Einladung zum Fakultätskolloquium am 30.11.2020, 17:00 Uhr, Vortrag von Prof. Dr. Michael Kaufmann, Emergent Knowledge Engineering for Big Data Management


Am Montag, den 30.11.2020, um 17:00 Uhr spricht Herr Prof. Dr. Michael Kaufmann, Hochschule Luzern, über das Thema:

Emergent Knowledge Engineering for Big Data Management

Hinweis: Auf Grund der neuen Corona-Rahmenbedingungen findet diese Veranstaltung ausschließlich online über Zoom statt.

Es besteht die Möglichkeit, per Zoom teilzunehmen:

Meeting-ID: 878 0410 0238
Kenncode: VortragMK

Interessenten sind herzlich eingeladen.


The data deluge that is the effect of a phenomenon known as “big data” calls for solutions to make use of data and to gain insights from data. Accordingly, the research questions that guided this research were twofold: In what ways can value be created from big data? And how can knowledge emergence be engineered in information systems?

Methodologically, this research was structured according to design-oriented information systems research (Österle et al. 2010). In the research approach, seven research projects were carried out and led to thirteen peer-reviewed international publications. The contributions encompass information system designs, data analysis methods, software prototypes, reference models, empirical experiments and testing of social science hypotheses. The conclusions drawn from this research propose the following answers to the research questions.

Firstly, value creation from data can be achieved in the following ways: providing a positive impact on decision support by automatically finding and visualizing associative patterns in data; making knowledge work more effective by combining knowledge representation and extraction from text data to visualize knowledge networks, and by automatically discovering concepts and associations in large corpora of unstructured data; implementing an iterative, knowledge-based effectuation of interactive business-aligned analytics on scalable data technology; and widening the scope of scientific research by analyzing social big data.

Secondly, the emergence of knowledge in information systems can be engineered with the following approaches: facilitating human interpretation of data analysis with automated interpretable data visualization; enabling emergent social semantics semi-automatically and interactively in a knowledge management system; iterative socialized factual and procedural knowledge construction from big data applications in socio-technical cognitive systems; clarifying the principles for an epistemology of big data; and facilitating human interpretation of large text corpora by entity and relationship extraction.

Based on the insights of this research, the following further research opportunities can be recognized: data value theory; sustainable data management; knowledge-oriented information systems; ontological socio-technical constructivism; and a foundation for establishing generally accepted ways of knowing in data science.

mathinf.webteam | 12.11.2020