Data Science and Management最新文献_第6页

Novel method for ranking batsmen in Indian Premier League 印度超级联赛击球手排名的新方法

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.06.004

M.K. Manju , Abin Oommen Philip

{"title":"Novel method for ranking batsmen in Indian Premier League","authors":"M.K. Manju , Abin Oommen Philip","doi":"10.1016/j.dsm.2023.06.004","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.004","url":null,"abstract":"<div><p>Sports analytics have benefited immensely from the growth and popularity of artificial intelligence and machine learning. These techniques enable sports analysts to evaluate player performance more effectively. A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty20 (T20) cricket. A novel framework was proposed to evaluate batsman strength based on individual performance, role in the team, and team interactions. Traditionally, proposed ranking systems are derived from static networks, that is, the aggregation of game results over time. However, the scores of the players (or teams) fluctuate over time. Intuitively, defeating a renowned player during peak performance is more rewarding than defeating the same player during other periods. To account for this, we propose a new method and apply it to the T20 format Indian Premier League. The method serves three main purposes: First, it creates a new performance index for players to rank them more accurately and effectively. Second, the players are clustered based on their expertise. In the third phase, a social network analysis approach is applied to visualize and analyze crickets as a network to gain better insights into players’ team interactions. This novel approach is a helpful index for sports coaches, analysts, cricket fans, and managers to evaluate player performance and rank for future aspects.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 3","pages":"Pages 158-173"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749481","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: 1

Effects of economic factors on median list and selling prices in the U.S. housing market 经济因素对美国房地产市场中位价和售价的影响

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.08.001

Durga Vaidynathan, Parthajit Kayal, Moinak Maiti

引用次数: 0

The impact of the digital economy on the servitization of industrial structures: the moderating effect of human capital 数字经济对产业结构服务化的影响:人力资本的调节作用

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.06.003

Rong Ran, Xinyuan Wang, Ting Wang, Lei Hua

{"title":"The impact of the digital economy on the servitization of industrial structures: the moderating effect of human capital","authors":"Rong Ran, Xinyuan Wang, Ting Wang, Lei Hua","doi":"10.1016/j.dsm.2023.06.003","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.003","url":null,"abstract":"<div><p>The digital economy, which was born during the late third technological revolution, has caused significant economic and societal changes. Amid sluggish global economic growth, China’s economy is facing upgrades and transformations. The sample selection for this study was conducted from 2013 to 2020. Data related to the digital economy and servitization of the industrial structure of 30 Chinese provinces, municipalities, and autonomous regions were collected. This study presents the human capital variable, based on which an econometric analysis was conducted, and examines its moderating effect. The findings indicate that even after the replacement variable indicator’s robustness test, the relationship between the digital economy and the servitization of industrial structures remains unchanged. This study demonstrats that the quality of human capital plays a positive role in this effect. Finally, a heterogeneity test demonstrated that there are different pathways for the impact of the digital economy on the servitization of industrial structures in the eastern, central, and western regions. This study provides evidence to help researchers understand the moderating utility of human capital.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 3","pages":"Pages 174-182"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749640","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: 6

Systematic review of data-centric approaches in artificial intelligence and machine learning 系统回顾人工智能和机器学习中以数据为中心的方法

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.06.001

Prerna Singh

{"title":"Systematic review of data-centric approaches in artificial intelligence and machine learning","authors":"Prerna Singh","doi":"10.1016/j.dsm.2023.06.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.001","url":null,"abstract":"<div><p>Artificial intelligence (AI) relies on data and algorithms. State-of-the-art (SOTA) AI smart algorithms have been developed to improve the performance of AI-oriented structures. However, model-centric approaches are limited by the absence of high-quality data. Data-centric AI is an emerging approach for solving machine learning (ML) problems. It is a collection of various data manipulation techniques that allow ML practitioners to systematically improve the quality of the data used in an ML pipeline. However, data-centric AI approaches are not well documented. Researchers have conducted various experiments without a clear set of guidelines. This survey highlights six major data-centric AI aspects that researchers are already using to intentionally or unintentionally improve the quality of AI systems. These include big data quality assessment, data preprocessing, transfer learning, semi-supervised learning, machine learning operations (MLOps), and the effect of adding more data. In addition, it highlights recent data-centric techniques adopted by ML practitioners. We addressed how adding data might harm datasets and how HoloClean can be used to restore and clean them. Finally, we discuss the causes of technical debt in AI. Technical debt builds up when software design and implementation decisions run into “or outright collide with” business goals and timelines. This survey lays the groundwork for future data-centric AI discussions by summarizing various data-centric approaches.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 3","pages":"Pages 144-157"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749915","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

