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作者： Liam Johnson Jonkoping University

学位级别：硕士

Today's dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains. The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using analytics. Qualitative methods have been applied for this study to gain a greater understanding of the Supply Chain analytics phenomenon. An inductive approach in form of interviews, was performed in order to gain new empirical data. Fifteen semi-structured interviews were conducted with professional individuals who hold managerial roles such as project managers, consultants, and end-users within the fields of Supply Chain Management and Big Data analytics. The received empirical information was later analyzed using the thematic analysis method. The main findings in this thesis relatively contradicts with previous studies and existing literature in terms of connotations, definitions and applications of the three main types of analytics. Furthermore, the findings present new approaches and perspectives that advanced analytics apply on both strategic and operational management levels that are shaping supply chain network designs.

关键词： Supply Chain analytics Big Data Business analytics descriptive analytics Supply Chain Network Design Logistics & Supply Chain Management Supply Chain Strategy Supply Chain Operations

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Quantum GIS Based descriptive and Predictive Data Analysis for Effective Planning of Waste Management

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IEEE ACCESS 2020年 8卷 46193-46205页

作者： Imran Ahmad, Shabir Kim, Do Hyeun Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea

Waste has a direct impact on human health and the surrounding environment. Apart from the health aspect, many industries' growth is effected by waste material such as the food industry. Waste management authorities are interested in reducing the cost of waste management operations and searching for sustainable waste management solutions. For effective planning of waste management, reliable data analysis is required to produce results that can facilitate the planning process. Data mining and machine learning-based data analysis over the waste data can produce a more detailed, and in-time waste information generation, which can lead to effectively manage the waste amount of specific area. In this paper, a descriptive data analysis approach, along with predictive analysis, is used to produce in-time waste information. The performance of the proposed approach is evaluated using a real waste dataset of Jeju Island, South Korea. Waste bins are virtualized on its actual location on the Jeju map in Quantum Geographic Information Systems(QGIS) software. The performance results of the predictive analysis models are evaluated in terms of Mean Absolute Error(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). Performance results indicate that predictive analysis models are reliable for the effective planning and optimization of waste management operations.

关键词： Waste monitoring QGIS descriptive analytics predictive analytics waste management data analysis

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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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Fuzzy formal concept analysis based opinion mining for CRM in financial services

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APPLIED SOFT COMPUTING 2017年 60卷 786-807页

作者： Ravi, Kumar Ravi, Vadlamani Prasad, P. Sree Rama Krishna Inst Dev & Res Banking Technol Ctr Excellence Analyt Castle Hills Rd 1 Hyderabad 500057 Andhra Prades India Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Prades India Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

Owing to the easy access to social media, consumers or customers are increasingly turning to social media to express their grievances and feedback on various products and services offered by the Banking, Financial, Services and Insurance industry. Because non-redressal of complaints eventually leads to customer churn, there is an urgent need to analyze the complaints. In this regard, we propose a novel descriptive analytics model that performs complaints/grievances analytics and summarizes the lengthy and verbose complaints concisely in a form that resembles association rules. The proposed hybrid model comprises fuzzy formal concept analysis and concept-level sentiment analysis (FFCA + SA) in tandem, which in turn is compared against formal concept analysis and concept-level sentiment analysis (FCA + SA). Because of the immediate fallout of the negative sentiments, a financial company is interested in studying them in more detail than the positive ones. Therefore, the model generates a list of association rules, the corresponding negative sentiment score along with the list of associated documents. Association rules are rank ordered according to the negative sentiment score, which in turn reflects severity affected services/products. The proposed model also provides interactive visualization that enables business analysts and managers to access a specific set of complaints without having to go through the entire set thoroughly. This saves a lot of time that would have otherwise been spent on cumbersome manual operations. Moreover, partial evaluation of the proposed methodology by human annotators yielded 64.06% matching score in terms of the opinions determination of aspects. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Sentiment analysis Fuzzy formal concept analysis Customer complaints Text mining Association rules descriptive 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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Visualization of descriptive multiblock analysis

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JOURNAL OF CHEMOMETRICS 2020年第1期34卷 e3071-e3071页

作者： Skotare, Tomas Sjogren, Rickard Surowiec, Izabella Nilsson, David Trygg, Johan Umea Univ Dept Chem Computat Life Sci Cluster CLiC S-90187 Umea Sweden Sartorius Stedim Data Analyt S-90736 Umea Sweden

Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.

