A case study on software ecosystem characteristics in industrial automation software

In the domain of industrial automation companies nowadays need to serve a mass market while at the same time customers demand highly customized solutions. To tackle this problem, companies frequently define software product lines (SPLs), which allow to automatically derive and further customize individual solutions based on a common platform. SPLs rely on defining common and variable platform features together with mappings, which define how the features are realized in implementation artifacts. In concurrent engineering such a feature-oriented process is challenged by the evolution of features, the complexity of feature-to-artifact mappings, and the diversity of the implementation artifacts. To address these challenges this paper introduces an approach supporting feature-oriented development and evolution in industrial SPLs. We outline the key elements and operations of our approach, including an implementation in a development environment. We report results of evaluating our approach regarding functional correctness, usefulness, and scalability based on a case study of a Pick-and-Place Unit (PPU) and an industrial case system.

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BioKEEN: a library for learning and evaluating biological knowledge graph embeddings

Bioinformatics ◽

10.1093/bioinformatics/btz117 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3538-3540 ◽

Cited By ~ 8

Author(s):

Mehdi Ali ◽

Charles Tapley Hoyt ◽

Daniel Domingo-Fernández ◽

Jens Lehmann ◽

Hajira Jabeen

Keyword(s):

Supplementary Information ◽

Knowledge Graph ◽

Biological Knowledge ◽

Command Line ◽

Graph Embeddings ◽

Command Line Interface ◽

Software Ecosystem ◽

Mapping Resource ◽

Significant Attention

Abstract Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies. Availability and implementation BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN Supplementary information Supplementary data are available at Bioinformatics online.

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Open source initiatives as basis for the establishment of new technologies in industrial automation: 4DIAC a case study

2010 IEEE International Symposium on Industrial Electronics ◽

10.1109/isie.2010.5637502 ◽

2010 ◽

Cited By ~ 39

Author(s):

Alois Zoitl ◽

Thomas Strasser ◽

Antonio Valentini

Keyword(s):

Open Source ◽

New Technologies ◽

Industrial Automation

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BioKEEN: A library for learning and evaluating biological knowledge graph embeddings

10.1101/475202 ◽

2018 ◽

Cited By ~ 3

Author(s):

Mehdi Ali ◽

Charles Tapley Hoyt ◽

Daniel Domingo-Fernández ◽

Jens Lehmann ◽

Hajira Jabeen

Keyword(s):

Knowledge Graph ◽

Biological Knowledge ◽

Command Line ◽

Graph Embeddings ◽

Command Line Interface ◽

Software Ecosystem ◽

Link Type ◽

Mapping Resource ◽

Significant Attention

AbstractKnowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programming and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.AvailabilityBioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN as well as through PyPI.

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Phylogenetic analysis of software evolution, a case study: VE&MINT

10.7287/peerj.preprints.27159v1 ◽

2018 ◽

Author(s):

Alvaro Ortiz-Troncoso

Keyword(s):

Phylogenetic Analysis ◽

Life Cycle ◽

Open Source ◽

Software Evolution ◽

Real Life ◽

Traditional View ◽

Software Ecosystem ◽

Life Project ◽

Project Governance

Open source projects may face a forking situation at some point during their life-cycle. The traditional view is that forks are a waste of project resources and should be avoided. However, in a wider technological and organisational context, forks can be a way to foster the creation of a software ecosystem. Either way, forking is explicitly allowed by open source licenses. Notwithstanding, methods for quantifying the evolution of forks are currently scarce. The present work attempts to answer the question whether a real-life project has forked. It does so by considering code and organisational characteristics of the project, and analysing these characteristics by applying methods ported from biological phylogenetics. After finding that the project is forked, implications for project governance are discussed.

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Optimization of User and System Knowledge

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128803200537 ◽

1988 ◽

Vol 32 (5) ◽

pp. 399-403

Author(s):

Ann G. Hammer ◽

Gerald G. Birdwell ◽

Harry L. Snyder ◽

R. H. Bogle

Keyword(s):

User Interface ◽

Software Environment ◽

Industrial Automation ◽

Context Sensitive ◽

System Components ◽

Integrated Software ◽

System Effectiveness ◽

System Knowledge

This paper presents the perspective of a user system knowledge continuum which recasts traditional user system components (user interface, context-sensitive help, completion aids, manuals, training) as interrelated knowledge components tasked with appropriately distributing required knowledge between user and system. It suggests that maximizing user system effectiveness is best viewed as optimization of a set of such knowledge components. The paper relies upon a case study showing this perspective at work in the development of APT - Applications Productivity Tool™, an integrated software environment for industrial automation applications.

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