Hermann Stolte

Hermann Stolte
Humboldt-Universität zu Berlin | HU Berlin · Department of Computer Science

M.Sc. Computer Science

About

5
Publications
1,426
Reads
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11
Citations
Citations since 2017
3 Research Items
11 Citations
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0

Publications

Publications (5)
Conference Paper
Full-text available
Exploratory data analysis is widespread across many scientific domains. It relies on complex pipelines and computational models for data processing, that are commonly designed collaboratively by scientists with diverse backgrounds for a variety of software stacks and computation environments. Here, a major challenge is the uncertainty about the cor...
Conference Paper
Transformative technologies are invading our lives ubiquitously. The citizens of the future will no longer be impacted by technology only in a passive way; they would need to transform and further develop it, actively. However, this will not be possible if the awareness of the importance to educate young generations in their future roles of active...
Conference Paper
Full-text available
This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play Store in a related work. Fine-granular opinions and the classification of their sentiment orientation were already...

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Projects

Projects (2)
Project
Exploratory data analysis is widespread across many scientific domains. It relies on complex pipelines and computational models for data processing, that are commonly designed collaboratively by scientists with diverse backgrounds for a variety of software stacks and computation environments. Here, a major challenge is the uncertainty about the correctness of analysis results, due to the high complexity of both, the actual data and the implemented analysis steps, and the continuous reuse and adaptation of data analysis pipelines in different application settings. This PhD project investigates how the design, adaptation, and evaluation of exploratory data analysis pipelines can be supported through automated plausibility assessment. To this end, we outline the requirements, our approach, and initial results for models and methods to enable plausibility checking in the context of exploratory data analysis.
Archived project
Paper submitted to a workshop on semantic information retrieval.