Big data analytics最新文献_第5页

Acknowledgment to the Reviewers of Analytics in 2022 对2022年《分析》评审者的感谢

Big data analytics Pub Date : 2023-01-18 DOI: 10.3390/analytics2010005

引用次数: 0

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs RDF知识图分析方法综述

Big data analytics Pub Date : 2023-01-17 DOI: 10.3390/analytics2010004

Maria-Evangelia Papadaki, Yannis Tzitzikas, M. Mountantonakis

引用次数: 2

Theoretical Contributions to Three Generalized Versions of the Celebioglu–Cuadras Copula 对Celebioglu-Cuadras Copula的三个广义版本的理论贡献

Big data analytics Pub Date : 2023-01-10 DOI: 10.3390/analytics2010003

C. Chesneau

{"title":"Theoretical Contributions to Three Generalized Versions of the Celebioglu–Cuadras Copula","authors":"C. Chesneau","doi":"10.3390/analytics2010003","DOIUrl":"https://doi.org/10.3390/analytics2010003","url":null,"abstract":"Copulas are probabilistic functions that are being used more and more frequently to describe, examine, and model the interdependence of continuous random variables. Among the numerous proposed copulas, renewed interest has recently been shown in the so-called Celebioglu–Cuadras copula. It is mainly because of its simplicity, exploitable dependence properties, and potential for applicability. In this article, we contribute to the development of this copula by proposing three generalized versions of it, each involving three tuning parameters. The main results are theoretical: they consist of determining wide and manageable intervals of admissible values for the involved parameters. The proofs are mainly based on limit, differentiation, and factorization techniques as well as mathematical inequalities. Some of the configuration parameters are new in the literature, and original phenomena are revealed. Subsequently, the basic properties of the proposed copulas are studied, such as symmetry, quadrant dependence, various expansions, concordance ordering, tail dependences, medial correlation, and Spearman correlation. Detailed examples, numerical tables, and graphics are used to support the theory.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77546606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A Parallel Implementation of the Differential Evolution Method 微分演化方法的并行实现

Big data analytics Pub Date : 2023-01-09 DOI: 10.3390/analytics2010002

Vasileios Charilogis, I. Tsoulos

引用次数: 0

A Review of the Vehicle Routing Problem and the Current Routing Services in Smart Cities 智慧城市车辆路径问题及路径服务现状综述

Big data analytics Pub Date : 2022-12-22 DOI: 10.3390/analytics2010001

Eleni Boumpa, Vasileios Tsoukas, Vasileios Chioktour, M. Kalafati, G. Spathoulas, A. Kakarountas, P. Trivellas, P. Reklitis, G. Malindretos

引用次数: 1

Using Internet Search Data to Forecast COVID-19 Trends: A Systematic Review 利用互联网搜索数据预测COVID-19趋势:系统综述

Big data analytics Pub Date : 2022-12-02 DOI: 10.3390/analytics1020014

Simin Ma, Yan Sun, Shihao Yang

引用次数: 2

A Quantitative Analysis of Information Systems Management in the Educational Industry 教育行业信息系统管理的定量分析

