Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data

Anastasiou, Nikolas; Horng, Tzu-Ching; Knottenbelt, William J.

doi:10.4108/icst.valuetools.2011.245715

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…This tool has been evaluated through a number of case studies in [1][2][3]. These case studies, conducted using synthetic location tracking data generated by LocTrackJINQS [6], employ several types of customer-processing systems, including systems with synchronisation, multiple customer classes and service cycles.…”

Section: Resultsmentioning

confidence: 99%

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PEPERCORN: Inferring Performance Models from Location Tracking Data

Anastasiou

Knottenbelt

2013

Quantitative Evaluation of Systems

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Abstract. Stochastic performance models are widely used to analyse the performance of systems that process customers and resources. However, the construction of such models is traditionally manual and therefore expensive, intrusive and prone to human error. In this paper we introduce PEPERCORN, a Petri Net Performance Model (PNPM) construction tool, which, given a dataset of raw location tracking traces obtained from a customer-processing system, automatically formulates and parameterises a corresponding Coloured Generalised Stochastic Petri Net (CGSPN) performance model.

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Section: Resultsmentioning

confidence: 99%

“…PEPERCORN is a Java-based implementation of our earlier work [1][2][3] which presented a methodology, based on a four-stage data processing pipeline (cf. Figure 1), that allows the automated construction of CGSPN performance models from high-precision location tracking data.…”

Section: Pepercornmentioning

confidence: 99%

PEPERCORN: Inferring Performance Models from Location Tracking Data

Anastasiou

Knottenbelt

2013

Quantitative Evaluation of Systems

Self Cite

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“…In fact, very few mining approaches have been proposed that are able to discover timed models, like stochastic Petri nets (see, e.g., Anastasiou et al [2011] and Hu et al [2011]). Therefore, in our setting, information about timings in the log can be helpful to a limited extent only.…”

Section: Causal Nets and Logsmentioning

confidence: 99%

Process Discovery under Precedence Constraints

Greco

Guzzo

Lupia

et al. 2015

ACM Trans. Knowl. Discov. Data

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Process discovery has emerged as a powerful approach to support the analysis and the design of complex processes. It consists of analyzing a set of traces registering the sequence of tasks performed along several enactments of a transactional system, in order to build a process model that can explain all the episodes recorded over them. An approach to accomplish this task is presented that can benefit from the background knowledge that, in many cases, is available to the analysts taking care of the process (re-)design. The approach is based on encoding the information gathered from the log and the (possibly) given background knowledge in terms of precedence constraints, that is, of constraints over the topology of the resulting process models. Mining algorithms are eventually formulated in terms of reasoning problems over precedence constraints, and the computational complexity of such problems is thoroughly analyzed by tracing their tractability frontier. Solution algorithms are proposed and their properties analyzed. These algorithms have been implemented in a prototype system, and results of a thorough experimental activity are discussed.

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“…Hu et al [1] proposed a technique that is primarily based on SPN model which is exponentially distributed for workflow log and depends on transition rate of firing. Anastasiou et al [2] proposed completely unique strategies, whereby they centered at the location data for generalized stochastic Petri net model for customer modelling flows. In their research work for transition intervals, they used constant hyper-Erlang distributions which shows waiting and run times and also used GSPN to upgrade the other corresponding transitions subnet depicting the same features which the hyper-Erlang distribution has.…”

Section: Related Workmentioning

confidence: 99%

Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

Shahzadi

Fang

Alilah

2021

Mathematical Problems in Engineering

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For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

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Deriving Generalised Stochastic Petri Net Performance Models from High-Precision Location Tracking Data

Cited by 13 publications

References 11 publications

PEPERCORN: Inferring Performance Models from Location Tracking Data

PEPERCORN: Inferring Performance Models from Location Tracking Data

Process Discovery under Precedence Constraints

Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

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