Data Architecture Research Articles

Contemporary High-Performance Computing for Big Data Applications

High-performance computing (HPC) involves leveraging parallel data processing to enhance computer performance and handle difficult tasks. HPC meets these aims by pooling computing capacity, enabling efficient, reliable, and prompt execution of even complex programs according to user demands and expectations. The rapid growth of HPDA in many sectors has led to the extension of the HPC market into new territory. HPC as well as Big Data systems differ not just in terms of technology but also in ecosystems. Extensive research in this sector has led to the emergence of various Big Data analytics models in recent years. As Big Data analytics spreads across several fields, new challenges about the usefulness of analytical paradigms also emerge. This article discusses the key analytical models, as well as the difficulties and challenges associated with high-performance data analytics. This research work aims to identify the factors influencing the integration of HPC with big data, including present and future trends. The study also proposes an architecture for big data with HPC convergence based on design principles.

A Comprehensive Survey on Automatic Knowledge Graph Construction

Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey covers models for knowledge refinement, including knowledge graph completion, and knowledge fusion. Methods to handle knowledge evolution are also systematically presented, including condition knowledge acquisition, condition knowledge graph completion, and knowledge dynamic. We present the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture. Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. The survey concludes with discussions on the challenges and possible directions for future exploration.

PENGEMBANGAN APLIKASI OPERASIONAL RESTORAN DENGAN FRAMEWORK SCRUM (STUDI KASUS: RESTORAN PT.XYZ)

In software development, it is necessary to plan carefully, so that the software developed is in accordance with user needs. Software development without good planning will result in a patchwork of software development. To avoid the patchwork of software development, a software development framework is required. The research "Developing Restaurant Operational Applications With Scrum Framework (Case Study: PT.XYZ Restaurant)" aims to develop restaurant operational software at PT. XYZ. The software development framework used in software development uses the Scrum framework. The Scrum framework is a variant of the Agile framework. Agile has a dynamic way of working (iterative and incremental). The Scrum framework has a product backlog, then the product backlog is divided into several sprints. One sprint will be run in one iteration, so that each end of a product sprint that meets the definition of “Done” must be achieved. The use of the Scrum framework emphasizes communication and collaboration between various stakeholders. The result of this research is a software that helps restaurant operations at PT. XYZ starting from master files, transactions files, to reports files. By using the Scrum framework in application development, the application development life cycle is created. Software development can be done by implementing an enterprise architecture that already belongs to a company. Data architecture, application architecture, and technology architecture as the basis for application development in the enterprise

Cooperating and Competing Digital Twins for Industrie 4.0 in Urban Planning Contexts

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.

Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation

AbstractMedical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. Computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary for achieving accurate outcomes. Image contrast is one of the most essential visual parameters. Insufficient contrast may present many challenges for computer vision techniques. Traditional contrast adjustment techniques may not be adequate for many applications. Occasionally, these technologies create photos that lack crucial information. The primary contribution of this work is designing a Big Data Architecture (BDA) to improve the dependability of medical systems by producing real-time warnings and making precise forecasts about patient health conditions. A BDA-based Bio-Medical Image Classification (BDA-BMIC) system is designed to detect the illness of patients using Metaheuristic Optimization (Genetic Algorithm) and Gradient Approximation to improve the biomedical image classification process. Extensive tests are conducted on publicly accessible datasets to demonstrate that the suggested retrieval and categorization methods are superior to the current methods. The suggested BDA-BMIC system has average detection accuracy of 94.6% and a sensitivity of 97.3% in the simulation analysis.

Perencanaan Arsitektur Enterprise Menggunakan Framework TOGAF (The Open Group Architecture Framework)

PKU Muhammadiyah Mayong Hospital is a health institution that focuses on excellent service to patients. The increasing need for service functions is a driving factor for the application of information technology at PKU Muhammadiyah Mayong Hospital. To support the alignment of the application of information systems with the needs of the hospital, a paradigm called enterprise architecture is needed. This study aims to analyze the planning of enterprise architecture using the Open Group Architecture Framework (TOGAF) framework with the Architecture Development Method (ADM) method at PKU Muhammadiyah Mayong Hospital. TOGAF is a generic architectural framework designed to be used in various types and sizes of organizations. ADM is a method that contains a set of activities used in modeling the development of enterprise architecture. This research is expected to be useful for PKU Muhammadiyah Mayong Hospital to integrate business processes with information technology. At each stage of ADM what is done is the process of identifying problems, analyzing problems and solutions to these problems. In this study the stages used only up to the information system architecture. The results of this study are proposed business architecture, data architecture and application architecture.

