Description
Like other software, DAWs may show unexpected behavior or even crash due to various reasons. Debugging aims at establishing a cause effect relationship between the observable problem and the actual error. Such error identification serves as an initial step of a reliable problem resolution, and thus debugging of DAWs is an indispensable task to increase the dependability of DAWs. However, debugging DAWs is particularly challenging due to the heterogeneous nature of the involved tasks and the distributed nature of the execution engine. The central research question addressed in this subproject is how to enable domain scientists to efficiently formulate, test, and refine a debugging hypothesis in the context of scientific software engineering. It will primarily work together with A3 on the adaptation of software test technologies to distributed DAWs and with B6 on the distributed monitoring of DAW executions. The subproject will be coordinated by Prof. Kehrer, an expert in model-based software development, and Prof. Markl, an expert in large-scale distributed data analytics.
PIs
Publications
2022
Jonathan Bader; Joel Witzke; Soeren Becker; Ansgar Lößer; Fabian Lehmann; Leon Doehler; Anh Duc Vu; Odej Kao
Towards Advanced Monitoring for Scientific Workflows Inproceedings
In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), IEEE, 2022.
@inproceedings{bader2022towards,
title = {Towards Advanced Monitoring for Scientific Workflows},
author = {Jonathan Bader and Joel Witzke and Soeren Becker and Ansgar Lößer and Fabian Lehmann and Leon Doehler and Anh Duc Vu and Odej Kao},
url = {https://arxiv.org/pdf/2211.12744.pdf},
year = {2022},
date = {2022-12-20},
urldate = {2022-12-20},
booktitle = {2022 IEEE International Conference on Big Data (IEEE BigData 2022)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Anh Duc Vu; Timo Kehrer; Christos Tsigkanos
Outcome-Preserving Input Reduction for Scientific Data Analysis Workflows Inproceedings Forthcoming
In: Forthcoming.
@inproceedings{vu2022outcome,
title = {Outcome-Preserving Input Reduction for Scientific Data Analysis Workflows},
author = {Anh Duc Vu and Timo Kehrer and Christos Tsigkanos},
year = {2022},
date = {2022-10-13},
urldate = {2022-10-13},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Gábor E. Gévay; Tilmann Rabl; Sebastian Breß; Loránd Madai-Tahy; Jorge-Arnulfo Quiané-Ruiz; Volker Markl
Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds? Journal Article
In: ACM SIGMOD Record, vol. 51, no. 1, pp. 1-8, 2022.
@article{noauthororeditorc,
title = {Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds?},
author = {Gábor E. Gévay and Tilmann Rabl and Sebastian Breß and Loránd Madai-Tahy and Jorge-Arnulfo Quiané-Ruiz and Volker Markl},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-01},
journal = {ACM SIGMOD Record},
volume = {51},
number = {1},
pages = {1-8},
abstract = {Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.
Muhammad Imran; Gábor E. Gévay; Jorge-Arnulfo Quiané-Ruiz; Volker Markl
Fast datalog evaluation for batch and stream graph processing Journal Article
In: World Wide Web, vol. 25, pp. 971-1003, 2022.
@article{Imran2022FastDE,
title = {Fast datalog evaluation for batch and stream graph processing},
author = {Muhammad Imran and Gábor E. Gévay and Jorge-Arnulfo Quiané-Ruiz and Volker Markl},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {World Wide Web},
volume = {25},
pages = {971-1003},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sebastian Müller; Valentin Gogoll; Anh Duc Vu; Timo Kehrer; Lars Grunske
Automatically finding Metamorphic Relations in Computational Material Science Parsers Inproceedings
In: 2022 IEEE 18th International Conference on e-Science (e-Science), pp. 521-528, 2022.
@inproceedings{9973609,
title = {Automatically finding Metamorphic Relations in Computational Material Science Parsers},
author = {Sebastian Müller and Valentin Gogoll and Anh Duc Vu and Timo Kehrer and Lars Grunske},
doi = {10.1109/eScience55777.2022.00092},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE 18th International Conference on e-Science (e-Science)},
pages = {521-528},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
