Big Data Management Research Articles

Under lock and key: Incorporation of blockchain technology in the field of ophthalmic artificial intelligence for big data management - A perfect match?

Big data has been a game changer of machine learning. But, big data is a form of centralized version of data only available and accessible to the technology giants. A way to decentralize this data and make machine learning accessible to the smaller organizations is via the blockchain technology. This peer-to-peer network creates a common database accessible to those in the network. Furthermore, blockchain helps in securing the digital data and prevents data tampering due to human interactions. This technology keeps a constant track of the document in terms of creation, editing, etc., and makes this information accessible to all. It is a chain of data being distributed across many computers, with a database containing details about each transaction. This record helps in data security and prevents data modification. This technology also helps create big data from multiple sources of small data paving way for creating a well serving artificial intelligence model. Here in this manuscript, we discuss about the usage of blockchain, its current role in machine learning and challenges faced by it.

Financial Big Data Management and Control and Artificial Intelligence Analysis Method Based on Data Mining Technology

Driven by capital and Internet information (IT) technology, the operating scale and capital scale of modern industrial and commercial enterprises and various organizations have increased exponentially. At present, the manual-based financial work model has been unable to adapt to the changing speed of the modern business environment and the business rhythm of enterprises. All kinds of enterprises and organizations, especially large enterprises, urgently need to improve the operational efficiency of financial systems. By enhancing the integrity, timeliness, and synergy of financial information, it improves the comprehensiveness and ability of analyzing complex problems in financial analysis. It can cope with such rapid changes and help improve the financial management capabilities of enterprises. It provides more valuable decision-making guidance for business operations and reduces business risks. In recent years, the vigorous development of artificial intelligence technology has provided a feasible solution to meet the urgent needs of enterprises. Combining data mining, deep learning, image recognition, natural language processing, knowledge graph, human-computer interaction, intelligent decision-making, and other artificial intelligence technologies with IT technology to transform financial processes, it can significantly reduce the processing time of repetitive basic financial processes, reduce the dependence on manual accounting processing, and improve the work efficiency of the financial department. Through the autonomous analysis and decision-making of artificial intelligence, the intelligentization of financial management is realized, and more accurate and effective financial decision-making support is provided for enterprises. This paper studies the company’s intelligent financial reengineering process, so as to provide reference and reference for other enterprises to upgrade similar financial systems. The results of the analysis showed that at the level of α = 0.05 , there was a significant difference in the mean between the two populations. When the r value is in the range of -1 and 1, the linear relationship between the x and y variables is more obvious. This paper proposes decision-making suggestions and risk control early warning to the group decision-making body, or evaluates the financial impact of the group’s decision-making, and opens the road to financial intelligence.

Semantic Segmentation-Based Adaptive Mining Algorithm for Ceramic Cultural Resource Data

Ceramic culture as Chinese culture has a long history and is the Chinese people’s spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches.

Engaging Through Awareness: Purpose-Driven Framework Development to Evaluate and Develop Future Business Strategies With Exponential Technologies Toward Healthcare Democratization.