Research on the Application of Big Data Analysis Platform in the Enterprise Management Optimization

As companies attempt to make analysis an important data support component of daily decision-making, big data analysis technology is rapidly expanding across all industries. Although there are many software tools and libraries available to assist analysts and software engineers in developing solutions, enterprises are looking for reliable analysis platforms that can meet their specific goals and requirements. In order to minimize costs, such platforms also need to coexist with existing IT infrastructure and reuse the knowledge and resources already accumulated within the organization. To meet these requirements, this article proposes the Data Analysis Solution Engineering (DASE) framework - a knowledge driven approach supported by semantic web technology, for the design and development of requirements engineering and new data analysis platforms. It includes capturing data analysis platform requirements through a knowledge base, and enterprises learning how to use this data analysis platform to analyze all daily production data involved in the engineering data analysis platform. This article analyzes the DASE framework through knowledge modeling, requirement modeling, data architecture modeling, and platform design modeling, and demonstrates how it promotes knowledge and requirement driven data analysis platform engineering. The resulting data analysis platform is considered user-friendly, easy to maintain, and flexible in handling changes in requirements. This work contributes to the knowledge system of knowledge driven requirements engineering and data analysis platform engineering by providing customized models and reference architectures for different analytical application fields.

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building’s energy efficiency.

Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

AbstractOvarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women.

Research on Expanding Grassroots Employment Space for College Students in the Context of Rural Revitalization + Data Platform

Abstract In this paper, in the context of rural revitalization + data platform, a grassroots employment platform for college students is designed according to the data architecture, and the data is processed hierarchically to achieve the organization and knowledge accumulation of cross-domain employment data, which makes multiple employment data standardized. Using data analysis parallelization algorithm and dimensionality reduction operation method, the training data are read and loaded into the distributed memory of the employment data platform to select the best employment features. According to the design platform of this paper, the employment rate for those employed in the eastern region and 80.16% in the central region has reached 91.15%. This paper's design model can improve employee efficiency and expand the grassroots employment space of college students, as indicated.

Research on Digital Management and Collaboration Platform for Civil Aircraft Manufacturing Logistics Scenarios

Abstract In this paper, the overall architecture design of the platform is proposed, and the data architecture and business architecture of the platform are discussed. On this basis, the logistics path planning method of digital collaboration is proposed, including the aspects of path demand splitting and distribution vehicle spatio-temporal network, and the demand splittable logistics path planning model is considered. Finally, the simulation of the workshop logistics dynamic scheduling example is designed, and the dynamic scheduling optimization results are analyzed and verified. The results show that: through optimization, its time penalty cost f 1 = 801.38, the total distance traveled by the delivery trolley f 2 = 1709m, and the total distance of empty load is 692.15 m, and the dispatching scheme increases the distance of empty load by 59 m . The research results of this paper can provide a reference for digital management and collaboration platform for civil aircraft manufacturing enterprises and provide a reference for research in related fields.

Optimising ePrescribing in hospitals through the interoperability of systems and processes: a qualitative study in the UK, US, Norway and the Netherlands

BackgroundInvestment in the implementation of hospital ePrescribing systems has been a priority in many economically-developed countries in order to modernise the delivery of healthcare. However, maximum gains in the safety, quality and efficiency of care are unlikely to be fully realised unless ePrescribing systems are further optimised in a local context. Typical barriers to optimal use are often encountered in relation to a lack of systemic capacity and preparedness to meet various levels of interoperability requirements, including at the data, systems and services levels. This lack of systemic interoperability may in turn limit the opportunities and benefits potentially arising from implementing novel digital heath systems.MethodsWe undertook n = 54 qualitative interviews with key stakeholders at nine digitally advanced hospital sites across the UK, US, Norway and the Netherlands. We included hospitals featuring ‘standalone, best of breed’ systems, which were interfaced locally, and multi-component and integrated electronic health record enterprise systems. We analysed the data inductively, looking at strategies and constraints for ePrescribing interoperability within and beyond hospital systems.ResultsOur thematic analysis identified 4 main drivers for increasing ePrescribing systems interoperability: (1) improving patient safety (2) improving integration & continuity of care (3) optimising care pathways and providing tailored decision support to meet local and contextualised care priorities and (4) to enable full patient care services interoperability in a variety of settings and contexts. These 4 interoperability dimensions were not always pursued equally at each implementation site, and these were often dependent on the specific national, policy, organisational or technical contexts of the ePrescribing implementations. Safety and efficiency objectives drove optimisation targeted at infrastructure and governance at all levels. Constraints to interoperability came from factors such as legacy systems, but barriers to interoperability of processes came from system capability, hospital policy and staff culture.ConclusionsAchieving interoperability is key in making ePrescribing systems both safe and useable. Data resources exist at macro, meso and micro levels, as do the governance interventions necessary to achieve system interoperability. Strategic objectives, most notably improved safety, often motivated hospitals to push for evolution across the entire data architecture of which they formed a part. However, hospitals negotiated this terrain with varying degrees of centralised coordination. Hospitals were heavily reliant on staff buy-in to ensure that systems interoperability was built upon to achieve effective data sharing and use. Positive outcomes were founded on a culture of agreement about the usefulness of access by stakeholders, including prescribers, policymakers, vendors and lab technicians, which was reflected in an alignment of governance goals with system design.

A Model of Big Data Architecture on the Base of FIWARE Components

The intensive development of hardware and software tools provides many opportunities for solving various real-world tasks, such as working with large data sets. This, in turn, is a powerful incentive and opportunity for the development of different approaches and tools for solving them. After discussing several well-known BigData architectures a model of a BigData architecture is designed. The architecture is organized in three sections: edge section, processing-and-storage section and application section. The model will be implemented using FIWARE components.

Data-driven decision-making challenges of local government in Indonesia

PurposeThis study aims to identify and outline the critical challenges affecting the inclination of executives to use data as the basis for making decisions at a local government level.Design/methodology/approachThe study conducted in-depth interviews with 21 public officials comprising middle- and top-level executives from 18 agencies and offices at the Bojonegoro Regency, one of Indonesia’s most progressive regencies in pursuing open government and smart cities.FindingsThe findings demonstrate that ensuring a good quality data architecture, nurturing data culture and developing analytics capability are essential in the case of a developing country such as Indonesia. However, insufficient policies and regulations, a nonexistent evaluative framework for data quality, disruptive local tradition and the ingrained autocratic administration represent significant and unique challenges to implementing data-driven decision-making in the local government in Indonesia.Research limitations/implicationsThe chosen research approach may result in a need for more generalizability beyond Indonesia, accentuating the necessity for the geographical objects to include other developing countries in future research.Practical implicationsThe findings showcase that lack of awareness and acceptance from public officials and the general public of the importance of a data-driven approach; as such, a better understanding of the change in attitudes and mindsets of public officials is invariably one of the critical practical determinants.Originality/valueThe findings signify the importance of creating robust accountability systems and evaluative frameworks that consider the many variables influencing decisions that capture the significance of organizational and local culture.

Data Recognition for Multi-Source Heterogeneous Experimental Detection in Cloud Edge Collaboratives

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with traditional manual data detection and recognition. Firstly, a recognition architecture for experimental detection data of intelligent measurement systems is designed based on a cloud-edge collaborative environment to improve the efficiency of data processing. Then, the improved DBNet network is used in the edge computing center to detect the text, and the correction module is used to correct the deviation of the detected text to ensure the standardization of the text. Finally, in the central cloud, the end-to-end indefinite length character recognition (CRNN) algorithm is used to analyze and identify the text order, rules, and other information of the image after the correction is completed, extract the test detection data, and convert the detection data into row data according to the two-dimensional table structure, and conduct structured storage and management through the relational database. An experimental analysis of the proposed method is conducted based on the deep learning framework, and results show that its accuracy rate and recall rate are close to 96% and 94%, respectively, with an average accuracy of 95.09%. This fully demonstrates the proposed method is effective and, therefore, applicable to power equipment experimental detection data recognition.

Strategic Information System Planning at RSUD Berkah Pandeglang Using the EAP (Enterprise Architecture Planning) Method

The intense competition in business development requires enterprises to innovate to assist in their business processes. One way to achieve success in innovation is by planning information systems and information technology that can meet business needs and align with current developments, using the Enterprise Architecture Planning (EAP) method. The results of the strategic information system planning include data architecture, application architecture, and network architecture. This research has produced a strategic planning document that can be used by RSUD BERKAH PANDEGLANG. This document contains data architecture, application architecture, and technology architecture.

A Unified Data Architecture for Assessing Motor Symptoms in Parkinson's Disease.

The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings. However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability. To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema. We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution.

Web-based E-learning in Elementary School: A Systematic Literature Review

This article presents literature review on web-based e-learning in elementary school in the latest literature. SLR method and PRISMA protocol with the stages of identification, screening, eligibility, inclusion, and abstraction, data analysis assisted by the Publish or Perish 7 application, VOSviewer, and NVIVO 12 Plus. The results of searching for articles on Scopus through the Publish or Perish 7 application are 507. Then the articles were filtered according to compatible themes into 50 articles. The topic findings are web-based e-learning, elementary school, the impact of web-based e-learning and web-based e-learning concept, academic performance, teaching/learning strategies, online learning, Covid-19, HPC database, web-based applications, distance learning, 3D visualization, automation, strategic learning, semantic web, technology, education, linguistic content, big data architecture, learning setting, e-readiness, linguistic content, STEM, etc., that are directly or indirectly connected. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described according to the research questions. The findings in this article explain that web-based e-learning integrates pedagogy and technology and becomes part of digital multimedia implemented in e-learning, blended learning, and face-to-face that impacts elementary school students and teachers directly or indirectly. Future research needs to explore web-based e-learning in schools that is current, safe, and needed by students and teachers.

Exploring Blockchain Data Analysis and Its Communications Architecture: Achievements, Challenges, and Future Directions: A Review Article

Blockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations.

Big data: "Navigating the Hadoop ecosystem: Unraveling the potential of big data”

Big data refers to extremely large data sets that can be analyzed to reveal patterns, trends, and associations, particularly those relating to hu-man behavior and interactions. This data is too large and complex to be processed using traditional data processing methods, necessitating the use of specialized software and systems to manage and analyze it. This paper discusses about the Big Data Architecture, Hadoop Ecosystem, HDFS, Map reduce, Yarn, Hive, and many other components of Hadoop ecosystem.