Big Data Collection Research Articles

빅데이터 분석을 통한 학생선수에 관한 개념과 속성 탐색

The purpose of this study was to verify the concept and perception of student athlete through social media big data anlaysis, and to suggest value and direction of policy and system for student athletes. The data collection period was from January 1, 2017 to June 30, 2022, focusing on the keyword of “student athlete” for unfiltered and unstructured text data appearing on Naver, Daum, Google, and YouTube. Textom, a big data collection and analysis solution, was used for data collection. For data analysis, frequency analysis and TF-IDF (Term Frequency Inverse Document Frequency) analysis of text mining analysis were performed, and connection centrality and CONCOR (CONvergence of iterated CORrelations) analysis of social network analysis were performed using Textom and UCINET 6.0 program. The results of this study were as follows. First, the total amount of textual data collected through social media was 5,424 cases (2,743KB). Second, as a result of frequency and TF-IDF analysis, ‘athlete’, ‘student’, ‘student athlete’, ‘competitions’, ‘sports’ had the most in common. Third, As a result of the connection centrality analysis, centrality measures were shown in the order of athletes (374.220), students (288.441), student athletes (175.441), competitions (120.373), and exercise (99.949). Lastly, as a result of CONCOR analysis, it was categorized into seven clusters: ‘player training system’, ‘academic parallelism’, ‘identity’, ‘policy support’, ‘career / college-preparatory’, ‘research activities’, and ‘social issues’. Through these results of such big data analysis, it is possible to provide the academic basic data necessary for proposing policies and strategic directions and improvement measures necessary for the growth of student athletes.

Research on the Indicators of Sustainable Campus Renewal and Reconstruction in Pursuit of Continuous Historical and Regional Context

As cities transition from incremental development to stock development, university campuses in suburban areas are progressively becoming urban university campuses. The stability of the boundary between urban university campuses and the city, along with the fact that the campus’s overall spatial capacity is reaching its maximum, makes it impossible for urban university campuses to have future spatial expansions. This article focused on the stock development, renewal, and transformation of urban campuses. From the perspective of urban university campus block morphology hierarchy and using the Wushan Campus of South China University of Technology in Guangzhou as an example, this study utilized urban morphology theory, data mining technology, big data collection, and visualization techniques to measure campus block morphology. Then, K-means clustering was utilized to classify the block form, and historical background research was employed to study the many forms of typical block form. Finally, the campus renewal and transformation guiding principles were introduced, and the control index of block form renewal and transformation was formed, evolving into the university campus block form renewal and transformation design technique. This strategy was used to investigate the general revitalization of college campuses.

The use of big data in interdisciplinary research on example of the Greater Mediterranean macroregion

Big data collection and analysis technologies are being intensively introduced into the social, economic and political spheres in countries and macroregions, including the Mediterranean region. This fact actualises the prospects of interdisciplinary research at the intersection of sociology, political science and regional studies. The article examines the benefits and risks of using these technologies in listed areas, taking Spain and the Middle East as examples. Among the main problems and threats, the authors consider the following: personal data confidentiality breach, and increased data manipulation for political purposes, the emergence of illegitimate forms of citizens self-organisation, the transition of Internet users to anonymous accounts due to the practice of “privatization” of personal data by non-state and terrorist groups in regional spaces, preventing the qualitative collection of data. The need to solve these problems at the international level was noted. The range of possibilities offered by working with big data to provide pragmatic and effective socio-political forecasting and operational regional studies is shown.

FDup: a framework for general-purpose and efficient entity deduplication of record collections.

Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.

Construction of Online English Corpus Based on Web Crawler Technology

Using 100 random questionnaires, 89 people said they were increasingly dependent on the Internet for information, while the remaining 11 said they were not dependent much. Nowadays, the development of Internet technology is more and more mature, and the scale of the Internet is more and more large. At the same time, with the gradual deepening of global economic integration, English has become one of the indispensable language methods for international communication and cooperation. The development of network technology has been applied more and more widely in the process of English teaching; especially, the construction, research, and practical application of corpus have ushered in a broad development prospect. Based on web crawler technology, this paper focuses on the construction of web English corpus, which lays a foundation for English learning. Experiments show that crawler technology can effectively solve the collection and recognition of big data in English corpus.

