scholarly journals Compi: a framework for portable and reproducible pipelines

2021 ◽  
Vol 7 ◽  
pp. e593
Author(s):  
Hugo López-Fernández ◽  
Osvaldo Graña-Castro ◽  
Alba Nogueira-Rodríguez ◽  
Miguel Reboiro-Jato ◽  
Daniel Glez-Peña
Keyword(s):  
Job Scheduling ◽  
Workflow Engine ◽  
Source Distribution ◽  
Application Framework ◽  
End User ◽  
Command Line Interface ◽  
Link Type ◽  
Specific Parameter ◽  
Interface Generation ◽  
User Interface Generation

Compi is an application framework to develop end-user, pipeline-based applications with a primary emphasis on: (i) user interface generation, by automatically generating a command-line interface based on the pipeline specific parameter definitions; (ii) application packaging, with compi-dk, which is a version-control-friendly tool to package the pipeline application and its dependencies into a Docker image; and (iii) application distribution provided through a public repository of Compi pipelines, named Compi Hub, which allows users to discover, browse and reuse them easily. By addressing these three aspects, Compi goes beyond traditional workflow engines, having been specially designed for researchers who want to take advantage of common workflow engine features (such as automatic job scheduling or logging, among others) while keeping the simplicity and readability of shell scripts without the need to learn a new programming language. Here we discuss the design of various pipelines developed with Compi to describe its main functionalities, as well as to highlight the similarities and differences with similar tools that are available. An open-source distribution under the Apache 2.0 License is available from GitHub (available at https://github.com/sing-group/compi). Documentation and installers are available from https://www.sing-group.org/compi. A specific repository for Compi pipelines is available from Compi Hub (available at https://www.sing-group.org/compihub.

SLA Monitoring of Presence-Enabled Services

2011 ◽  
pp. 307-322
Author(s):  
Tapati Bandopadhyay ◽  
Pradeep Kumar
Keyword(s):  
Service Providers ◽  
Instant Messaging ◽  
Service Level Agreement ◽  
Service Level ◽  
Application Framework ◽  
End User ◽  
New Approach ◽  
Clear Guideline ◽  
Evaluation Measurement ◽  
Monitoring Service

The concept of presence was initially associated with an instant messaging service, allowing an end user to recognize the presence of a peer online to send or receive messages. Now the technology has grown up to include various services like monitoring performance of any type of end user device, and services are accessible from anywhere, any time. The need for enhanced value remains the driving force behind these services, for example, Voice over Internet Protocol (VoIP) services, which is drawing tremendous research interest in services performance evaluation, measurement, benchmarking, and monitoring. Monitoring service level parameters happens to be one of the most interesting application-oriented research issues because various service consumers at the customer companies/end users’ level are finding it very difficult to design and monitor an effective SLA (Service Level Agreement) with the presence-enabled service providers. This chapter focuses on to these specific issues and presents a new approach of SLA monitoring through Data Envelopment Analysis (DEA). This extreme point approach actually can work much better in the context of SLA monitoring than general central-tendency-based statistical tools, a fact which has been corroborated by similar application examples of DEA presented in this chapter and has therefore it acts as the primary motivation to propose this new approach. Towards this end, this chapter first builds up the context of presence-enabled services (Day, Rosenburg, & Sugano, 2000), its SLA and SLA parameters, and the monitoring requirements. Then it explains the basics of DEA and its application in various other engineering and services context. Ultimately, a DEA application framework for monitoring an SLA of presence-enabled services is proposed which can serve as a clear guideline for the customers of presence-enabled services, not only for SLA monitoring but also at various other stages of implementing presence-enabled services frameworks. This approach exploits the definitive suitability of the application of DEA methods to presence-enabled service monitoring problems, and can be easily implemented by the industry practitioners.

Comparing end-user and intelligent remote control interface generation

2005 ◽  
Vol 10 (2-3) ◽  
pp. 136-143 ◽  
Author(s):  
Olufisayo Omojokun ◽  
Jeffrey S. Pierce ◽  
Charles L. Isbell ◽  
Prasun Dewan
Keyword(s):  
Remote Control ◽  
End User ◽  
Control Interface ◽  
Interface Generation

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

2018 ◽  
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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