Knowledge and Wisdom
Edited as a Festschrift in honor of Prof Milan Zeleny, this volume reflects and emulates his unmistakable legacy: the essential multidimensionality of human and social affairs. It contains papers dealing with: Multiple Criteria Decision Making; Social and Human System Management; and Information, Knowledge and Wisdom Management.
This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links. Inhaltsverzeichnis Chapter 1 Knowledge Management.- Chapter 2 Data Sets.- Chapter 3 Basic Forecasting ToolsChapter 3 Basic Forecasting Tools.- Chapter 4 Multiple Regression.- Chapter 5 Regression Tree Models.- Chapter 6 Autoregressive Models.- Chapter 7 GARCH Models.- Chapter 8 Comparison of Models.
Modeling the Impact of COVID-19
The book explores the profound effects of COVID-19 on global economies, particularly focusing on supply chains and financial operations. It presents analytic tools and epidemic modeling to help governments and businesses navigate pandemic-related challenges. The text includes quantitative and text data sources, illustrating the pandemic's impacts, especially on the Swedish banking sector. Additionally, it covers financial contagion, debt risk analysis, and health system efficiency, emphasizing practical methods and accessible data rather than theoretical discussions.
Dieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Es thematisiert die Verbindung von menschlichem Wissen und technologischen Fortschritten in der heutigen Gesellschaft, insbesondere im Kontext von Big Data. Es werden drei Arten von Analyseinstrumenten vorgestellt: Die deskriptive Analyse liefert Berichte über vergangene Ereignisse, während die prädiktive Analyse statistische und KI-Methoden zur Vorhersage nutzt. Die diagnostische Analytik analysiert Sensoreingaben zur automatischen Steuerung von Kontrollsystemen. Die präskriptive Analytik optimiert Systeme mithilfe quantitativer Modelle. Data Mining umfasst sowohl deskriptive als auch prädiktive Modellierung, während Operations Research alle drei Bereiche abdeckt. Der Fokus des Buches liegt auf der deskriptiven Analytik, mit einfachen Erklärungen und Demonstrationen. Es bietet Beispiele für die Auswirkungen von Big Data und behandelt Assoziationsregeln sowie Clusteranalysen. Die Kapitel decken verschiedene Aspekte ab: einen Überblick im Wissensmanagement, grundlegende Software für Datenvisualisierung, Warenkorbanalyse, RFM-Modellierung, Assoziationsregel-Mining, Clusteranalyse und Link-Analyse. Die Modelle werden mit geschäftsbezogenen Daten demonstriert, und der Stil ist beschreibend, ohne tiefgehende wissenschaftliche Referenzen. Die verwendeten Datensätze und Software sind für Leser mit Internetzugang leicht zugänglich.