Discovering Similar Workflows via Provenance Clustering: A Case Study

Alawini, Abdussalam; Chen, Leshang; Davidson, Susan B.; Fisher, Stephen; Kim, Junhyong

doi:10.1007/978-3-319-98379-0_9

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, such work explicitly focuses on the detection of attacks on the system [8], uses bespoke projections from graphs to vector spaces [20,21] or only analyzes individual characteristics of the computation process [41]. Other approaches aim to compare provenance graphs by summarizing via clustering [2,31].…”

Section: Related Workmentioning

confidence: 99%

Towards Specificationless Monitoring of Provenance-Emitting Systems

Stoffers

Weinert

2022

Runtime Verification

View full text Add to dashboard Cite

Monitoring often requires insight into the monitored system as well as concrete specications of expected behavior. More and more systems, however, provide information about their inner procedures by emitting provenance information in a W3C-standardized graph format. In this work, we present an approach to monitor such provenance data for anomalous behavior by performing spectral graph analysis on slices of the constructed provenance graph and by comparing the characteristics of each slice with those of a sliding window over recently seen slices. We argue that this approach not only simplies the monitoring of heterogeneous distributed systems, but also enables applying a host of well-studied techniques to monitor such systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards Specificationless Monitoring of Provenance-Emitting Systems

Stoffers

Weinert

2022

Runtime Verification

View full text Add to dashboard Cite

show abstract

Ontology of RNA Sequencing (ORNASEQ) and its application

Fisher

Kim

2018

Preprint

Self Cite

View full text Add to dashboard Cite

21Background: Next-generation RNA sequencing is a rapidly developing technology 22 with complex procedures encompassing different experimental modalities. As the 23 technology evolves and its use expand, so does the need to capture the data 24 provenance from these sequencing studies and the need to create new tools to 25 manage and manipulate these provenance stores. 26 27 Results: Here we used the Ontology for Biomedical Investigations (OBI) and many 28 other ontologies from the Open Biological and Biomedical Ontology (OBO) Foundry 29 as a framework from which to create an application ontology (ORNASEQ: Ontology 30 of RNA sequencing) to capture data provenance for next-generation RNA 31 sequencing studies. Additionally, we provide an extensive real-life sample 32 provenance data set for use in developing new provenance tools and additional 33 sequencing data models. 34 35 Conclusions: The Ontology of RNA Sequencing (ORNASEQ) provides core terms for 36 use in building data models to capture the provenance from next-generation RNA 37 sequencing studies. The supplied sample provenance data also exemplifies many of 38 the complexities of RNA sequencing studies and underscores the need for potent 39 workflow management systems. 40 Keywords 41 Ontology, RNAseq, PROV-XML 42 Fisher, Kim 3 43 Background 44 Until recently the cost of performing next-generation RNA sequencing (RNAseq) 45 experiments limited the amount of data generated by a single lab and managing and 46 properly documenting a few experiments was not fundamentally challenging. 47 Ontology Number Terms BFO: Basic Formal Ontology

show abstract