Abstract. In a variety of contexts, time-stamped and typed event logs enable the construction of a stochastic model capturing the sequencing and timing of observable discrete events. This model can serve various objectives including: diagnosis of the current state; prediction of its evolution over time; scheduling of response actions. We propose a technique that supports online scheduling of actions based on a prediction of the model state evolution: the model is derived automatically by customizing the general structure of a semi-Markov process so as to fit the statistics of observed logs; the prediction is updated whenever any observable event changes the current state estimation; the (continuous) time point of the next scheduled action is decided according to policies based on the estimated distribution of the time to given critical states. Experimental results are reported to characterize the applicability of the approach with respect to general properties of the statistics of observable events and with respect to a specific reference dataset from the context of Ambient Assisted Living.

A stochastic model-based approach to online event prediction and response scheduling / Biagi, M.; Carnevali, L.; Paolieri, M.; Patara, F.; Vicario, E.. - ELETTRONICO. - LNCS 9951:(2016), pp. 32-47. (Intervento presentato al convegno 13th European Workshop on Performance Engineering, EPEW 2016 tenutosi a Chios, Greece nel October 5-7 2016) [10.1007/978-3-319-46433-6_3].

A stochastic model-based approach to online event prediction and response scheduling

BIAGI, MARCO;CARNEVALI, LAURA;PAOLIERI, MARCO;PATARA, FULVIO;VICARIO, ENRICO
2016

Abstract

Abstract. In a variety of contexts, time-stamped and typed event logs enable the construction of a stochastic model capturing the sequencing and timing of observable discrete events. This model can serve various objectives including: diagnosis of the current state; prediction of its evolution over time; scheduling of response actions. We propose a technique that supports online scheduling of actions based on a prediction of the model state evolution: the model is derived automatically by customizing the general structure of a semi-Markov process so as to fit the statistics of observed logs; the prediction is updated whenever any observable event changes the current state estimation; the (continuous) time point of the next scheduled action is decided according to policies based on the estimated distribution of the time to given critical states. Experimental results are reported to characterize the applicability of the approach with respect to general properties of the statistics of observable events and with respect to a specific reference dataset from the context of Ambient Assisted Living.
2016
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13th European Workshop on Performance Engineering, EPEW 2016
Chios, Greece
October 5-7 2016
Biagi, M.; Carnevali, L.; Paolieri, M.; Patara, F.; Vicario, E.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1079374
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