Auflistung nach Autor:in "Keller, Fabian"
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- ZeitschriftenartikelALEA – Adaptive eLEArning System – Lernende datenbankbasierte Lernsysteme in der Datenbanklehre(Datenbank-Spektrum: Vol. 21, No. 2, 2021) Schneider, Kerstin; Keller, Fabian; Habekost, Pia; Schmeil, Victor; Dampmann, Markus; Schorch, FlorianIm Bereich Datenbanken werden selbstentwickelte E‑Learning-Systeme an vielen Hochschulen seit Jahren erfolgreich verwendet. An der Hochschule Harz werden E‑Learning-Systeme im Bereich Datenbanken im Rahmen der Lehre und für die Lehre entwickelt, weiterentwickelt und eingesetzt. Das Gesamtsystem, welches die zusammengehörenden Systeme umfasst, wird als ALEA bezeichnet. Es werden relevante Komponenten von ALEA erläutert, die im Rahmen der Datenbanklehre für die klassischen Teilgebiete SQL, ER-to-Relational-Mapping und Normalisierung genutzt werden.
- ZeitschriftenartikelLeveraging Palladio for Performance Awareness in the IETS 3 Integrated Specification Environment(Softwaretechnik-Trends Band 36, Heft 4, 2016) Keller, Fabian; Völter, Markus; van Hoorn, André; Birken, KlausPerformance is an important concern when designing and implementing software-intensive systems. Various techniques are available for specifying and evaluating performance concerns throughout the system Performance is an important concern when designing and implementing software-intensive systems. Various techniques are available for specifying and evaluating performance concerns throughout the system life-cycle. However, there is a gap in terms of tooling when moving between requirements, design, and implementation artifacts. We address this gap by integrating simulation-based and analytical performance prediction tools into IETS3 — an integrated specification environment for technical software systems based on the JetBrains MPS language workbench. In this paper, we provide an overview of our work in progress on integrating performance awareness support into the IETS3 editor and user interface. We leverage Palladio’s prediction infrastructure by transforming to Palladio’s modeling language to obtain performance predictions, which are then fed back into the IETS3 user interface. The approach yields a tight integration of the requirements and the design of a system strengthened by a real-time feedback loop.b life-cycle. However, there is a gap in terms of tooling when moving between requirements, design, and implementation artifacts. We address this gap by integrating simulation-based and analytical performance prediction tools into IETS3 — an integrated specification environment for technical software systems based on the JetBrains MPS language workbench. In this paper, we provide an overview of our work in progress on integrating performance awareness support into the IETS3 editor and user interface. We leverage Palladio’s prediction infrastructure by transforming to Palladio’s modeling language to obtain performance predictions, which are then fed back into the IETS3 user interface. The approach yields a tight integration of the requirements and the design of a system strengthened by a real-time feedback loop.