Concepts, Methods, and Systems for Machine Data Analysis
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Publikationen: Thesis / Studienabschlussarbeiten und Habilitationsschriften › Dissertation
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T1 - Concepts, Methods, and Systems for Machine Data Analysis
AU - Rothschedl, Christopher Josef
N1 - no embargo
PY - 2020
Y1 - 2020
N2 - This thesis is concerned with the means of acquiring data from cyber physical systems, including the required infrastructure, and the use of both classical and novel approaches to derive insights from time series data. The systems discussed are associated with industries which require the establishment of complete data management frameworks to integrate essential domain expertise to gain knowledge. In a generic manner, the development of a holistic, secure and flexible concept for data life cycle management is presented, for heterogeneous fleets of mobile machines. Apart from providing robustness, such a concept must be capable of adopting changes, such as extensions with subsystems or replacements of components resulting from obsolescence of technology. After a study of the fundamental requirements for hardware, machine interfaces, data handling and storage, as well as data provisioning, an implementation is shown for different machines used in the mining and materials handling industry. To take the insights from these implementation scenarios into account, the concept for a new system for machines of the geotechnical engineering sector is developed and implemented. The framework for a qualitative data flow is presented, which allows experts to interact with the data of their machines; a step necessary to create added value from time series. It consists of multiple levels of data preparation and presentation methods, to identify elements of interest. Outliers can be highlighted for further investigations, based on rules applied to key performance indicators. Furthermore, it is shown that true knowledge discovery can be supported significantly by mimicking the mechanisms of the emergence of natural language. This step takes the specific nature of the data into account, since the time series emanate from physical systems and, hence, must abide by the laws of physics. An exemplary evaluation performed in this manner reveals implicit hierarchical structure in the operational data. Only an initial set of language elements are defined as input for a subsequent iterative process. A hierarchy of compounded frequent elements is yielded, the top layer of which reveals the existence of two major sequences that correlate with the two main operation modes. It is shown that the interpretation of the results by domain experts is indispensable for knowledge gain. This is emphasised by the metaphorical capacity exhibited by language-affine evaluation approaches, which are discussed in detail. A model for the emergence of language, based on phenomenological aspects, is proposed to combine the factors of relevance for knowledge discovery.
AB - This thesis is concerned with the means of acquiring data from cyber physical systems, including the required infrastructure, and the use of both classical and novel approaches to derive insights from time series data. The systems discussed are associated with industries which require the establishment of complete data management frameworks to integrate essential domain expertise to gain knowledge. In a generic manner, the development of a holistic, secure and flexible concept for data life cycle management is presented, for heterogeneous fleets of mobile machines. Apart from providing robustness, such a concept must be capable of adopting changes, such as extensions with subsystems or replacements of components resulting from obsolescence of technology. After a study of the fundamental requirements for hardware, machine interfaces, data handling and storage, as well as data provisioning, an implementation is shown for different machines used in the mining and materials handling industry. To take the insights from these implementation scenarios into account, the concept for a new system for machines of the geotechnical engineering sector is developed and implemented. The framework for a qualitative data flow is presented, which allows experts to interact with the data of their machines; a step necessary to create added value from time series. It consists of multiple levels of data preparation and presentation methods, to identify elements of interest. Outliers can be highlighted for further investigations, based on rules applied to key performance indicators. Furthermore, it is shown that true knowledge discovery can be supported significantly by mimicking the mechanisms of the emergence of natural language. This step takes the specific nature of the data into account, since the time series emanate from physical systems and, hence, must abide by the laws of physics. An exemplary evaluation performed in this manner reveals implicit hierarchical structure in the operational data. Only an initial set of language elements are defined as input for a subsequent iterative process. A hierarchy of compounded frequent elements is yielded, the top layer of which reveals the existence of two major sequences that correlate with the two main operation modes. It is shown that the interpretation of the results by domain experts is indispensable for knowledge gain. This is emphasised by the metaphorical capacity exhibited by language-affine evaluation approaches, which are discussed in detail. A model for the emergence of language, based on phenomenological aspects, is proposed to combine the factors of relevance for knowledge discovery.
KW - Cyber-physisches System
KW - Bergbau und Fördertechnik
KW - Geotechnik
KW - Phänomenologie
KW - Anwendungsfachexpertise
KW - Datenwissenschaft
KW - Wissensforschung
KW - Natürliche Sprache
KW - Symbolische Zeitreihenanalyse
KW - Hierarchische Struktur
KW - Implizite Struktur
KW - Cyber Physical Systems
KW - Mining and Materials Handling
KW - Geotechnical Engineering
KW - Domain Expertise
KW - Data Science
KW - Knowledge Discovery
KW - Symbolic Time Series Analysis
KW - Natural Language
KW - Hierarchical Structure
KW - Implicit Structure
KW - Phenomenology
M3 - Doctoral Thesis
ER -