关键词： data fusion descriptive analytics multiblock analysis OnPLS visualization

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Treatment process differences with and without an ambulance offload zone

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第7期71卷 1137-1144页

作者： Elliott, Molly Vanberkel, T. Peter Carter, J. E. Alix Dalhousie Univ Halifax NS Canada

In this paper we compare the emergency department (ED) treatment processes which occur when an Ambulance Offload Zone (OZ) is functioning and when it is not. An OZ is a monitored waiting space for ambulance patients which is designed to allow ambulance crews to return to service more quickly. The implementation of OZ style concepts has been trialled around the world with mixed results. In this paper we use concurrent comparison data and show how treatment processes and the behaviour of ED actors change when the OZ is functioning. We discuss how these changes in behaviour impact the effectiveness of the OZ.

关键词： Emergency medical systems ambulance offload delay descriptive analytics behavioural operational research

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A novel decision support system for the interpretation of remote sensing big data

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EARTH SCIENCE INFORMATICS 2018年第1期11卷 31-45页

作者： Boulila, Wadii Farah, Imed Riadh Hussain, Amir Univ Manouba Natl Sch Comp Sci RIADI Lab Manouba Tunisia Univ Rennes 1 ITI Dept Telecom Bretagne Brest France Univ Stirling Sch Nat Sci Div Comp Sci & Maths Stirling Scotland

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the increase of aerial and satellite sensors. With this continuous increase, the necessity of having automated tools for the interpretation and analysis of RS big data is clearly obvious. The manual interpretation becomes a time consuming and expensive task. In this paper, a novel tool for interpreting and analyzing RS big data is described. The proposed system allows knowledge gathering for decision support in RS fields. It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics. The paper outlines the design and development of a framework based on three steps: RS data acquisition, modeling, and analysis & interpretation. The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules. Results show that the proposed tool can provide efficient decision support (descriptive and predictive) which can be adapted to several RS users' requests. Additionally, assessing these results show good performances of the developed tool.

关键词： Decision support system Remote sensing data Image interpretation ETL process Data warehouse Predictive analytic descriptive analytics Prescriptive analytics

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From Hello to Bye-Bye: Churn Prediction in English Language Learning App 29

From Hello to Bye-Bye: Churn Prediction in English Language ...

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29th International Conference on Computers in Education (ICCE)

作者： Singh, Daevesh Pathan, Rumana Banerjee, Gargi Rajendran, Ramkumar Indian Inst Technol IDP Educ Technol Mumbai Maharashtra India

ISBN: (纸本)9789869721479

Mobile phones and apps have changed the landscape of e-learning and have revolutionised the way people learn a second language by facilitating anytime-anywhere learning, game-based resources and socially interactive learning activities. Despite these features and affordances, these language learning apps suffer a fate of high churn rates. In this paper, we examined the churning behaviour of learners in the context of a language learning app called Hello English. We applied descriptive analytics to analyse the behavioural differences between churners and non-churners and studied their interaction with the app to early-predict churning behaviour. Our findings indicate that non-churners interact with the mobile app more frequently compared to churners. Also, the trained machine learning classifiers can predict learner churning behaviour with a high recall value (0.824) and F1 (0.778). This churn detection will enable the app developers to provide intervention for learner retention.

关键词： Language learning churn rate interaction behaviour descriptive analytics

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'Without data, you're just another person with an opinion'

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EXPERT REVIEW OF PHARMACOECONOMICS & OUTCOMES RESEARCH 2020年第2期20卷 147-154页

作者： Kolasa, Katarzyna Goettsch, Wim Petrova, Guenka Berler, Alexander Akad Leona Kozminskiego Hlth Econ & Healthcare Ma Dept Hlth Econ & Healthcare Management Warsaw Poland Univ Utrecht WHO Collaborating Ctr Pharmaceut Policy Div Pharmacoepidemiol & Clin Pharmacol Utrecht Netherlands Med Univ Sofia Fac Pharm Dept Social Pharm & Pharmacoecon Sofia Bulgaria Gnomon Dept Hlth Econ & Healthcare Management Athens Greece

Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy. Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare. Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

关键词： Big data predictive modeling descriptive analytics prescriptive analytics predictive analytics

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