Industry 4.0 and digital transformation will likely come with an era of changes for most manufacturers and tech industries, and even healthcare delivery will likely be affected. A few trends are already foreseeable such as an increased number of patients, advanced technologies, different health-related business models, increased costs, revised ethics, and regulatory procedures. Moreover, cybersecurity, digital invoices, price transparency, improving patient experience, management of big data, and the need for a revised education are challenges in response to digital transformation. Indeed, forward-looking innovation about exponential technologies and their effect on healthcare is now gaining momentum. Thus, we developed a framework, followed by an online survey, to investigate key areas, analyze and visualize future-oriented developments concerning technologies and innovative business models while attempting to translate visions into a strategy toward healthcare democratization. When forecasting the future of health in a short and long-term perspective, results showed that digital healthcare, data management, electronics, and sensors were the most common predictions, followed by artificial intelligence in clinical diagnostic and in which hospitals and homes would be the places of primary care. Shifting from a reactive to a proactive digital ecosystem, the focus on prevention, quality, and faster care accessibility are the novel value propositions toward democratization and digitalization of patient-centered services. Longevity will translate into increased neurodegenerative, chronic diseases, and mental illnesses, becoming severe issues for a future healthcare setup. Besides, data privacy, big data management, and novel regulatory procedures were considered as potential problems resulting from digital transformation. However, a revised education is needed to address these issues while preparing future health professionals. The “P4 of health”, a novel business model that is outcome-based oriented, awareness and acceptance of technologies to support public health, a different mindset that is proactive and future-oriented, and an interdisciplinary setting to merge clinical and technological advances would be key to a novel healthcare ecosystem. Lastly, based on the developed framework, we aim to conduct regular surveys to capture up-to-date technological trends, sustainable health-related business models, and interdependencies. The engagement of stakeholders through awareness and participation is the key to recognizing and improving healthcare needs and services.

Information Resource Sharing Based on University Dance Big Data Management and Utilization System Design

With the expansion of college enrollment, the number of college students is increasing, and the amount of information to be dealt with in the information management of college students is increasing. In most colleges and universities, managing college student information is done artificially. In this context, how to effectively manage and utilize the dance big data of college students has important research value. Therefore, this article takes the development and realization of student dance under the public service platform system of colleges and universities as the subject and design the university dance big data management and utilization system based on information resource sharing. Due to the high mobility of college students, this work also carries out an information resource sharing strategy to establish the dance big data management and utilization system for universities; after the implementation of the student dance big data management service system, colleges and universities can provide more convenient transfer, management, query, and other services of student files.

Sustainable Insights for Energy Big Data Governance in China: Full Life Cycle Curation from the Ecosystem Perspective

With the development of the Energy Internet and the Internet of Things, diversified social production activities are making the interactions between energy, business, and information flow among physical, social, and information systems increasingly complex. As the carrier of information and the hub between physical and social systems, the effective management of energy big data has attracted the attention of scholars. This work indicates that China’s energy companies have carried out a series of activities that are centered on energy big data collection, as well as development and exchange, and that the energy big data ecosystem has begun to take shape. However, the research on and the application of energy big data are mainly limited to micro-level fields, and the development of energy big data in China remains disordered because the corresponding macro-level instructive governance frameworks are lacking. In this work, to facilitate the sustainable development of the energy big data ecosystem and to solve existing problems, such as the difficult-to-determine governance boundaries and the difficult-to-coordinate interests, and to analyze the structure and mechanism of the energy big data ecosystem, data curation is introduced into energy big data governance, and a paradigm is constructed for sustainable energy big data curation that encompasses its full life cycle, including the planning, integration, application, and maintenance stages. Key paradigmatic issues are analyzed in-depth, including data rights, fusion, security, and transactions.

Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers

Given the increasing complexity of the global supply chain, it is an important issue to enhance the agilities of enterprises that manufacture new energy materials to reduce the ripple effects of supply chains. Quality function deployment (QFD) has been applied in many areas to solve multi-criteria decision making (MCDM) problems successfully. However, there is still lack of sufficient research on the use of MCDM to develop two house-of-quality systems in the supply chain of new energy materials manufacturing enterprises to determine ripple effect factors (REFs), supply chain agility indicators (SCAIs), and industry 4.0 enablers (I4Es). This study aimed to develop a valuable decision framework by integrating MCDM and QFD; using key I4Es to enhance the agility of supply chain and reduce or mitigate its ripple effects ultimately, this study provides an effective method for new energy materials manufacturers to develop supply chains that can rapidly respond to change and uncertainty. The case study considered China’s largest new energy materials manufacturing enterprise as the object and obtained important management insights, as well as practical significance, from implementing the proposed research framework. The study found the following to be the most urgent I4Es required to strengthen the agility of supply chain and reduce the key REFs: ensuring data privacy and security, guarding against legal risks, adopting digital transformation investment to improve economic efficiency, ramming IT infrastructure for big data management, and investing and using the new equipment of Industry 4.0. When these measures are improved, the agility of the supply chain can be improved, such as long-term cooperation with partners to strengthen trust relationships, supply chain information transparency and visualization to quickly respond to customer needs, and improving customer service levels and satisfaction. Finally, REFs, such as the bullwhip effect caused by inaccurate prediction, facility failure, and poor strain capacity caused by supply chain disruption, can be alleviated or eliminated. The proposed framework provides an effective strategy for formulating I4Es to strengthen supply chain agility (SCA) and mitigate ripple effects, as well as provides a reference for supply chain management of other manufacturing enterprises in the field of cleaner production.

