Subdue

Ketkar, Nikhil; Holder, Lawrence B.; Cook, Diane J.

doi:10.1145/1133905.1133915

Cited by 70 publications

(19 citation statements)

References 21 publications

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“…To identify subgraphs, we used the software package Subdue: Graph-Based Knowledge Discovery from Washington State University. It finds subgraphs most prevalent in a set of graphs by way of compressing the graph data (Ketkar et al, 2005).…”

Section: Unknown Hazard Identification Methodologymentioning

confidence: 99%

Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

McIntire

Hoyle

Tumer

et al. 2016

AIEDAM

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Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

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Section: Unknown Hazard Identification Methodologymentioning

confidence: 99%

Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

McIntire

Hoyle

Tumer

et al. 2016

AIEDAM

View full text Add to dashboard Cite

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“…An overview of frequent subgraph mining algorithms can be found in the literature (Jiang et al 2013). A general introduction to graph mining is given by Cook and Holder (2006), who also proposed a compression-based subgraph miner called SubDue (Ketkar et al 2005). SubDue has also been one of our main inspirations for a compression-based approach.…”

Section: Frequent Subgraph Miningmentioning

confidence: 99%

“…To rule out any effects due to approximate mining, we considered only exact miners. Therefore, we also could not use SubDue (Ketkar et al 2005), which directly tries to optimize compression. Furthermore, SubDue was not able to discover both edit operations in the second experiment (see Sect.…”

Section: Approachmentioning

confidence: 99%

“…We used the compression-based ranking since it is arguably superior to the purely frequencybased ranking, as we have seen empirically in the first two experiments. Furthermore, there is also more evidence for this choice in the literature (Ketkar et al 2005;Bariatti et al 2020). The edit operations were presented in the visual transformation language of henShin, which we introduced to our participants before.…”

Section: Experiments Setupmentioning

confidence: 99%

See 1 more Smart Citation

Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling

et al. 2023

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Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.

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“…Secondly, FSM algorithms to find frequent subgraphs in a single large connected graph are Apriori-based approaches [3], gSpan based approaches [1,2] and SUBDUE [5]. The Apriori-based approaches would not be efficient methods for a single large connected graph since it requires a huge runtime to check isomorphism among candidate sets.…”

Section: Frequent Pattern Extraction Of Standard Cells Combination Bymentioning

confidence: 99%

New library development method by FSM based cell pattern extraction

Kim

Han

Yang

et al. 2018

IEICE Electron. Express

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The performance, power and area optimization with respect to an implementation cost is a fundamental problem in digital circuit design. With a custom design approach, finding patterns of frequent cell combinations can inspire the new cell development and optimization. A FSM (Frequent Subgraph Mining) method can help to develop new cell libraries, however, this requires huge engineering efforts for finding feasible operation conditions. This paper presents an optimized frequent subgraph mining platform by integrating various FSM methods. The experiment results with various designs demonstrate that the proposed method can reduce an overall runtime.

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Subdue

Cited by 70 publications

References 21 publications

Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems

Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling

New library development method by FSM based cell pattern extraction

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