Blockchain and its derived technologies shape the future generation of digital businesses: a focus on decentralized finance and the Metaverse b区块链及其衍生技术塑造了下一代数字业务:专注于去中心化金融和虚拟世界

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.06.002

Saeed Banaeian Far , Azadeh Imani Rad , Maryam Rajabzadeh Asaar

{"title":"Blockchain and its derived technologies shape the future generation of digital businesses: a focus on decentralized finance and the Metaverse","authors":"Saeed Banaeian Far , Azadeh Imani Rad , Maryam Rajabzadeh Asaar","doi":"10.1016/j.dsm.2023.06.002","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.002","url":null,"abstract":"<div><p>Without a doubt, blockchain, as one of the most valuable technological advancements, has been introduced over the past decade and has played a significant role in the industrial revolution. The emergence of other technologies derived from blockchain, such as decentralized finance (DeFi) and the Metaverse, has fundamentally transformed people’s daily lives and profoundly impacted future versions of digital businesses. This study explored the evolution of digital businesses in the near future, with a specific focus on the two primary technologies mentioned above. First, we reviewed DeFi-based technologies, including GameFi, SciFi, SocialFi, and others which serve as foundational building blocks for future jobs and businesses. Second, we examined Metaverse-based jobs, such as Metaverse-based academies and markets which are expected to be launched as commonly used businesses. Ultimately, this study provides several guidelines, such as how to use DeFi 2.0 and apply centralized decentralized finance (CeDeFi)-based platforms. Additionally, it offers future directions for launching these businesses, including controlling the progress of artificial intelligence (AI) in practical applications, utilizing cloud-assisted models for the Metaverse, and providing conditional privacy for future Metaverse-based businesses.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 3","pages":"Pages 183-197"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49758595","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: 7

Effects of economic factors on median list and selling prices in the U.S. housing market 经济因素对美国房地产市场中位价和售价的影响

Data Science and Management Pub Date : 2023-09-01 DOI: 10.1016/j.dsm.2023.08.001

Durga Vaidynathan , Parthajit Kayal , Moinak Maiti

{"title":"Effects of economic factors on median list and selling prices in the U.S. housing market","authors":"Durga Vaidynathan , Parthajit Kayal , Moinak Maiti","doi":"10.1016/j.dsm.2023.08.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.08.001","url":null,"abstract":"<div><p>This study investigates the effects of key economic factors on the median list price and median selling price in the U.S. housing market. Key economic factors such as interest rates, unemployment rates, inflation rates, real gross domestic product, money supply, mortgage rate, Standard & Poor’s (S&P) 500, and government expenditure are investigated to understand their relationships with housing prices. Conventional econometric models are typically used for housing market analysis; however, advancements in data science and machine learning allow these relationships to be examined more accurately. This study employs a decision tree regressor, k-nearest neighbors, random forest, and gradient boosting to enhance analysis accuracy and feature selection, thus enriching literature pertaining to machine learning in the housing market domain. The significance of housing market data as an indicator of economic growth is emphasized, and its effect on the overall economy, consumer spending, investment patterns, and financial stability is discussed. By utilizing a robust dataset and performing rigorous preprocessing, this study aims to provide valuable insights for policymakers, investors, and individuals involved in the housing sector.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 4","pages":"Pages 199-207"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71784311","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

Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation 革命性的空间数据分析:揭示批坐标转换的前沿方法

