LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention
Proceedings of the 27th International Conference on Very Large Data Bases
Computers in Industry - Special issue: Process/workflow mining
Diagnosis for monitoring system of municipal solid waste incineration plant
Expert Systems with Applications: An International Journal
Predictive business operations management
International Journal of Computational Science and Engineering
The Case for Quantitative Process Management
IEEE Software
Decision analysis of data mining project based on Bayesian risk
Expert Systems with Applications: An International Journal
Real-time Process Quality Control for Business Activity Monitoring
ICCSA '09 Proceedings of the 2009 International Conference on Computational Science and Its Applications
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
A rule-based approach to proactive exception handling in business processes
Expert Systems with Applications: An International Journal
Runtime prediction of service level agreement violations for composite services
ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
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In this paper, we propose a novel approach to real-time business process monitoring for prediction of abnormal termination. Existing real-time monitoring approaches are difficult to use proactively, owing to unobserved data from gradual process executions. To improve the utility and effectiveness of real-time monitoring, we derived a KNNI (k nearest neighbor imputation)-based LOF (local outlier factor) prediction algorithm. In each monitoring period of an ongoing process instance, the proposed algorithm estimates the distribution of LOF values and the probability of abnormal termination when the ongoing instance is terminated, which estimations are conducted periodically over entire periods. Thereby, we can probabilistically predict outcomes based on the current progress. In experiments conducted with an example scenario, we showed that the proposed predictors can reflect real-time progress and provide opportunities for proactive prevention of abnormal termination by means of an early alarm. With the proposed method, abnormal termination of an ongoing instance can be predicted, before its actual occurrence, enabling process managers to obtain insights into real-time progress and undertake proactive prevention of probable risks, rather than merely reactive correction of risk eventualities.