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International Conference on Business and Technology on Navigating the Technological Tide - The Evolution and Challenges of Business Model Innovation (ICBT)

作者： Ruqti, Dina Alkhammash, Mohammad Al-Eisawi, Dalia Princess Sumaya Univ Technol Amman 11941 Jordan Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9783031674334;9783031674341

This research paper addresses transportation and traffic challenges through descriptive data analytics and a proposed real-time visual interactive Business Intelligence (BI) dashboard. The paper examines data on critical attributes such as speed limits, traffic volume, road types, and average, minimum, and maximum speeds in different regions. The proposed monitoring and control solution is presented by employing visualisation techniques and methods that involve the utilisation of diverse data analytics graph formats and visuals. The CRISP-DM methodology was implemented as a core methodology, starting from data understanding, and ending with a rigorous data visualisation process. Power BI was used as a tool to visually create a clear and user-friendly dashboard with the aim of aiding individuals and businesses in optimising decision-making. This includes selecting the most efficient routes and forecasting travel time. The dashboard provides valuable insights. As a final implication, the current study aimed to assist authorities in identifying practical solutions to traffic and congestion issues, thereby improving citizens' quality of life.

关键词： descriptive analytics Dashboard Efficient Routes Power BI Crisp-dm Methodology

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Vessel Trajectory Data Mining: A Review

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IEEE ACCESS 2025年 13卷 4827-4856页

作者： Troupiotis-Kapeliaris, Alexandros Kastrisios, Christos Zissis, Dimitris Univ Aegean Intelligent Transportat Syst Lab Smart MOVE Dept Prod & Syst Design Engn Ermoupoli 84100 Greece Univ New Hampshire Ctr Coastal & Ocean Mapping Joint Hydrog Ctr Durham NH 03824 USA

Recent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring within specific spatial and temporal contexts. This survey offers a comprehensive review of contemporary research in trajectory data mining, with a particular focus on maritime applications. The article collects and evaluates state-of-the-art algorithmic approaches and key techniques pertinent to various use case scenarios within this domain. Furthermore, this study provides an in-depth analysis of recent developments in trajectory data mining as applied to the maritime sector, identifying available data sources and conducting a detailed examination of significant applications, including trajectory forecasting, activity recognition, and trajectory clustering.

关键词： Maritime monitoring Data mining spatio-temporal data mining trajectory analytics pattern mining descriptive analytics predictive analytics Maritime monitoring data mining spatio-temporal data mining trajectory analytics pattern mining descriptive analytics predictive analytics

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Evolving research themes in six selected wood science journals: insights from text mining and latent dirichlet allocation

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JOURNAL OF WOOD SCIENCE 2024年第1期70卷 56页

作者： Hwang, Sung-Wook Lee, Won-Hee Kyungpook Natl Univ Inst Agr Sci & Technol 80 Daehak Ro Daegu 41566 South Korea Kyungpook Natl Univ Sch Forestry Sci & Landscape Architecture Major Wood & Paper Sci 80 Daehak Ro Daegu 41566 South Korea

This study analyzes the status, trends, and future directions in wood science research using text-mining techniques. We applied these techniques to a textual dataset constructed from metadata of six major wood science journals, covering the period from 2002 to 2024. The research explores publication trends, international collaborations, keywords, and research networks, and it employs topic modeling using the Latent Dirichlet Allocation model. The descriptive analysis reveals a consistent increase in publication volume throughout the study period, unaffected by the COVID-19 pandemic. In contrast, international collaboration declined after 2020, likely due to the pandemic. In addition, a network analysis identified key research areas, including surface treatments, structural composites, and high-performance wood products, with lignin, mechanical properties, and moisture content emerging as central keywords. Topic modeling reveals a growing interest in wood modification technologies and an increased focus on studying wood as a sustainable material. The study confirms a shift of the field towards sustainable innovations while also highlighting the enduring relevance of traditional research areas. Future research should adapt to these evolving trends and address emerging challenges to maximize the potential of wood for carbon neutrality and sustainable development. This analysis provides a concise overview of current research trends and future directions in wood science.

关键词： Centrality descriptive analytics Latent dirichlet allocation Network analysis Text mining Topic modeling Trend analysis Wood science

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A Systematic Review Towards Big Data analytics in Social Media

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Big Data Mining and analytics 2022年第3期5卷 228-244页

作者： Md.Saifur Rahman Hassan Reza School of Electrical Engineering and Computer Science University of North DakotaGrand ForksND 58202USA

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 *** new era allows the consumer to directly connect with other individuals,business corporations,and the *** are open to sharing opinions,views,and ideas on any topic in different formats out *** creates the opportunity to make the"Big Social Data"handy by implementing machine learning approaches and social data *** study offers an overview of recent works in social media,data science,and machine learning to gain a wide perspective on social media big data *** explain why social media data are significant elements of the improved data-driven decision-making *** propose and build the"Sunflower Model of Big Data"to define big data and bring it up to date with technology by combining 5 V’s and 10 *** discover the top ten social data analytics to work in the domain of social media platforms.A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work."Text analytics"is the most used analytics in social data analysis to *** create a taxonomy on social media analytics to meet the need and provide a clear ***,techniques,and supporting data type are also discussed in this research *** a result,researchers will have an easier time deciding which social data analytics would best suit their needs.

