Development Of A Modular Knowledge Discovery Framework Based On Machine Learning For The Interdisciplinary Analysis Of Complex Phenomena In The Context Of Gdi Combustion Processes
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Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes
Author: Botticelli, Massimiliano
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-07-03
In this work, a novel knowledge discovery framework able to analyze data produced in the Gasoline Direct Injection (GDI) context through machine learning is presented and validated. This approach is able to explore and exploit the investigated design spaces based on a limited number of observations, discovering and visualizing connections and correlations in complex phenomena. The extracted knowledge is then validated with domain expertise, revealing potential and limitations of this method.
Referenzarchitekturmodell für Experience Management
Author: Krickel, Kai
language: de
Publisher: KIT Scientific Publishing
Release Date: 2025-01-23
Die Erfassung und Analyse individueller Erlebniswelten über digitale Feedback-Kommunikation bietet neue Chancen. In Verbindung mit der Rekonstruktion zugehöriger, digitaler Ereignisketten lassen sich Erlebniswelten eines Kunden nachbilden. Die Daten dieser Erlebniswelt können so einem Managementprozess zugeführt werden. Zur wirtschaftlichen Nutzung der entstehenden Datenmenge für Experience Management benötigen Unternehmen referenzierbare Modelle und IT-Architekturen.
Advanced Methods for Knowledge Discovery from Complex Data
Author: Ujjwal Maulik
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-06
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.