Industry 4.0 and intelligent predictive maintenance: a survey about the advantages and constraints in the Italian context

PurposeIn the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.Design/methodology/approachA survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.FindingsResults show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.Originality/valueThe article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.

Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big Data

The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.

Big data from customers and non-customers through crowdsourcing, citizen science and crowdfunding

Purpose The unprecedented growth in the volume, variety and velocity with which data is generated and collected over the last decade has led to the spread of big data phenomenon. Organizations have become increasingly involved in the collection and analysis of big data to improve their performance. Whereas the focus thus far has mainly been on big data collected from customers, the topic of how to collect data also from those who are not yet customers has been overlooked. A growing means of interacting with non-customers is through crowd-based phenomena, which are therefore examined in this study as a way to further collect big data. Therefore, this study aims to demonstrate the importance of jointly considering these phenomena under the proposed framework. Design/methodology/approach This study seeks to demonstrate that organizations can collect big data from a crowd of customers and non-customers through crowd-based phenomena such as crowdsourcing, citizen science and crowdfunding. The conceptual analysis conducted in this study produced an integrated framework through which companies can improve their performance. Findings Grounded in the resource-based view, this paper argues that non-customers can constitute a valuable resource insofar as they can be an additional source of big data when participating in crowd-based phenomena. Companies can, in this way, further improve their performance. Originality/value This study advances scientific knowledge of big data and crowd-based phenomena by providing an overview of how they can be jointly applied to further benefit organizations. Moreover, the framework posited in this study is an endeavour to stimulate further analyses of these topics and provide initial suggestions on how organizations can jointly leverage crowd-based phenomena and big data.

IT and Strategic Human Capital: Impact on Knowledge-Based Processes, Networks, and Outcomes

Advancements in information technology (IT) can have profound impacts on the development and management of human capital. Extant research has examined how human capital in markets and organizations are shaped by increased digitization and the deployment of strategies relying on collection and analysis of big data, cloud computing, social media, Internet of Things (IoT), and the like. In knowledge-intensive settings, however, IT-enabled mechanisms have heterogenous impacts on issues related to human capital. Although IT potentially expands capacity for collection, storage, and utilization of knowledge, some firms and individuals successfully leverage technology and information systems while others do not--or are even possibly left worse off. This symposium contributes to examining why such discrepancies may exist by shedding light on the market and organizational processes that underlie the intersection of IT and knowledge-based processes, networks, and outcomes as they relate to issues in strategic human capital. Can Social Media Alleviate Inequality? Evidence from Venture Capital Financing Presenter: Gavin Wang; Wharton OPIM Presenter: Lynn Wu; The Wharton School, U. of Pennsylvania Presenter: Lorin Hitt; U. of Pennsylvania Are Knowledge Sharing and Learning Tradeoffs? Linking Performance Incentives with KMS Usage Presenter: Sae-Seul Park; Carnegie Mellon U. - Tepper School of Business Cloud Adoption and Strategic Human Capital: Evidence from Norway Presenter: Derrick Choe; - Presenter: Amir Sasson; BI Norwegian Business School Presenter: Robert Channing Seamans; NYU Stern Hunting For Talent: Firm-Driven Labor Market Search in the United States Presenter: Ines Black; - Presenter: Sharique Hasan; Fuqua School of Business, Duke U.

State of Art Survey on Plant Leaf Disease Detection

Benefits of independent learning and extraction of features have received a lot of attention in recent years from both academic and professional circles. A subcategory of artificial intelligence is deep learning. The use of deep learning towards plant disease recognition can prevent the drawbacks associated with crop disease and production losses. In order to identify and characterize the signs of plant diseases, numerous established machine learning and deep learning architectures are used in conjunction with a number of visualization tools. The detection of leaf disease using image processing has been covered in this survey. Leaf disease diagnosis is enhanced when image segmentation is used in combination with deep learning or machine learning models. A big data collection can be segmented with the use of image segmentation, and the output is then fed to the AI algorithms on disease detection. Additionally, this survey covers the performance metrics of prior studies, which offered guidance for future advancements in plant disease detection and prevention methods.