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things.

With the continuous development of social economy and the continuous improvement of people's ideological level, the state and social people have given more and more care and assistance to the disadvantaged group of disabled persons. The construction of a public service cloud platform for the disabled is one of the important measures to protect the human rights of the disabled and improve the lives of the disabled. According to the statistics of the China Disabled Persons' Federation, since the twentieth century, with the acceleration of the equalization of basic public services and the establishment of urban and rural social security systems, a practical and effective public service system for the disabled has made leaps and bounds. The construction of the cloud platform for public services for the disabled is an important branch of the construction of the public service system for the disabled. However, due to the late development of the construction of the cloud platform for public services for the disabled in China, it is not perfect, and there are still some problems to be solved. This paper mainly studies the public service cloud platform for the disabled based on the big data management model of the Internet of Things. Based on this research theme, this paper conducts a service response efficiency test for the disabled provided by the disabled public service cloud platform based on the big data management model of the Internet of Things. It concluded that the public service cloud platform for the disabled based on the big data management model of the Internet of Things has improved the response efficiency of providing services for the disabled by 57%.

Digitalization of the supply chain: transformation factors

PurposeThere is an increasing interest in the supply chain’s digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain’s digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.Design/methodology/approachThis study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and abstracts and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.FindingsThe results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.Research limitations/implicationsWhile the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.Practical implicationsThis study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.Originality/valueThe digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.

Realizing Smart City Infrastructure at Scale, in the Wild: A Case Study

The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.

A Comparative Study of MongoDB and Document-Based MySQL for Big Data Application Data Management

In the context of the heavy demands of Big Data, software developers have also begun to consider NoSQL data storage solutions. One of the important criteria when choosing a NoSQL database for an application is its performance in terms of speed of data accessing and processing, including response times to the most important CRUD operations (CREATE, READ, UPDATE, DELETE). In this paper, the behavior of two of the major document-based NoSQL databases, MongoDB and document-based MySQL, was analyzed in terms of the complexity and performance of CRUD operations, especially in query operations. The main objective of the paper is to make a comparative analysis of the impact that each specific database has on application performance when realizing CRUD requests. To perform this analysis, a case-study application was developed using the two document-based MongoDB and MySQL databases, which aim to model and streamline the activity of service providers that use a lot of data. The results obtained demonstrate the performance of both databases for different volumes of data; based on these, a detailed analysis and several conclusions were presented to support a decision for choosing an appropriate solution that could be used in a big-data application.

Multiple Stressors in Vegetable Production: Insights for Trait-Based Crop Improvement in Cucurbits.