关键词： big data social media big data analytics social media analytics text analytics image analytics audio analytics video analytics predictive analytics descriptive analytics prescriptive analytics diagnostic analytics

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Morocco's Football Triumph in the 2022 FIFA World Cup: A Data-Driven Analysis of Sociocultural Impact Using Big Data analytics

Morocco's Football Triumph in the 2022 FIFA World Cup: A Dat...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Bari, Anasse Hon, Edward Wang, Charles Cui, Caitlyn Ker, Emos Miinko, Alex Alabdullah, Nawaf Aishehhi, Ali Lawlor, Kelly Straesser, Sebastian Abror, Advait NYU Dept Comp Sci Coll Arts & Sci Courant Inst Math Sci New York NY 10003 USA

ISBN: (纸本)9798350361513;9798350372304

In the 2022 Federation Internationale de Football Association (FIFA) World Cup, Morocco's National Football Team achieved a historic fourth-place finish, garnering global attention on platforms like Reddit, X, Facebook, and Instagram. This study presents an advanced descriptive analytics framework, establishing a proprietary set of composite Morocco Indices - Country Index, World Cup Index, Jersey Index, YouTube Index, and Tourism Index - through alternative data and natural language processing. These Indices captured a surge in positive sentiment on topics related to Morocco, specifically a 400% average increase in global search volumes related to Morocco's cuisine, culture, and attractions during and after the World Cup. Notably, queries like "best time to visit Morocco", "Visa to Morocco", and "Morocco travel," and other semantically-related posts saw drastic increases in search volume. This study's results exhibit the profound sociocultural impact of mega sporting events on developing nations, highlighting how such events are able to significantly alter international perception and cultural interest in a country. The devised framework that is specialized at monitoring public opinion and sentiment may offer invaluable insights to FIFA and other entities keen on reporting and strategizing future returns on investment through the avenue of sports.

关键词： FWA Morocco World Cup Natural Language Processing descriptive analytics Predictive analytics Big Data

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Baca Beverage Distributors: surviving the great attrition

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CASE Journal 2024年第3期20卷 604-633页

作者： Anderson, Joe Joshi, Mahendra Williams, Susan K. The W. A. Franke College of Business Northern Arizona University

Theoretical basis: This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic. Research methodology: BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes. Case overview/synopsis: During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving. Complexity academic level: This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time fro

关键词： Dashboards Data storytelling descriptive analytics Dynamic dashboard Employee attrition Human resource management Tableau Visualization

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Enhancing manufacturing process by predicting component failures using machine learning

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NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 18155-18169页

作者： Saadat, Raihanus Syed-Mohamad, Sharifah Mashita Azmi, Athira Keikhosrokiani, Pantea Univ Sains Malaysia Sch Comp Sci George Town 11800 Penang Malaysia Univ Putra Malaysia Fac Design & Architecture Serdang 43400 Selangor Malaysia

Manufacturers customize computer components upon receipt of sale orders. They perform burn-in tests on each unit of product before shipment to ensure a high standard of quality. Burn-in is normally associated with high production costs and slows down manufacturing operations. This study aims to enhance the manufacturing process by predicting test failure patterns using machine learning methods. By identifying the components that are likely to cause failures, manufacturers can accelerate the rectification process and improve delivery time which in turn leads to better customer service. This study hypothesized that the component of concern produces a higher test failure rate. To provide insight into the data and test the hypothesis, descriptive and predictive analytics are used at various stages. Predictive analytics was performed using machine learning via Naive Bayes since it outperformed SVM and Random Forest classifier. For the descriptive analysis stage, a visual representation revealed many components (81) to be associated with a more than average test failure rate. Fisher's exact test confirmed that 12 of them are statistically significant and worth studying their behaviour further. Moreover, an association rule mining exercise identified several combinations of modules that have a higher inclination with the test failure. For the predictive analytics stage, the Naive Bayes classifier predicted test failure with 79% accuracy and 53% recall rate. Another Naive Bayes classifier predicted error messages associated with a test failure with 68% recall rate over manually labelled error messages. However, a neural network-based automatic text classifier was developed and tested that yielded 66% accuracy. This analysis provides the foundation for a recommendation made that can reduce the burn test failure rate by 25% which is expected to increase further with the improved performance model upon training with a larger data set.

