activity
20212024
most citedA Robust and Accurate Approach to Detect Process Drifts from Event Streams

12 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2024★ 1 cited

The Runge--Kutta discontinuous Galerkin method with stage-dependent polynomial spaces for hyperbolic conservation laws

Qifan Chen, Zheng Sun, Yulong Xing

In this paper, we present a novel class of high-order Runge--Kutta (RK) discontinuous Galerkin (DG) schemes for hyperbolic conservation laws. The new method extends beyond the trad…

cs.LG2023★ 7 cited

Modeling Unknown Stochastic Dynamical System via Autoencoder

Zhongshu Xu, Yuan Chen, Qifan Chen +1

We present a numerical method to learn an accurate predictive model for an unknown stochastic dynamical system from its trajectory data. The method seeks to approximate the unknown…

math.NA2023★ 2 cited

The Runge--Kutta discontinuous Galerkin method with compact stencils for hyperbolic conservation laws

Qifan Chen, Zheng Sun, Yulong Xing

In this paper, we develop a new type of Runge--Kutta (RK) discontinuous Galerkin (DG) method for solving hyperbolic conservation laws. Compared with the original RKDG method, the n…

cs.DB2021★ 8 cited

A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining

Qifan Chen, Yang Lu, Charmaine S. Tam +1

Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process…

cs.SE2021★ 12 cited

A Robust and Accurate Approach to Detect Process Drifts from Event Streams

Yang Lu, Qifan Chen, Simon Poon

Business processes are bound to evolve as a form of adaption to changes, and such changes are referred as process drifts. Current process drift detection methods perform well on cl…