Vegetable production is a key determinant of contribution from the agricultural sector toward national Gross Domestic Product in a country like India, the second largest producer of fresh vegetables in the world. This calls for a careful scrutiny of the threats to vegetable farming in the event of climate extremes, environmental degradation and incidence of plant pests/diseases. Cucurbits are a vast group of vegetables grown almost throughout the world, which contribute to the daily diet on a global scale. Increasing food supply to cater to the ever-increasing world population, calls for intensive, off-season and year-round cultivation of cucurbits. Current situation predisposes these crops to a multitude of stressors, often simultaneously, under field conditions. This scenario warrants a systematic understanding of the different stress specific traits/mechanisms/pathways and their crosstalk that have been examined in cucurbits and identification of gaps and formulation of perspectives on prospective research directions. The careful dissection of plant responses under specific production environments will help in trait identification for genotype selection, germplasm screens to identify superior donors or for direct genetic manipulation by modern tools for crop improvement. Cucurbits exhibit a wide range of acclimatory responses to both biotic and abiotic stresses, among which a few like morphological characters like waxiness of cuticle; primary and secondary metabolic adjustments; membrane thermostability, osmoregulation and, protein and reactive oxygen species homeostasis and turnover contributing to cellular tolerance, appear to be common and involved in cross talk under combinatorial stress exposures. This is assumed to have profound influence in triggering system level acclimation responses that safeguard growth and metabolism. The possible strategies attempted such as grafting initiatives, molecular breeding, novel genetic manipulation avenues like gene editing and ameliorative stress mitigation approaches, have paved way to unravel the prospects for combined stress tolerance. The advent of next generation sequencing technologies and big data management of the omics output generated have added to the mettle of such emanated concepts and ideas. In this review, we attempt to compile the progress made in deciphering the biotic and abiotic stress responses of cucurbits and their associated traits, both individually and in combination.

Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things

The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.

ModelOps for enhanced decision-making and governance in emergency control rooms

For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation as a disaster unfolds is critical to saving lives, infrastructure and the environment. Incident control-room operators manage complex information and systems, especially with the emergence of Big Data. They are increasingly making decisions supported by artificial intelligence (AI) and machine learning (ML) tools for data analysis, prediction and decision-making. As the volume, speed and complexity of information increases due to more frequent fire events, greater availability of myriad IoT sensors, smart devices, satellite data and burgeoning use of social media, the advances in AI and ML that help to manage Big Data and support decision-making are increasingly perceived as “Black Box”. This paper aims to scope the requirements for bushfire EMS to improve Big Data management and governance of AI/ML. An analysis of ModelOps technology, used increasingly in the commercial sector, is undertaken to determine what components might be fit-for-purpose. The result is a novel set of ModelOps features, EMS requirements and an EMS-ModelOps framework that resolves more than 75% of issues whilst being sufficiently generic to apply to other types of mission-critical applications.

MODERN ECONOMIC AND ORGANIZATIONAL IMPERATIVES OF CONSTRUCTION DEVELOPMENT IN PROJECTS FOR THE ADMINISTRATION OF STRATEGIC CHANGES IN ENTERPRISES

The article is devoted to the study of modern concepts that influence the cognitive mechanisms of project management in construction. The conducted analysis proved that obtaining a greater effect from the use of the knowledge of the participants in the implementation of the construction project is possible under the condition of the joint implementation of three modern concepts: enterprise integration, information modeling in construction and big data management. In addition to an effectively working communication system within the project, an important condition is the availability of effective knowledge management tools. Knowledge management tools are technologies used to improve and ensure the implementation of knowledge management sub-processes. Information modeling technologies in construction can become an important tool for analyzing big data, generating information and knowledge, because after the completion of each stage of the implementation of a construction project, large data sets remain. Accumulated data can be a corporate asset, the acquisition and use of which allows you to make better forecasts and make the right management decisions. In addition, the knowledge that arose in the project and was tested by practice can be considered more reliable in comparison with data from experiments or simulations, because they contain more fundamental knowledge about reality. The developed methods and models allow: solving the task of choosing the optimal network organizational structure based on the criterion of maximum efficiency in the use of information connections between project participants; carry out an assessment of the effect of the joint introduction of information modeling in construction and the integrated implementation of the construction project; to carry out a comprehensive study and optimization of the information and communication network of the participants in the implementation of the construction project; start the process of introducing information modeling in public procurement in the construction industry of Ukraine. The obtained results can be used by scientific institutions, enterprises and organizations - key participants in the implementation of construction projects, authorities at various levels, and also form the basis for further theoretical, methodological and applied research.

