Datasets

Source: SAGA random generator

Randomly generated task graphs with a converging (fan-in) tree structure. Tasks flow from multiple sources to a single sink. Generated with 2-4 levels and branching factor 2-3.

Source: SAGA random generator

Randomly generated task graphs with a diverging (fan-out) tree structure. Tasks flow from a single source to multiple sinks. Generated with 2-4 levels and branching factor 2-3.

Source: SAGA random generator

Task graphs consisting of multiple parallel chains connected by a super-source and super-sink. Generated with 2-5 chains of length 2-5.

Source: WFCommons

Basic Local Alignment Search Tool workflows for sequence alignment. One of the most widely-used bioinformatics tools.

Source: WFCommons

Burrows-Wheeler Aligner workflows for mapping sequencing reads to a reference genome.

Source: WFCommons

Agroecosystem modeling workflows that simulate crop growth and soil processes.

Source: WFCommons

Epigenomics analysis workflows for studying chemical modifications to DNA and histone proteins.

Source: WFCommons

Genomics workflows from the 1000 Genomes Project for identifying genetic variants across human populations.

Source: WFCommons

Astronomical image mosaicking workflows that combine multiple sky survey images into seamless composites.

Source: WFCommons

Seismic data processing workflows for analyzing earthquake data.

Source: WFCommons

Soybean Knowledge Base workflows for genomic analysis of soybean varieties.

Source: WFCommons

Sequence Read Archive search workflows for querying large genomic databases.

Source: RIOTBench

Extract-Transform-Load workflows for IoT data processing in edge/fog computing environments.

Source: RIOTBench

Machine learning prediction workflows for real-time IoT data inference pipelines.

Source: RIOTBench

Statistical analysis workflows for IoT sensor data streams.

Source: RIOTBench

Machine learning training workflows for IoT data across edge, fog, and cloud nodes.