SFDL: MVC Applied to Workflow Design

Process management based on workflow systems is a growing trend in collaborative environments. One of the most notorious areas of improvement is that of user interfaces, especially since business process definition languages do not address efficiently the point of contact between workflow engines and human interactions. With that in focus, we propose the MVC pattern design to workflow systems. To accomplish this, we have designed a new dynamic view definition language called SFDL, oriented towards the easy interoperability with the different workflow definition languages, while maintaining enough flexibility to be represented in different formats and being adaptable to several environments. To validate our approach, we have carried out an implementation in a real banking scenario, which has provided continuous feedback and enabled us to refine the proposal. The work is fully based on widely accepted and used web standards (XML, YAML, JSON, Atom and REST). Some guidelines are given to facilitate the adoption of our solution

Cloud Process Execution Engine - Evaluation of the Core Concepts

In this technical report we describe describe the Domain Specific Language (DSL) of the Workflow Execution Execution (WEE). Instead of interpreting an XML based workflow description language like BPEL, the WEE uses a minimized but expressive set of statements that runs directly on to of a virtual machine that supports the Ruby language.Frameworks/Virtual Machines supporting supporting this language include Java, .NET and there exists also a standalone Virtual Machine. Using a DSL gives us the advantage of maintaining a very compact code base of under 400 lines of code, as the host programming language implements all the concepts like parallelism, threads, checking for syntactic correctness. The implementation just hooks into existing statements to keep track of the workflow and deliver information about current existing context variables and state to the environment that embeds WEE

Patterns-based Evaluation of Open Source BPM Systems: The Cases of jBPM, OpenWFE, and Enhydra Shark

In keeping with the proliferation of free software development initiatives and the increased interest in the business process management domain, many open source workflow and business process management systems have appeared during the last few years and are now under active development. This upsurge gives rise to two important questions: what are the capabilities of these systems? and how do they compare to each other and to their closed source counterparts? i.e. in other words what is the state-of-the-art in the area?. To gain an insight into the area, we have conducted an in-depth analysis of three of the major open source workflow management systems - jBPM, OpenWFE and Enhydra Shark, the results of which are reported here. This analysis is based on the workflow patterns framework and provides a continuation of the series of evaluations performed using the same framework on closed source systems, business process modeling languages and web-service composition standards. The results from evaluations of the three open source systems are compared with each other and also with the results from evaluations of three representative closed source systems - Staffware, WebSphere MQ and Oracle BPEL PM, documented in earlier works. The overall conclusion is that open source systems are targeted more toward developers rather than business analysts. They generally provide less support for the patterns than closed source systems, particularly with respect to the resource perspective which describes the various ways in which work is distributed amongst business users and managed through to completion

ASaiM: A Galaxy-based framework to analyze microbiota data

Background: New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies. Findings: We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io). Conclusions: Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable

Generating eScience Workflows from Statistical Analysis of Prior Data

A number of workflow design tools have been developed specifically to enable easy graphical specification of workflows that ensure systematic scientific data capture and analysis and precise provenance information. We believe that an important component that is missing from these existing workflow specification and enactment systems is integration with tools that enable prior detailed analysis of the existing data - and in particular statistical analysis. By thoroughly analyzing the existing relevant datasets first, it is possible to determine precisely where the existing data is sparse or insufficient and what further experimentation is required. Introducing statistical analysis to experimental design will reduce duplication and costs associated with fruitless experimentation and maximize opportunities for scientific breakthroughs. In this paper we describe a workflow specification system that we have developed for a particular eScience application (fuel cell optimization). Experimental workflow instances are generated as a result of detailed statistical analysis and interactive exploration of the existing datasets. This is carried out through a graphical data exploration interface that integrates the widely-used open source statistical analysis software package, R, as a web service

Scientific Models: A User-oriented Approach to the Integration of Scientific Data and Digital Libraries

Many scientific communities are struggling with the challenge of how to manage the terabytes of data they are producing, often on a daily basis. Scientific models are the primary method for representing and encapsulating expert knowledge in many disciplines. Scientific models could also provide a mechanism: for publishing and sharing scientific results; for teaching complex scientific concepts; and for the selective archival, curation and preservation of scientific data. As such, they also provide a bridge for collaboration between Digital Libraries and eScience. In this paper I describe research being undertaken within the FUSION project at the University of Queensland to enable scientists to construct, publish and manage scientific model packages that encapsulate and relate the raw data to its associated contextual and provenance metadata, processing steps, derived information and publications. This work involves extending tools and services that have come out of the Digital Libraries domain to support e-Science requirements

Scientific Publication Packages: A Selective Approach to the Communication and Archival of Scientific Output

The use of digital technologies within research has led to a proliferation of data, many new forms of research output and new modes of presentation and analysis. Many scientific communities are struggling with the challenge of how to manage the terabytes of data and new forms of output, they are producing. They are also under increasing pressure from funding organizations to publish their raw data, in addition to their traditional publications, in open archives. In this paper I describe an approach that involves the selective encapsulation of raw data, derived products, algorithms, software and textual publications within "scientific publication packages". Such packages provide an ideal method for: encapsulating expert knowledge; for publishing and sharing scientific process and results; for teaching complex scientific concepts; and for the selective archival, curation and preservation of scientific data and output. They also provide a bridge between technological advances in the Digital Libraries and eScience domains. In particular, I describe the RDF-based architecture that we are adopting to enable scientists to construct, publish and manage "scientific publication packages" - compound digital objects that encapsulate and relate the raw data to its derived products, publications and the associated contextual, provenance and administrative metadata

Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome

How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory