Unit of Work Supporting Generative Scientific Workflow Recommendation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-07

AUTHORS

Jia Zhang , Maryam Pourreza , Seungwon Lee , Ramakrishna Nemani , Tsengdar J. Lee

ABSTRACT

Service discovery and recommendation is playing increasingly important role, as more and more reusable web services are published onto the Internet. Existing methods typically recommend either individual services, or multiple services without their interconnections. In contrast, this research aims to mine service usage history and extract units of work (UoWs) comprising a collection of services chained together through intermediate components. A novel technique is proposed in this paper to study how services collaborated, or could collaborate, in the form of reusable UoWs to serve various workflows (i.e., mashups), based on an evolving service social network. Upon receiving a scientific workflow request, a recommend-as-you-go algorithm simulates how human minds work and relies on a sliding aggressiveness gauge to incrementally recommend context-aware UoWs. In this way, we hope to move one step further toward automatic service composition. Extensive experiments on the real-world datasets demonstrate the effectiveness and efficiency of the UoW-oriented service recommendation approach. More... »

PAGES

446-462

References to SciGraph publications

  • 2009. Action Patterns in Business Process Models in SERVICE-ORIENTED COMPUTING – ICSOC 2007
  • 2003-07. Workflow Patterns in DISTRIBUTED AND PARALLEL DATABASES
  • 2011. Efficient, Interactive Recommendation of Mashup Composition Knowledge in SERVICE-ORIENTED COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-03596-9_32

    DOI

    http://dx.doi.org/10.1007/978-3-030-03596-9_32

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1108060006


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