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Institute of Parallel and Distributed Systems, University of Stuttgart, Universitätsstr. 38, 70569 Stuttgart, Germany

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2 Papers

SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis

Topics: Industrial Applications of Artificial Intelligence; HCI on Enterprise Information Systems; Data Mining; Deep Learning; Machine Learning in HCI

Authors: Michael Behringer , Dennis Treder-Tschechlov , Julius Voggesberger , Pascal Hirmer and Bernhard Mitschang

Affiliation: Institute of Parallel and Distributed Systems, University of Stuttgart, Universitätsstr. 38, D-70569 Stuttgart, Germany

Digital Object Identifier: 10.5220/0011998300003467

Proceedings of ICEIS 2023, ISBN: 978-989-758-648-4

Quick Abstract

Today, data analytics is widely used throughout many domains to identify new trends, opportunities, or risks and improve decision-making. By doing so, various heterogeneous data sources must be selected to form the foundation for knowledge discovery driven by data analytics. However, discovering and selecting the suitable and valuable data sources to improve the analytics results is a great challenge. Domain experts can easily become overwhelmed in the data selection process due to a large amount of available data sources that might contain similar kinds of information. Supporting domain experts in discovering and selecting the best suitable data sources can save time, costs and significantly increase the quality of the analytics results. In this paper, we introduce a novel approach – SDRank – which provides a Deep Learning approach to rank data sources based on their similarity to already selected data sources. We implemented SDRank, trained various models on 4 860 datasets, and measured the achieved precision for evaluation purposes. By doing so, we showed that SDRank is able to highly improve the workflow of domain experts to select beneficial data sources.

Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features

Topics: HCI on Enterprise Information Systems; Data Mining; User Experience

Authors: Michael Behringer , Pascal Hirmer , Dennis Tschechlov and Bernhard Mitschang

Affiliation: Institute of Parallel and Distributed Systems, University of Stuttgart, Universitätsstr. 38, 70569 Stuttgart, Germany

Digital Object Identifier: 10.5220/0011092000003179

Proceedings of ICEIS 2022, ISBN: 978-989-758-569-2

Quick Abstract

Today, the amount of data is growing rapidly, which makes it nearly impossible for human analysts to comprehend the data or to extract any knowledge from it. To cope with this, as part of the knowledge discovery process, many different data mining and machine learning techniques were developed in the past. A famous representative of such techniques is clustering, which allows the identification of different groups of data (the clusters) based on data characteristics. These algorithms need no prior knowledge or configuration, which makes them easy to use, but interpreting and explaining the results can become very difficult for domain experts. Even though different kinds of visualizations for clustering results exist, they do not offer enough details for explaining how the algorithms reached their results. In this paper, we propose a new approach to increase explainability for clustering algorithms. Our approach identifies and selects features that are most meaningful for the clustering result. We conducted a comprehensive evaluation in which, based on 216 synthetic datasets, we first examined various dispersion metrics regarding their suitability to identify meaningful features and we evaluated the achieved precision with respect to different data characteristics. This evaluation shows, that our approach outperforms existing algorithms in 93 percent of the examined datasets.