A Brief Survey on Recent Advances in Cloud Control Systems

Networked control systems (NCSs) have played a key role in Internet of Things (IoTs) to realize the vision of a fully interactive and responsive intelligent environment. However, traditional NCSs cannot address the challenges of big data collection, storage and analysis brought by large-scale IoTs applications. Thanks to the rapid development of cloud computing and control theory, a new paradigm of NCSs named cloud control systems (CCSs) has been proposed to address the challenges. Since proposed in 2012, it has drawn wide attentions from industry and academia. In this brief, we survey the recent advances of CCSs. Particularly, we present a taxonomy of existing results for CCSs, which include cloud control perspectives of traditional control techniques, security and privacy of cloud-based control, and CCSs for industry automation. We discuss each category in depth by comparing and contrasting different approaches subsequently. Finally, some future directions of how to design more efficient and practical CCSs have been discussed.

Methodology for determining passengers correspondence by public transport from electronic tickets validation operations

Introduction. Current methods for determining passenger ridership, due to their complexity and limited efficiency, do not allow monitoring transport demand at the proper level (in terms of comprehensiveness and continuity). Nowadays, technologies based on the collection, integration and analysis of big data (Urban computing, Big data, Internet of things) are used to create effective solutions in many aspects of our lives (including for urban transit).Materials and methods. Within the framework of this approach, a methodology for determining the correspondence of transit passengers from the operations of validating any kind of electronic travel tickets: smart cards, transport cards, magnetic cards has been developed. Each operations details are recorded in the Automated Fare Collection (AFC) system during the validation.This methodology based on the definition and evaluation the set of acceptable options of passenger trips sequences, which form the passenger correspondence, taking into account many factors that effect on the route choice by a passenger. For example, in contrast to previous studies, the practice of paying for trip at any point on the route, not necessarily immediately after boarding the vehicle, was taken into account.Results. It was proved that passenger correspondence calculated using the developed methodology statistically corresponds to the general set of transit passenger ridership within acceptable errors. The chaacteristics of transit demand is provided in the results.Discussion and conclusion. The application of the developed methodology makes it possible to organize continuous monitoring of passenger flows, technical and operational indicators of the functioning of transit system and thus implement the concept of sustainable development of public transport by designing public transit supply that meets demand.

Design of Information Visualizations in the Internet of Nano-Things Air Quality Systems

Today's age is characterized by large amount of information that surrounds us in which visual information plays a significant role. Nanosensor air quality measurement systems that use Internet of Nano-Things technology enable the collection of big data. Ease of display and storage of information that the user can easily interpret are imperative in designing a visual interface. Only a good combination of visual elements complemented by data and map display will contribute to the clarity of the processed data. This paper will give an overview of the factors that affect the excellence of the transmission of visual information. Ways of presenting visualizations of air quality data measured by IoNT systems will be discussed through descriptive and empirical analysis of visualizations. A special emphasis is on the review of existing practices and principles, and the possibilities of visual presentation of information in this area will be explained through the discussion.

