, key-value, and graph) as well as relational schemas. We will formally define the mappings between U-Schema and the data model defined for each paradigm. How these mappings have been implemented and validated will be discussed, and some applications of U-Schema will be shown. To achieve flexibility to respond to data changes, most of NoSQL systems are "schema-on-write," and the declaration of schemas is not required. Such an absence of schema declaration makes structural variability possible, i.e., stored data of the same entity type can have different structure. Moreover, data relationships supported by each data model are different. We will show how all these issues have been tackled in our approach. Our metamodel goes beyond the existing proposals by distinguishing entity types and relationship types, representing aggregation and reference relationships, and including the notion of structural variability. Our contributions also include developing schema extraction strategies for schemaless systems of each NoSQL data model, and tackling performance and scalability in the implementation for each store., Comment: 31 pages, 18 figures

While relational databases are still predominant, there is a growing interest in NoSQL, and many database systems are evolving to accommodate several NoSQL paradigms. Moreover, polyglot persistence is envisioned as the database architecture of complex modern applications. In this heterogeneous scenario, the existence of a paradigm-independent language to specify schemas is of paramount importance. Here we present Athena, a database-independent schema declaration language. Athena is a textual domain specific language based on a generic metamodel able of representing NoSQL and relational schemas. Along this paper, we will describe the requirements of the language, constructs, and some applications of Athena.