Facilitating big-data management in modern business and organizations using cloud computing: a comprehensive study

AbstractModern digital life has produced big data in modern businesses and organizations. To derive information for decision-making from these enormous data sets, a lot of work is required at several levels. The storage, transmission, processing, mining, and serving of big data create problems for digital domains. Despite several efforts to implement big data in businesses, basic issues with big data remain (particularly big-data management (BDM)). Cloud computing, for example, provides companies with well-suited, cost-effective, and consistent on-demand services for big data and analytics. This paper introduces the modern systems for organizational BDM. This article analyzes the latest research to manage organization-generated data using cloud computing. The findings revealed several benefits in integrating big data and cloud computing, the most notable of which is increased company efficiency and improved international trade. This study also highlighted some hazards in the sophisticated computing environment. Cloud computing has the potential to improve corporate management and accountants' jobs significantly. This article's major contribution is to discuss the demands, advantages, and problems of using big data and cloud computing in contemporary businesses and institutions.

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

BackgroundNowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy.ObjectiveThis study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases.MethodsThe relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data.ResultsThe data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%.ConclusionsThe main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

Development of DNA molecular ID card in hog millet germplasm based on high motif SSR

<p id="C3">In order to identify hog millet (<italic>Panicum miliaceum</italic>) germplasm rapidly, to establish a big data management platform, to provide a theoretical basis for germplasm identification and traceability management, 130 germplasms from four ecological cultivation areas were used as materials to construct a molecular ID based on 35 high-motif SSRs (21, 10, and 4 with four-, five-, and six-nucleotide repeats, respectively). The results showed that 30 out of the 35 SSRs could be used as core markers for the construction of molecular ID cards. Ninety allelic variants were detected; effective allelic variants (<italic>Ne</italic>) ranged from 2.3186 to 2.9982 with an average of 2.7607; Shannon diversity index (<italic>I</italic>) ranged from 0.9158 to 1.0873 with an average of 1.0472. The observed heterozygosity (<italic>Ho</italic>) was 0.5000-0.8678 with a mean of 0.7168; the expected observed heterozygosity (<italic>He</italic>) was 0.5710-0.6691 with a mean of 0.6386; the Nei’s gene diversity index (<italic>Nei</italic>) was 0.5687-0.6665 with a mean of 0.6360; the polymorphism information content (PIC) was 0.5151-0.7898, with a mean of 0.6966. All accessions were divided into three groups (Group I, II, and III) according to UPGMA analysis. In terms of Shanxi accessions, landraces and breeding varieties were classified into Group I and III, respectively, and farmers materials were distributed into three groups. Based on PCA analysis, all accessions were classified into four clusters, which were related to their geographical origin. As for the rule that the most germplasms were determined using the least markers, 3 markers were excluded due to their high similarity coefficient with others, namely RYW23, RYW49, and RYW51. Screening the remaining 27 markers, the combinations of 17 SSR (RYW35, RYW40, RYW37, RYW18, RYW30, RYW16, RYW20, RYW19, RYW8, RYW5, RYW3, RYW7, RYW1, RYW14, RYW9, RYW6, and RYW10) could identify all hog millet accessions. The DNA molecular identifications of character strings, bar code, and quick response (QR) codes were constructed via ID analysis 4.0, software online of bar code and QR codes technique (<uri>http://barcode.cnaidc.com/app/html/bcgcode128.php</uri> and <uri>https://cli.im/</uri>).

MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain

The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed.

An Analysis on the Way of Improving the Effectiveness of College Students' Party History Education in the Era of Big Data

The arrival of the era of big data has promoted the new reform and development of various social industries, especially providing opportunities and challenges to improve the effectiveness of party history education in college students. At present, there are major problems in the party history education of college students, such as weak awareness of big data application, lack of big data application ability, imperfect party history education management system and the need to expand the party history education data platform to be expanded. Because of the problem, the corresponding countermeasures are put forward from establishing the concept of big data, building high-quality big data teachers, improving the big data management system of party history education for college students, and actively expanding the party history education big data platform.