A graph-based data model for API ecosystem insights
Erik Wittern, Jim Laredo, et al.
ICWS 2014
The execution of distributed applications are captured by the events generated by the individual components. However, understanding the behavior of these applications from their event logs can be a complex and error prone task, compounded by the fact that applications continuously change rendering any knowledge obsolete. We describe our experiences applying a suite of processaware analytic tools to a number of real world scenarios, and distill our lessons learned. For example, we have seen that these tools are used iteratively, where insights gained at one stage inform the configuration decisions made at an earlier stage. As well, we have observed that data onboarding, where the raw data is cleaned and transformed, is the most critical stage in the pipeline and requires the most manual effort and domain knowledge. In particular, missing, inconsistent, and low-resolution event time stamps are recurring problems that require better solutions. The experiences and insights presented here will assist practitioners applying process analytic tools to real scenarios, and reveal to researchers some of the more pressing challenges in this space.
Erik Wittern, Jim Laredo, et al.
ICWS 2014
K. R. Kallapalayam Radhakrishnan, Vinod Muthusamy, et al.
Big Data 2022
Xue Han, Lianxue Hu, et al.
SCC 2020
Yara Rizk, Vatche Isahagian, et al.
BPM 2020