Data Science and Management Pub Date : 2023-07-06 DOI: 10.1016/j.dsm.2023.07.001

Waruna Buddhika , Kumesha Premawansha , Thushara R. Bandara , Lakdinu Samaranayake , Viraj Dayananda , Chameera Mudannayaka , Shyama Priyadarshani

{"title":"Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation","authors":"Waruna Buddhika , Kumesha Premawansha , Thushara R. Bandara , Lakdinu Samaranayake , Viraj Dayananda , Chameera Mudannayaka , Shyama Priyadarshani","doi":"10.1016/j.dsm.2023.07.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.07.001","url":null,"abstract":"<div><p>Spatial data have become indispensable across various disciplines and provide crucial insights. These data are associated with coordinates and different coordinate systems. However, the diversity of geospatial data formats and disparate coordinate systems present challenges in harmonizing them for analysis. This study addresses the pressing need for an improved approach to the batch transformation of commonly used coordinate systems in Sri Lanka. First, we examine different coordinate transformation systems and identify their limitations. Subsequently, we present a comprehensive procedure for seamless coordinate transformations between various systems. To demonstrate the practical applications of our approach, we have developed a user-friendly desktop application capable of simultaneously converting input coordinates into multiple systems. This application streamlines the process for users unfamiliar with sophisticated geographic information system (GIS) applications and datum transformations. We validate the output coordinates transformed using our application by comparing them with those obtained from established applications such as ArcGIS and epsg.io. The results, which have been assessed based on the root mean squared error (RMSE) and mean absolute error (MAE), indicate high levels of accuracy, with a maximum RMSE of approximately 0.013 and a maximum MAE of approximately 0.008. A performance evaluation reveals that our approach is exceptionally efficient, outperforming ArcGIS and epsg.io by 40x and 60x, respectively. Moreover, the proposed pipeline holds potential as an infrastructure for developing web applications, mobile applications, and plugins for popular GIS platforms such as ArcGIS and QGIS.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 4","pages":"Pages 214-226"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000322/pdfft?md5=902e0547dd52f80eb999b91669690d7e&pid=1-s2.0-S2666764923000322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91773965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing spatial data analysis: Unveiling a cutting-edge approach for batch coordinate transformation 革命性的空间数据分析:揭示批坐标转换的前沿方法

Data Science and Management Pub Date : 2023-07-01 DOI: 10.1016/j.dsm.2023.07.001

Waruna Buddhika, Kumesha Premawansha, Thushara R. Bandara, Lakdinu Samaranayake, Viraj Dayananda, Chameera Mudannayaka, Shyama Priyadarshani

引用次数: 0

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management 一种机器学习方法，利用震级和后果的层次来形成地震类别，以指导应急管理

Data Science and Management Pub Date : 2023-06-28 DOI: 10.1016/j.dsm.2023.06.005

Donald Douglas Atsa'am , Terlumun Gbaden , Ruth Wario

{"title":"A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management","authors":"Donald Douglas Atsa'am , Terlumun Gbaden , Ruth Wario","doi":"10.1016/j.dsm.2023.06.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.06.005","url":null,"abstract":"<div><p>This study deployed <em>k</em>-means clustering to formulate earthquake categories based on magnitude and consequence, using global earthquake data spanning from 1900 to 2021. Based on patterns within the historical data, numeric boundaries were extracted to categorize the magnitude, deaths, injuries, and damage caused by earthquakes into low, medium, and high classes. Following a future earthquake incident, the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude, number of fatalities and injuries, and monetary estimates of total damage. The resulting taxonomy provides a means of classifying future earthquake incidents, thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident. Furthermore, the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 4","pages":"Pages 208-213"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000310/pdfft?md5=d091cab3db8db2f195cb54b6af5a5125&pid=1-s2.0-S2666764923000310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90015335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time series clustering of COVID-19 pandemic-related data COVID-19大流行相关数据的时间序列聚类

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.003

Zhixue Luo , Lin Zhang , Na Liu , Ye Wu

引用次数: 1