Data Analysis System Research Articles

Extraction and analysis algorithms for Sanxingdui cultural relics based on hyperspectral imaging

The Sanxingdui Ruins site, situated in Sichuan Province, China, constitutes a significant component of the ancient Shu culture. Confronted with the challenge of excavating and analyzing cultural relics, this study introduces hyperspectral methods for the first time, utilizing the cultural relics unearthed from sacrificial pits. It proposes a novel algorithm for the extraction and analysis of Sanxingdui cultural relics, while also establishing a comprehensive data acquisition and analysis system. The specific innovations of this research are as follows: 1) Acquiring spectral curves of representative materials such as ivory, jade, bronze, and stone pillars, and constructing a spectral database. 2) Introducing the Dual-encoder UNet (D-Unet) model from a spatial perspective to achieve preliminary extraction of cultural relics. 3) Proposing an unsupervised Fuzzy C-means (FCM) algorithm from a spectral perspective to analyze the material properties of cultural relics. Experimental results demonstrate that the proposed algorithm successfully achieves the extraction and analysis of Sanxingdui cultural relics. Ablation experiments further reveal that the algorithm yields excellent results in scenarios involving exposed cultural relics, semi-exposed cultural relics on the ground, and typical cultural relics. Moreover, the algorithm can be extended to other aspects of archeological excavation and analysis. During the experiment, it was observed that: 1) The cultural relics' surfaces contain distinctive substances, tentatively identified as proteins, suggesting the presence of textile capabilities in the ancient Shu civilization. 2) An unearthed bronze mask exhibits structural asymmetry, contrasting with other highly refined artifacts. Through intelligent synthesis technology analysis, a totem-like target was discovered at the center of the eyebrow, potentially associated with the veneration of eyes in Sanxingdui cultural relics, thereby providing additional validation of the algorithms effectiveness.

Research on Guangdong Province Meteorological Data Analysis System under Hadoop Environment

In order to better monitor and manage the real-time transmission of meteorological information in Guangdong Province and improve communication quality, the Guangdong Meteorological Information Center has organized the development of the Guangdong Meteorological Data Analysis System. The system has the characteristics of intuitive and easy to use, strong real-time performance, and convenient query and statistics, which better meets the real-time monitoring and time management requirements of meteorological information transmission in Guangdong Province. Hadoop is a popular big data computing oriented, open-source big data processing system. Its characteristics of high throughput, high fault tolerance and easy expansion make it widely used in all walks of life. Therefore, this article focuses on the characteristics of real-time reception of meteorological information in Guangdong Province, and introduces the development ideas, use of some new technologies, and page implementation of the Guangdong Meteorological Data Analysis System under Hadoop environment. In the Hadoop environment, it is possible to dynamically exchange data with the server without refreshing the entire page, improving the efficiency of data transmission between the server and client, making browsing more intuitive and convenient. Enriched the methods and capabilities of data processing.

Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study

BackgroundElectronic medical records (EMRs) contain a wealth of information related to breast cancer diagnosis and treatment. Extracting relevant features from these medical records and constructing a knowledge graph can significantly contribute to an efficient data analysis and decision support system for breast cancer diagnosis.MethodsAn approach was proposed to develop a workflow for effectively extracting breast cancer-related features from Chinese breast cancer mammography reports and constructing a knowledge graph for breast cancer diagnosis. Firstly, the concept layer of the knowledge graph for breast cancer diagnosis was constructed based on breast cancer diagnosis and treatment guidelines, along with insights from clinical experts. .Next, a BiLSTM-Highway-CRF model was designed to extract the mammography features, which formed the data layer of the knowledge graph. Finally, the knowledge graph was constructed by combining the concept layer and the data layer in a Neo4j graph data platform, and then applied in visualization analysis, semantic query and computer assisted diagnosis.ResultsMammographic features were extracted from a total of 1171 mammography examination reports. The overall extraction performance of the model achieved an accuracy rate of 97.16%, a recall rate of 98.06%, and a F1 score of 97.61%. Additionally, 47,660 relationships between entities were identified based on the four different types of relationships defined in the concept layer. The knowledge graph for breast cancer diagnosis was constructed after inputting mammographic features and relationships into the Neo4j graph data platform. The model was assessed from the concept layer, data layer, and application layer perspectives, and showed promising results.ConclusionsThe proposed workflow is applicable for constructing knowledge graphs for breast cancer diagnosis based on Chinese EMRs. This study serves as a reference for the rapid design, construction, and application of knowledge graphs for diagnosis and treatment of other diseases. Furthermore, it offers a potential solution to address the issues of limited data sharing and format inconsistencies present in Chinese EMR data.

