More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

Workflows and e-Science: An overview of workflow system features and capabilities

As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers I review current approaches to these issues, including virtual machines and workflow systems, and their limitations I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges I illustrate this with several examples of Docker use with a focus on the R statistical environment

An introduction to Docker for reproducible research

Pegasus, a workflow management system for science automation

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

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The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

Most scientists conduct analyses and run models in several different software and hardware environments, mentally coordinating the export and import of data from one environment to another. The Kepler scientific workflow system provides domain scientists with an easy-to-use yet powerful system for capturing scientific workflows (SWFs). SWFs are a formalization of the ad-hoc process that a scientist may go through to get from raw data to publishable results. Kepler attempts to streamline the workflow creation and execution process so that scientists can design, execute, monitor, re-run, and communicate analytical procedures repeatedly with minimal effort. Kepler is unique in that it seamlessly combines high-level workflow design with execution and runtime interaction, access to local and remote data, and local and remote service invocation. SWFs are superficially similar to business process workflows but have several challenges not present in the business workflow scenario. For example, they often operate on large, complex and heterogeneous data, can be computationally intensive and produce complex derived data products that may be archived for use in reparameterized runs or other workflows. Moreover, unlike business workflows, SWFs are often dataflow-oriented as witnessed by a number of recent academic systems (e.g., DiscoveryNet, Taverna and Triana) and commercial systems (Scitegic/Pipeline-Pilot, Inforsense). In a sense, SWFs are often closer to signal-processing and data streaming applications than they are to control-oriented business workflow applications.

/pdf/kepler-an-extensible-system-for-design-and-execution-of-4nt3chsgqp.pdf

S. Mock

Papers

Kepler: an extensible system for design and execution of scientific workflows