딥러닝 기반의 객체 탐지 모델을 활용한 과수 생육 단계 판별 시스템

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

조직 및 인사 연구에서 빅데이터 분석 방법론 활용

The purpose of this study is to introduce big data analysis methodologies that have recently received attention from academia to practitioners. We first summarized the research trends by reviewing previous studies using big data and analysis techniques. By the end of 2021, 39 empirical studies published in 13 overseas journals and 3 domestic journals were reviewed to examine the research trends. In addition, the concepts, principles, and utilizations were summarized by selecting four methods frequently borrowed from studies conducted with big data and machine learning techniques. First, the types of big data and data collection methods for research purposes were presented. It was divided into archival data and organic data, and examined how previous studies generated large amounts of data. Second, it is a method of maximizing predictive power with structured data. Types of supervised learning, unsupervised learning, and reinforcement learning were explained, and procedures for applying supervised learning mainly used in previous studies were presented in seven stages. Third, it is topic modeling that analyzes unstructured test data. Since it appears particularly frequently in overseas academic journals, the principles and advantages of this technique were highlighted and presented. Fourth, it is explained how to analyze and use videos and images. Three analysis solutions mainly used in previous studies were summarized. The theoretical contribution is that it examined the usefulness of big data and machine learning techniques in the areas of organization and management and proposed a method of using them. Furthermore, there is a practical contribution to providing utility to practitioners by enhancing their understanding of the four techniques mainly used in the organization.

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.

Transportation data visualization with a focus on freight: a literature review

ABSTRACT Road-based freight movements are a critical component of the supply chain and transportation networks. With the drive to invest in big data collection capabilities, most of the collected freight movement data remain underutilized. To improve the efficiency and resiliency of the supply chain, it is essential to enhance the visibility of goods movements on road networks. To this end, we provide in this paper a comprehensive literature review on this topic and analyze the previous research from different perspectives, such as data levels of abstraction and existing visualization techniques. In addition, we provide a taxonomy of freight transportation visualization according to the underlying analytic objective. Furthermore, we propose a decision support tool to aid freight data analysts in selecting the right visualization tools. Finally, we identify research gaps in the field of freight transportation visualization.

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.

Neural Network-Based Dynamic Segmentation and Weighted Integrated Matching of Cross-Media Piano Performance Audio Recognition and Retrieval Algorithm.

This paper presents a dynamic segmentation and weighted comprehensive matching algorithm based on neural networks for cross-media piano performance audio recognition and retrieval. The 3D convolutional neural network process is separated to compress the network parameters and improve the computational speed. Skip connection and layer-wise learning rate solve the problem that the separated network is challenging to train. The piano performance audio recognition is facilitated by shuffle operation. In pattern recognition, music retrieval algorithms are gaining more and more attention due to their ease of implementation and efficiency. However, the problems of imprecise dynamic note segmentation and inconsistent matching templates directly affect the accuracy of the MIR algorithm. We propose a dynamic threshold-based segmentation and weighted comprehensive matching algorithm to solve these problems. The amplitude difference step is dynamically set, and the notes are segmented according to the changing threshold to improve the accuracy of note segmentation. A standard score frequency is used to transform the pitch template to achieve input normalization to enhance the accuracy of matching. Direct matching and DTW matching are fused to improve the adaptability and robustness of the algorithm. Finally, the effectiveness of the method is experimentally demonstrated. This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and storage module of cross-media piano performance big data, the building module of audio recognition of cross-media piano performance big data, and the dynamic precision module of cross-media piano performance big data.

The de-realization of Black bodies in an era of mass digital surveillance: A techno-criminological critique

This article describes the ways in which existing methods of dataveillance and big data collection have contributed to the current de-realization of Black bodies. In the present or ultramodern era, de-realization consists of datafication (i.e. digital profiling techniques and life mining strategies) in support of techno-crime control policy. The process of de-realization both de-politicizes Black identities and de-personalizes the lived experience of Blackness. In order to make explicit our thesis, section one proposes a techno-criminological theory of de-realization. The theory explains how the racialized construction of surveillance in the current age is mediated by the algorithmic logic of pre-crime and the asymmetric rationale of post-criminology. In order to situate our overall theorizing, section two explains how Black bodies have historically been the subject of excessive and invasive forms of de-realization. This history includes slavery and visceral forms of de-realization (e.g. the technologies of branding), as well as political opposition to Civil Rights and volatile forms of de-realization (e.g. the technologies of suspicion). In the present era, the de-realization of Black bodies consists of the mass digital surveillance of social movements (i.e. bodies of activist social change), including Black Lives Matter (BLM), that are policed through the technologies of information analytics. Section three speculates on the criminological fall-out stemming from present day manifestations of de-realization. This speculation emphasizes how history, theory, and culture are relevant to historicizing the administration of injustice in the ultramodern age of digital reality construction.