Scientific Process Automation and Workflow Management

Ludäscher, Bertram; Altıntaş, Ilkay; Bowers, Shawn; Cummings, Julian; Critchlow, Terence; Deelman, Ewa; Roure, David De; Freire, Juliana; Goble, Carole; Jones, Matthew B.; Klasky, Scott; McPhillips, Timothy M.; Podhorszki, Norbert; Silva, Claudio; Taylor, Ian; Vouk, Mladen A.

doi:10.1201/9781420069815-c13

Cited by 61 publications

(48 citation statements)

References 44 publications

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“…In one of our earlier work [10] we have classified the dynamic tools of scientific workflow management systems (SWfMS) at different phases of the workflow lifecycle and at different levels, namely system level, task level and workflow level. These features include some kind of language support [11], advance and late modelling techniques [13], [14], incremental compilation techniques [17], [18], [19], [20], dynamic resource allocation [15], workflow partitioning [21] and flexible data management [16]. Obviously most of them relates on monitoring the workflow execution, or the state of the computing resources.…”

Section: Related Workmentioning

confidence: 99%

A novel approach to user-steering in scientific workflows

Kail

Kacsuk

Kozlovszky

2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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Abstract-From the scientist's perspective the workflow execution is like black boxes [25]. The scientist submits the workflow and at the end, the result or a notification about failed completion is returned. Concerning long running experiments or when workflows are in experimental phase it may not be acceptable. Scientist may need to fine-tune and monitor their experiments. To support the scientist with special user interaction tool we introduced intervention points (iPoints) where the user takes over the control for a while and has the possibility to interfere, namely to change some parameters or data, or to stop, to restart the workflow or even to deviate from the original workflow model during enactment. We plan to implement our solution in IWIR [9] language which was targeted to provide interoperability between four existing well-known SWfMS within the framework of the SHIWA project [1].

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Section: Related Workmentioning

confidence: 99%

A novel approach to user-steering in scientific workflows

Kail

Kacsuk

Kozlovszky

2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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“…This language is similar to other workflow expression languages, such as ZIGZAG [1] from the MEANDRE framework, Web Services Business Process Execution Language (BPEL-WS) [2] from IBM and others listed in Deelman et al [3] and Ludäscher et al [4]. Using DISPEL abstraction constructs, the ADMIRE framework validates and optimizes execution (in other words, enactment) of data mining and integration applications.…”

Section: (A) Abstraction and Languagementioning

confidence: 99%

Validation and mismatch repair of workflows through typed data streams

Yaikhom

Atkinson

Hemert

et al. 2011

Phil. Trans. R. Soc. A.

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The type system of a language guarantees that all of the operations on a set of data comply with the rules and conditions set by the language. While language typing is a fundamental requirement for any programming language, the typing of data that flow between processing elements within a workflow is currently being treated as optional. In this paper, we introduce a three-level type system for typing workflow data streams. These types are parts of the Data Intensive System Process Engineering Language programming language, which empowers users with the ability to validate the connections inside a workflow composition, and apply appropriate data type conversions when necessary. Furthermore, this system enables the enactment engine in carrying out type-directed workflow optimizations.

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“…The datasets are created in the different phases of the scientific workflow lifecycle [8], [9] and originate from three different sources. The scientist can give information when to design the abstract model, when to get the results or after the results are published.…”

Section: Introductionmentioning

confidence: 99%

Minimal sufficient information about the scientific workflows to create reproducible experiment

Bánáti

Kacsuk

Kozlovszky

2015

2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES)

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-The reproducibility of an in-silico experiment is a great challenge because of the parallel and distributed environment and the complexity of the scientific workflows. In order to solve such problems on one hand provenance data has to be captured about the dataflow, the ancestry of the results and the environment of the execution, on the other hand description data has to be collected from the scientist and stored about the essential details, the types and samples of input/output data, and the operation of the experiment. The ultimate goal of our work is to propose a minimal dataset for recording and reporting scientific workflow based experiment, which will facilitate the reproducibility of such experiments, the public repositories and enable to share and reuse the scientific result. One part of the dataset can be filled in manually by the scientist, certain part can be filled in automatically by the system and other part can be filled in from provenance data.

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Scientific Process Automation and Workflow Management

Cited by 61 publications

References 44 publications

A novel approach to user-steering in scientific workflows

A novel approach to user-steering in scientific workflows

Validation and mismatch repair of workflows through typed data streams

Minimal sufficient information about the scientific workflows to create reproducible experiment

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