A semantic framework for automatic generation of computational workflows using distributed data and component catalogues

2013 IEEE Seventh International Conference on Semantic Computing

2013

Self Cite

Abstract-Current workflows have limited reusability and shelf life because they specify particular application software to be run at each step, which can become obsolete or no longer run. We describe semantic workflow representations composed of domain tasks, which the system then automatically maps to the application codes available in the particular execution environment. We have implemented this approach in the WINGS semantic workflow system, and demonstrate the mapping of workflows to different sets of application codes depending on the workflow execution engine and execution environment selected.

Section: Generating Executable Workflows From Workflows Of Domain mentioning

confidence: 99%

Mapping Semantic Workflows to Alternative Workflow Execution Engines

2013 IEEE Seventh International Conference on Semantic Computing

2013

Self Cite

“…In order for ThAIs to access comprehensive scientific knowledge, we need to augment our current representations and capture more complex aspects of scientific research such as data analysis processes, hypotheses and claims, and synthesis of evidence from data [8]. I am personally interested in computational processes for data analysis, and have developed semantic workflow representations to capture knowledge about how the properties of datasets affect how they should be analyzed, and how the assumptions of data analysis algorithms constrain their use in a data analysis method [15]. The WINGS intelligent workflow system incorporates a variety of computational techniques that use those representations to assist scientists in: (1) finding appropriate workflows, (2) customizing existing workflows, (3) exploring the space of alternative workflow configurations, (4) correlating the results of exploratory executions, and (5) detecting commonly used workflow fragments.…”

Section: Networked Thais: Towards a Scientific Knowledge Webmentioning

confidence: 99%

Thoughtful artificial intelligence: Forging a new partnership for data science and scientific discovery

2017

Self Cite

Abstract. Artificial intelligence will play an increasingly more prominent role in scientific research ecosystems, and will become indispensable as more interdisciplinary science questions are tackled. While in recent years computers have propelled science by crunching through data and leading to a data science revolution, qualitatively different scientific advances will result from advanced intelligent technologies for crunching through knowledge and ideas. We propose seven principles for developing thoughtful artificial intelligence, which will turn intelligent systems into partners for scientists. We present a personal perspective on a research agenda for thoughtful artificial intelligence, and discuss its potential for data science and scientific discovery.

“…In our work, we use the Wings workflow system [6]. Wings is unique in that it uses semantic workflow representations to describe the kinds of data and computational steps in the workflow.…”

Section: Data Processing With Wings: Metadata About Analysis Processesmentioning

confidence: 99%

“…Karma, our data preparation tool, carries out data preparation and integration steps [14]. Wings, our workflow system, carries our data processing steps through computations [6]. Both systems capture and use metadata as the data flows through different steps.…”

Section: Approach: Provenance-aware Systems That Manage Metadatamentioning

confidence: 99%

Mind Your Metadata: Exploiting Semantics for Configuration, Adaptation, and Provenance in Scientific Workflows

The Semantic Web – ISWC 2011

Szekely

Villamizar

et al. 2011

Self Cite

Abstract. Scientific metadata containing semantic descriptions of scientific data is expensive to capture and is typically not used across entire data analytic processes. We present an approach where semantic metadata is generated as scientific data is being prepared, and then subsequently used to configure models and to customize them to the data. The metadata captured includes sensor descriptions, data characteristics, data types, and process documentation. This metadata is then used in a workflow system to select analytic models dynamically and to set up model parameters automatically. In addition, all aspects of data processing are documented, and the system is able to generate extensive provenance records for new data products based on the metadata. As a result, the system can dynamically select analytic models based on the metadata properties of the data it is processing, generating more accurate results. We show results in analyzing stream metabolism for watershed ecosystem management.