Big data analytics Pub Date : 2022-12-01 DOI: 10.3390/analytics1020013

J. L. Rubio Sánchez

{"title":"A Quantitative Analysis of Information Systems Management in the Educational Industry","authors":"J. L. Rubio Sánchez","doi":"10.3390/analytics1020013","DOIUrl":"https://doi.org/10.3390/analytics1020013","url":null,"abstract":"1. Purpose: One of the consequences of the COVID-19 pandemic period was the migration of educational centers from face-to-face learning to e-learning. Most centers adapted their educational services and technological resources so that the students could attend the courses online and the teachers (and the rest of the staff) could telework. So, technology departments have become critical in educational services and need to adapt their processes. The ITIL (Information Technology Infrastructure Library) standard guides companies for this transformation. If educational centers are involved in digital transformation, the question to solve is the following: How far are the processes used in the technology departments of educational centers from the ITIL standard adopted in the information technology industry? The purpose of this research was to investigate whether technology departments have implemented the necessary processes. 2. Methods. The research was conducted by means of an online form sent to educational organizations to gather information about their technological processes. The responses collected from the web forms were statistically analyzed. 3. Results and conclusion. The main finding in this paper was that technology departments in educational centers have yet to adopt the processes required for an intensive online service, demonstrating a weakness in educational institutions.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"574 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80578657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Speed Matters: What to Prioritize in Optimization for Faster Websites 速度问题:如何优先优化更快的网站

Big data analytics Pub Date : 2022-11-25 DOI: 10.3390/analytics1020012

Christina Xilogianni, Filippos-Rafail Doukas, Ioannis C. Drivas, D. Kouis

{"title":"Speed Matters: What to Prioritize in Optimization for Faster Websites","authors":"Christina Xilogianni, Filippos-Rafail Doukas, Ioannis C. Drivas, D. Kouis","doi":"10.3390/analytics1020012","DOIUrl":"https://doi.org/10.3390/analytics1020012","url":null,"abstract":"Website loading speed time matters when it comes to users’ engagement and conversion rate optimization. The websites of libraries, archives, and museums (LAMs) are not an exception to this assumption. In this research paper, we propose a methodological assessment schema to evaluate the LAMs webpages’ speed performance for a greater usability and navigability. The proposed methodology is composed of three different stages. First, the retrieval of the LAMs webpages’ speed data is taking place. A sample of 121 cases of LAMs worldwide has been collected using the PageSpeed Insights tool of Google for their mobile and desktop performance. In the second stage, a statistical reliability and validity analysis takes place to propose a speed performance measurement system whose metrics express an internal cohesion and consistency. One step further, in the third stage, several predictive regression models are developed to discover which of the involved metrics impact mostly the total speed score of mobile or desktop versions of the examined webpages. The proposed methodology and the study’s results could be helpful for LAMs administrators to set a data-driven framework of prioritization regarding the rectifications that need to be implemented for the optimized loading speed time of the webpages.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"46 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80013515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Foundation for Archival Engineering 档案工程基础

Big data analytics Pub Date : 2022-11-18 DOI: 10.3390/analytics1020011

Kenneth Thibodeau

{"title":"A Foundation for Archival Engineering","authors":"Kenneth Thibodeau","doi":"10.3390/analytics1020011","DOIUrl":"https://doi.org/10.3390/analytics1020011","url":null,"abstract":"Archives comprise information that individuals and organizations use in their activities. Archival theory is the intellectual framework for organizing, managing, preserving and access to archives both while they serve the needs of those who produce them and later when researchers consult them for other purposes. Archival theory is sometimes called archival science, but it does not constitute a modern science in the sense of a coherent body of knowledge formulated in a way that is appropriate for empirical testing and validation. Both archival theory and practice are seriously challenged by the spread and continuing changes in information technology and its increasing and increasingly diverse use in human activities. This article describes problems with and controversies in archival theory and advocates for a reformulation of concepts to address the digital challenge and to make the field more robust, both by addressing the problems and by enriching its capabilities by adopting concepts from other fields such as taxonomy, semiotics and systemic functional linguistics. The objective of this reformulation is to transform the discipline on the model of modern scientific method in a way that engenders a new discipline of archival engineering that is robust enough to guide the development of automated methods even in the face of continuing and unpredictable change in IT.","PeriodicalId":93078,"journal":{"name":"Big data analytics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78342885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Segmentation and Classification of Aerial Forest Imagery 航空森林图像的自动分割与分类

Big data analytics Pub Date : 2022-11-14 DOI: 10.3390/analytics1020010

Kieran Pichai, B. Park, Aaron Bao, Yiqiao Yin

引用次数: 0