A Microservices-based Framework for Scalable Data Analysis in Agriculture with IoT Integration

We propose a microservices-based framework for scalable data analysis in agriculture with IoT integration, leveraging the flexibility and modularity of microservices architecture to build a highly adaptable, maintainable, and efficient data analysis system. This framework allows for faster data processing and carry a diversity of agricultural data analysis tasks while maintaining scalability and fault tolerance. Despite the potential benefits, several challenges and obstacles need to be addressed, such as data integration and standardization, the development of agricultural-specific analytical microservices, and ensuring data security and privacy. Practical application and real-world validation are required to assess the impact of the proposed framework on the agricultural sector and inform future research directions.

민간조사제도에 대한 빅데이터 분석 연구

In this study, we would like to explore how the media means the private investigation system through “Big Cains,” a news big data analysis site. Therefore, it provided a big data system for the media and collected data from various media organizations to analyze media reports on private investigations through BigKinds, a news big data analysis system created as news content. In order to achieve the purpose of the research, the flow of keywords was identified by dividing them into 2000s, 2010s and 2020s and deriving and presenting the number of related articles every year during that period. As a result of this study, it was found that in the 2000s, the frequency of media reporting was low at a time when public interest in the private investigation system and various studies were not conducted in academia. Second, in the 2010s, there was a high topic rank on the private survey system compared to the 2000s, and various research and education were conducted in academia and industry related to government policies and private surveys. Third, the 2020s marked a high topic rank as related laws were revised, heralding a new change in the private investigation system. Therefore, it is hoped that the government will move in a direction that will greatly help the people's security demand by supplementing the protection of citizens' rights, minimizing conflicts between direct translations, and limitations of national security.

Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology

Data mining (DM) is an in-depth approach to data analysis by mining useful information from large amounts of data, and this technique is now being used in an increasing number of fields. In this paper, the authors present the design of a real-time data analysis system for sports training based on DM technology and use the corresponding mining tools of DM technology to discover relevant patterns or laws hidden in the data. Therefore, using the real-time data analysis system for sports training based on DM technology, useful information and patterns for improving examination performance can be obtained, which can improve targeted teaching methods and help students overcome learning difficulties, providing rational teaching, synchronizing courses, establishing preparation, effectively guiding students in course selection, and improving course quality and educational effectiveness.

Pengaruh Struktur Modal, Profitabilitas dan Pertumbuhan Perusahaan terhadap Nilai Perusahaan

The purpose of the exploration is to see whether or not the capital structure, profitability, and growth of the company have an influence on the value of the company in the transportation sector listed on the IDX in 2020 – 2022. The independent variables used in this exploration are Capital Structure (DER), Profitability (ROA), and Company Growth (Net Revenue Growth), while the dependent variable is Company Value (PBV). This exploration was conducted using a quantitative system using secondary data. The population data in this exploration uses 54 transportation subsector companies listed on the Indonesia Stock Exchange in 2020 – 2022. The exploration sample used intentional slice with destined criteria to gain 18 companies fiscal statement data. The data analysis system used is multiple direct retrogression analysis through the SPSS program. The independent variable used in this exploration only has a 9 impact on the value of the company. The results showed that profitability has a positive effect on the value of the company, while the capital structure and growth of the company don't affect its value. thus, it's recommended that farther exploration be carried out to consider other independent variables that affect the value of the company, similar as the size of the company, because the size of the company is a pivotal fiscal index for a company.

