Learning Service Selection Decision Making Behaviors During Scientific
  Workflow Development
Xihao Xie Jia Zhang Rahul Ramachandran Tsengdar J. Lee Seungwon Lee
Increasingly, more software services have been published onto the Internet,
making it a big challenge to recommend services in the process of a scientific
workflow composition. In this paper, a novel context-aware approach is proposed
to recommending next services in a workflow development process, through
learning service representation and service selection decision making behaviors
from workflow provenance. Inspired by natural language sentence generation, the
composition process of a scientific workflow is formalized as a step-wise
procedure within the context of the goal of workflow, and the problem of next
service recommendation is mapped to next word prediction. Historical service
dependencies are first extracted from scientific workflow provenance to build a
knowledge graph. Service sequences are then generated based on diverse
composition path generation strategies. Afterwards, the generated corpus of
composition paths are leveraged to study previous decision making strategies.
Such a trained goal-oriented next service prediction model will be used to
recommend top K candidate services during workflow composition process.
Extensive experiments on a real-word repository have demonstrated the
effectiveness of this approach.