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Auflistung nach Autor:in "Schalles, Christian"

1 - 4 von 4
  • Konferenzbeitrag
    Exploring usability-driven differences of graphical modeling languages: an empirical research report
    (Modellierung 2012, 2012) Schalles, Christian; Creagh, John; Rebstock, Michael
    Documenting, specifying and analyzing complex domains such as information systems or business processes have become unimaginable without the support of graphical models. Generally, models are developed using graph-oriented languages such as Event Driven Process Chains (EPCs) or diagrams of the Unified Modeling Language (UML). For industrial use, modeling languages aim to describe either information systems or business processes. Heterogeneous modeling languages allow different grades of usability to their users. In our paper we focus on an evaluation of four heterogeneous modeling languages and their different impact on user performance and user satisfaction. We deduce implications for both educational and industrial use using the Framework for Usability Evaluation of Modeling Languages (FUEML).
  • Konferenzbeitrag
    Ein generischer Ansatz zur Messung der Benutzerfreundlichkeit von Modellierungssprachen
    (Modellierung 2010, 2010) Schalles, Christian; Rebstock, Michael; Creagh, John
    Eine Ermittlung der Benutzerfreundlichkeit im Sinne der Usability von Modellierungssprachen war bisher nicht Zielsetzung empirischer Evaluationsstudien. In den meisten Usabilitystudien wurden und werden Applikationen, Webseiten und technische Produkte evaluiert. Ziel dieses Beitrags ist die Schaffung eines Rahmenkonzeptes zur Bewertung der Usability von Modellierungssprachen. Es ist als Beitrag zu verstehen, der die komplexen Zusammenhänge einer Usabilitystudie für Modellierungssprachen erarbeitet und eine Grundlage für daran anknüpfende empirische Untersuchungen schafft.
  • Konferenzbeitrag
    SportsTables: A new Corpus for Semantic Type Detection
    (BTW 2023, 2023) Langenecker, Sven; Sturm, Christoph; Schalles, Christian; Binnig, Carsten
    Table corpora such as VizNet or TURL which contain annotated semantic types per column are important to build machine learning models for the task of automatic semantic type detection. However, there is a huge discrepancy between corpora that are used for training and testing since real-world data lakes contain a huge fraction of numerical data which are not present in existing corpora. Hence, in this paper, we introduce a new corpus that contains a much higher proportion of numerical columns than existing corpora. To reflect the distribution in real-world data lakes, our corpus SportsTables has on average approx. 86% numerical columns, posing new challenges to existing semantic type detection models which have mainly targeted non-numerical columns so far. To demonstrate this effect, we show the results of a first study using a state-of-the-art approach for semantic type detection on our new corpus and demonstrate significant performance differences in predicting semantic types for textual and numerical data.
  • Textdokument
    Towards Learned Metadata Extraction for Data Lakes
    (BTW 2021, 2021) Langenecker, Sven; Sturm, Christoph; Schalles, Christian; Binnig, Carsten
    An important task for enabling the efficient exploration of available data in a data lake is to annotate semantic type information to the available data sources. In order to reduce the manual overhead of annotation, learned approaches for automatic metadata extraction on structured data sources have been proposed recently. While initial results of these learned approaches seem promising, it is still not clear how well these approaches can generalize to new unseen data in real-world data lakes. In this paper, we aim to tackle this question and as a first contribution show the result of a study when applying Sato -a recent approach based on deep learning -to a real-world data set. In our study we show that Sato is only able to extract semantic data types for about 10% of the columns of the real-world data set. These results show the general limitation of deep learning approaches which often provide near-perfect performance on available training and testing data but fail in real settings since training data and real data often strongly vary. Hence, as a second contribution we propose a new direction of using weak supervision and present results of an initial prototype we built to generate labeled training data with low manual efforts to improve the performance of learned semantic type extraction approaches on new unseen data sets.