关键词： descriptive analytics Predictive analytics Burn-in tests Manufacturing process

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To imprison or not to imprison: an analytics model for drug courts

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期303卷 101-124页

作者： Delen, Dursun Zolbanin, Hamed M. Crosby, Durand Wright, David Halic Univ Fac Management Istanbul Turkey Univ Dayton Sch Business Dayton OH 45469 USA Oklahoma Dept Mental Hlth & Subst Abuse Serv Oklahoma City OK USA Oklahoma State Univ Dept Management Sci & Informat Syst Spears Sch Business 700 N Greenwood Ave Tulsa OK 74106 USA

Despite all the promises of analytics, its complexity, multidimensionality, and multidisciplinary nature can sometimes disserve its efficacy. What can further aggravate the problem is the need to deal with human behavior and social interactions as inherent qualities of the application domain. One such area is the drug court;an alternative for traditional criminal courts that attempts to transform the traditional punitive jurisprudence to a therapeutic one. Under this new philosophy, the eligible offenders are considered as individuals in need of rehabilitative treatments and are persuaded to undergo a regimen that seeks to return them back to the community, rather than sending them to prison. This initiative, if performed properly, has proven to be effective in lowering the costs and improving the social outcomes. While many researchers have studied this initiative from the perspective of its factors, requirements, and tradeoffs, there currently is a lack of a comprehensive analytics model that can accurately predict who would (or would not) graduate from these programs. To fill this gap, and to enable better management of resources and improvement of outcomes, this study develops an analytics model to describe a large real-world sample of drug court participants;to predict who would or would not graduate from these courts;and to prescribe a set of guidelines (presented as characteristics of the offenders) that can help jurisdictions and drug court administrators to make more effective and efficient decisions.

关键词： Data analytics descriptive analytics Predictive analytics Prescriptive analytics Drug courts Prison diversion programs

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Mining on Senior Citizens Data: Empirical Evidence in the Philippines Locale "Basis for Social Services Improvement and Decision Making"

Mining on Senior Citizens Data: Empirical Evidence in the Ph...

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2022 International Conference on Automatic Control and Intelligent Systems

作者： Castro, Eltimar T., Jr. Romano, Nephi P. Father Saturnino Urios Univ Data Sci & Analyt Ctr Butuan City Philippines

ISBN: (纸本)9781665495813

Senior citizens are among the most vulnerable members of society. Each country is concerned about how to improve their level of life and secure their social support and services. The Philippines faced with the problem of improving social services for its expanding population of elderly. Several agencies are tasked with the responsibility of providing improved social services that promote the welfare of the city's senior citizens. To achieve highest degree of satisfaction to all elderly, rigorous steps must be taken to identify process gaps and build a system that aid in the process's acceleration. Data mining technique help to generate insights and visualization, thus facilitating government organizations that oversee health and social services. The results can aid for policy and decision making of the National and Local Government Agency. The results showed that there are increasing number of senior citizens profiled with an estimated average of 3,338 per year started last 2017. The target numbers of elderlies to be profiled is around 25,000 in 2022, these figures are aligned to Philippines Statistic Authority (PSA). The findings revealed that 25 percent of profiled elderlies are not registered in PhilHealth, and 85 percent of them are not pensioner thus, the results are implying that there are elderlies entitled to receive benefits as mandated by Law. Overall, the assessment showed that there are areas in social services that needs immediate actions to deliver quality social services for senior citizens in future. Furthermore, researcher recommends more dataset to be analyze and system integration for realtime monitoring and immediate actions.

关键词： Senior Citizens Data Mining analytics descriptive analytics Trends

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Exploring the Characteristics of Data Breaches: A descriptive Analytic Study

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Journal of Information Security 2024年第2期15卷 168-195页

作者： Dominik Molitor Aditya Saharia Viju Raghupathi Wullianallur Raghupathi Gabelli School of Business Fordham University New York USA Brooklyn College The City University of New York New York USA

Data breaches have massive consequences for companies, affecting them financially and undermining their reputation, which poses significant challenges to online security and the long-term viability of businesses. This study analyzes trends in data breaches in the United States, examining the frequency, causes, and magnitude of breaches across various industries. We document that data breaches are increasing, with hacking emerging as the leading cause. Our descriptive analyses explore factors influencing breaches, including security vulnerabilities, human error, and malicious attacks. The findings provide policymakers and businesses with actionable insights to bolster data security through proactive audits, patching, encryption, and response planning. By better understanding breach patterns and risk factors, organizations can take targeted steps to enhance protections and mitigate the potential damage of future incidents.

关键词： Cyber Security Information Security Data Breaches descriptive analytics Privacy Visualization Visual analytics

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