A Cloud-Based Prediction Framework for Analyzing Business Process Performances

Abstract : This paper presents a framework for analyzing and predicting the performances of a business process, based on historical data gathered during its past enactments. The framework hinges on an inductive-learning technique for discovering a special kind of predictive process models, which can support the run-time prediction of some performance measure (e.g., the remaining processing time or a risk indicator) for an ongoing process instance, based on a modular representation of the process, where major performance-relevant variants of it are equipped with different regression models, and discriminated through context variables. The technique is an original combination of different data mining methods (namely, non-parametric regression methods and a probabilistic trace clustering scheme) and ad hoc data transformation mechanisms, meant to bring the log traces to suitable level of abstraction. In order to overcome the severe scalability limitations of current solutions in the literature, and make our approach really suitable for large logs, both the computation of the trace clusters and of the clusters’ predictors are implemented in a parallel and distributed manner, on top of a cloud-based service-oriented infrastructure. Tests on a real-life log confirmed the validity of the proposed approach, in terms of both effectiveness and scalability.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-01635015
Contributor : Hal Ifip <>
Submitted on : Tuesday, November 14, 2017 - 4:06:55 PM
Last modification on : Wednesday, November 15, 2017 - 1:15:12 AM
Long-term archiving on : Thursday, February 15, 2018 - 1:52:44 PM

File

430962_1_En_5_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Eugenio Cesario, Francesco Folino, Massimo Guarascio, Luigi Pontieri. A Cloud-Based Prediction Framework for Analyzing Business Process Performances. International Conference on Availability, Reliability, and Security (CD-ARES), Aug 2016, Salzburg, Austria. pp.63-80, ⟨10.1007/978-3-319-45507-5_5⟩. ⟨hal-01635015⟩

Share

Metrics

Record views

86

Files downloads

103