2 citations · 7 across the 6 of their papers we have counts for
6 papers
DDTR: Diffusion Denoising Trace Recovery
Maximilian Matyash, Avigdor Gal, Arik Senderovich
With recent technological advances, process logs, which were traditionally deterministic in nature, are being captured from non-deterministic sources, such as uncertain sensors or…
Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach
Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio +1
Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specificati…
Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models
David Chapela-Campa, Ismail Benchekroun, Opher Baron +3
Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be th…
Agent Miner: An Algorithm for Discovering Agent Systems from Event Data
Andrei Tour, Artem Polyvyanyy, Anna Kalenkova +1
Process discovery studies ways to use event data generated by business processes and recorded by IT systems to construct models that describe the processes. Existing discovery algo…
Capacity Allocation for Clouds with Parallel Processing, Batch Arrivals, and Heterogeneous Service Requirements
Eugene Furman, Arik Senderovich, Shane Bergsma +1
Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires mu…
Can machines solve general queueing systems?
Eliran Sherzer, Arik Senderovich, Opher Baron +1
In this paper, we analyze how well a machine can solve a general problem in queueing theory. To answer this question, we use a deep learning model to predict the stationary queue-l…