Approaches to the design of a software system for monitoring and analyzing data of social networks, adapted to the specificity of the kazakh and russian languages

This article is a description of the process of designing a software system for data analysis and monitoring of social networks, adapted to the specifics of the Kazakh and Russian languages. Also, a review of existing systems used by ordinary users, government agencies, commercial organizations, as well as scientific organizations, and designed for the analysis and monitoring of social networks, was carried out.

Fragmented Securities Regulation, Information-Processing Costs, and Insider Trading

Using a unique setting where stand-alone banks submit filings to bank regulators instead of the U.S. Securities and Exchange Commission (SEC), we examine the consequences of fragmented securities regulation for information-processing costs and opportunistic insider trading. We find the market reaction to insider-trading filings on FDICconnect is less timely than to those on SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, suggesting FDICconnect generates higher information-processing costs. We also find only large investors trade more on insider-trading filings on FDICconnect than on insider-trading filings on SEC EDGAR, thus extracting benefits from the delayed market reaction to insider-trading filings on FDICconnect. Finally, we find increased insider selling in stand-alone banks prior to negative earnings news, suggesting insiders’ opportunistic use of private information. These findings collectively suggest regulatory fragmentation undermines market efficiency and distorts the level playing field. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the Eugene Lang Junior Faculty Fellowship. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4903 .

Demonstrating Retrieval of Financial Information in XBRL

The Securities and Exchange Commission (SEC) has upgraded the existing system of Electronic Data Gathering, Analysis, and Retrieval (EDGAR), to Interactive Data Electronic Applications (IDEA) platform, using eXtensible Business Reporting Language (XBRL). In January 2009, the SEC issued its final mandate for XBRL adoption and the conversion target dates for all firms. This conversion enables users to retrieve listed companies’ financial statement information at two levels, document and the data element, compared to the document level alone under the existing system of EDGAR. Beneficial to all users, this change is particularly important to resource-strapped entities such as small businesses, and universities. The result is user friendly for students researching information. With simply the industry standard tool of Microsoft Excel, without expensive proprietary XBRL modules, detailed information is now available, quickly, efficiently and at minimum cost. This paper explains the basic concept of XBRL and demonstrates the ease of the retrieval process in Microsoft Excel.

Utilizing Machine Learning and Big Data Analysis for Risk Mitigation and Fraud Detection in Finance

With the rise of online banking systems and easy transactions, there is an increase in fraud in the banking system and in the field of finance. To reduce fraud in the transactions we can apply the systems of machine learning algorithms and big data analysis. In this research paper, we discuss various methods used in the field such as Supervised learning, Unsupervised learning, and Ensemble Methods in the field of machine learning and transaction monitoring, behavior analytics, network analytics, and pattern recognition in the field of real-time monitoring. We have used a data set from Kaggle on credit card transactions and the methods of Random Forest Classification and Support Vector Machine which comes under the supervised learning method in machine learning and discussed other results and benefits achieved from it.

Development of Marketplace Product Data Mining and Analysis Application as a Reference in Running a Business

<p><strong>Marketplaces has become the first choice as a third party that provides online selling and payment services that bridge sellers and buyers. The increasing need to shop in an efficient and convenient way has driven the popularity of using the marketplace. Moreover, with a variety of products and a fast transaction process, and more competitive prices, the use of the marketplace has shown significant growth in activity by the wider community. </strong><strong>With the increasing use of marketplaces, data analysis from marketplaces is becoming a key element to understand market trends and make informed transaction decisions. Data analysis systems based on bot technology and Web-Scrapping libraries, such as Puppeteer, allow users to perform data analysis more efficiently and quickly, minimize the need for a data analysis team, and reduce time and costs. Given these factors, this research focuses on developing a web-based application that can analyze data coming from marketplaces and present it to users quickly and accurately.</strong><strong></strong></p>

