On models and query languages for probabilistic processes

This work depicts the model established and claims that it achieves a good balance between expressivity and query evaluation complexity, and compares and contrast the model with other common models for probabilistic processes.

Abstract

Probabilistic processes appear naturally in various contexts, with applications to Business Processes, XML data management and more. Many models for specifying and querying such processes exist in the literature; a main goal of research in this area is to design models that are expressive enough to capture real-life processes and analysis tasks, but at the same time allow for efficient query evaluation. We depict the model established in [13, 16, 17, 18], and claim that it achieves a good balance between expressivity and query evaluation complexity. We compare and contrast the model with other common models for probabilistic processes, highlighting the different choices made in models design and their effect on expressivity and incurred complexity.