Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis

Barseghian, Derik; Altıntaş, Ilkay; Jones, Matthew B.; Crawl, Daniel; Potter, Nathan; Gallagher, James; Cornillon, Peter; Schildhauer, Mark; Borer, Elizabeth T.; Seabloom, Eric W.; Hosseini, Parviez R.

doi:10.1016/j.ecoinf.2009.08.008

Cited by 84 publications

(45 citation statements)

References 29 publications

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“…One active area of research lies in integrating QA/QC with data and metadata management systems and scientific workflow systems so that well-documented data can be automatically assessed for metadata completeness and data quality [25][26][27][28][29]. For instance, the LTER Network Information System (NIS) is designed so that data products are evaluated, processed and classified according to five categories, ranging from raw or minimally modified site data (Level-0) to data that are gap filled and semantically adjusted to meet the needs of specific synthetic data products (Level-4) [25].…”

mentioning

confidence: 99%

Ecoinformatics: supporting ecology as a data-intensive science

Michener

Jones

2012

Trends in Ecology & Evolution

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365

324

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mentioning

confidence: 99%

Ecoinformatics: supporting ecology as a data-intensive science

Michener

Jones

2012

Trends in Ecology & Evolution

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365

324

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“…Kepler [25] is a graphical system for scientific workflows design, execute, reuse, and sharing. Kepler's provide high effective workflow designing process by monitoring data and provenance information during the initial workflow design stage Kepler supports also many advanced features such as automatic workflow validation and editing; by providing a semantic annotation of workflow tasks from a domain ontology.…”

Section: Reasoning With Workflows At the Knowledge Levelmentioning

confidence: 99%

Technical Perspectives on Knowledge Management in Bioinformatics Workflow Systems

Ismail¹,

Aksoy²

2015

ijacsa

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Abstract-Workflow systems by it's nature can help bioinformaticians to plan for their experiments, store, capture and analysis of the runtime generated data. On the other hand, the life science research usually produces new knowledge at an increasing speed; Knowledge such as papers, databases and other systems knowledge that a researcher needs to deal with is actually a complex task that needs much of efforts and time. Thus the management of knowledge is therefore an important issue for life scientists. Approaches has been developed to organize biological knowledge sources and to record provenance knowledge of an experiment into a readily resource are presently being carried out. This article focuses on the knowledge management of in silico experimentation in bioinformatics workflow systems.

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“…at the 'point of creation') e.g. [1,2], and tackling the challenges in representation for interoperability [3]. However, there is a lack of understanding as to how users can exploit provenance data to enhance their work at the 'point of use'.…”

Section: Introductionmentioning

confidence: 99%

Personalised Provenance Reasoning Models and Risk Assessment in Business Systems: A Case Study

Townend

Webster

Venters

et al. 2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

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Abstract-As modern information systems become increasingly business-and safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe the STRAPP approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project.

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Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis

Abstract: Abstract

Cited by 84 publications

References 29 publications

Ecoinformatics: supporting ecology as a data-intensive science

Ecoinformatics: supporting ecology as a data-intensive science

Technical Perspectives on Knowledge Management in Bioinformatics Workflow Systems

Personalised Provenance Reasoning Models and Risk Assessment in Business Systems: A Case Study

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