Distributed intelligence for pervasive optical network telemetry

Optical network automation and failure management require measuring the status and the performance of the different network devices to anticipate any degradation and ensure the quality of the provided services, i.e., optical connectivity. Such pervasive network telemetry entails collecting large amounts of measurements and events from different sources and with very fine granularity, which given the amount and variety of telemetry sources and the size of each measurement and event, imposes requirements that are hard to achieve without large investments. In this paper, we analyze the main limitations of telemetry architectures relying exclusively on centralized systems for data analysis and propose an architecture with distributed intelligence. Data aggregation techniques, especially conceived for optical network telemetry, are presented with the objective of reducing data dimensionality. Illustrative results from our experimental telemetry system reveal a reduction of 3 orders of magnitude in terms of total data volume without introducing significant error and processing delay and, more importantly, helping network automation algorithms to identify meaningful changes in the network status.

Approach strategies and application of metabolomics to biotechnology in plants.

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Pre-Evaluating Efficiency Analysis of Mergers and Acquisitions of Full-Service Carriers in Korea

In November 2020, Korean Air signs an agreement to acquire and merges with 63.88% of Asiana Airlines’ shares, which is conditionally approved by the Korea Fair Trade Commission to address exclusivity concerns. The conditions require both airlines to return certain take-off or landing positions and revise their licenses for 26 international and 8 domestic routes within 10 years. This paper collects passenger traffic data from 2009 to 2019 using Korean data analysis, retrieval, and transfer systems employed by both airlines. Data envelopment analysis is utilized to assess their performance assuming the merger and acquisition. The analysis reveals that Korean Air’s super-efficiency performance in 2011 is the highest among all decision making units (DMUs). The best super-efficiency performance is achieved not only by individual companies but also by the combined enterprise in 2019.

LOC based battery management system

Electric vehicles generate a large amount of real vehicle data every day, which poses great difficulties for researchers to conduct subsequent data analysis. This article proposes an overall plan for the data analysis system, and the software design implements its main functions, including reading different battery data types, analysing and displaying vehicle, individual, and fault information, greatly reducing the workload of researchers.

Design of a System for the Protection of Cardiac Data

The recording, processing, analysis and storage of cardiac data is an essential step in achieving better care for the cardiac health status of patients. An important aspect in the processing and access to this type of data is the ability to reliably protect it from unauthorized access. The article shows an automated system for registration and mathematical analysis of cardiac data and methods for their protection. Information protection is achieved by implementing two-factor authentication. The characteristic of two-factor authentication is that access to the system takes place in two stages: entering a password as the first step and a code/key as the second. In this way, for protection, unauthorized access to the patient 39 file, including personal information and results of cardiac tests, is prevented.

LETHE: A Digital Intervention for Cognitive Decline.

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

Retracted: Data Analysis and Feedback System Construction of University Students’ Psychological Fitness Based on Fuzzy Clustering

Retracted: Data Analysis and Feedback System Construction of University Students’ Psychological Fitness Based on Fuzzy Clustering

What influences gig economy delivery rider behaviour and safety? a systems analysis

The safety of gig economy delivery riders is a growing area of concern. As a relatively new form of work, little research has examined delivery rider crash causation or the influences on delivery rider behaviour. This study involved the development of an ActorMap and AcciMap showing key stakeholders and factors that influence gig economy delivery rider behaviour and safety in Victoria, Australia, based on a literature review and workshops involving delivery riders and gig economy, road safety, and workplace safety stakeholders. A participatory design process was then undertaken to create PreventiMaps of potential interventions designed to respond to key issues with a view to enhancing delivery rider safety. The findings identify multiple factors influencing delivery rider behaviour spanning eight gig economy system areas: 1. the delivery task, 2. the workforce, 3. apps, algorithms and data, 4. infrastructure, 5. cultural and social influences, 6. clients, customers and other road users, 7. delivery platform processes, and 8. laws and regulations. Example proposed interventions include improvements to rider onboarding processes, training, and education programs, enforcement, enhanced data collection and analysis systems, modifications to delivery apps, improvements in cycling and road infrastructure, changes to payment and superannuation, and the development of regulatory mechanisms specifically for the gig economy delivery rider context. The implications for gig economy delivery rider safety, research, and practice are discussed and a systems thinking approach is advocated.