Modeling hydrochory effects on the Tunisian island populations of Pancratium maritimum L. using colored Petri nets

Sanaa, Adnen; Abid, Samir Ben; Boulila, Abdennacer; Messaoud, Chokri; Boussaïd, Mohamed; Fadhel, Najeh Ben

doi:10.1016/j.biosystems.2015.02.001

Cited by 15 publications

(11 citation statements)

References 26 publications

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“…Eq. (5) is used to decide the dependency relation between the activities in the control-flow network: (5) where the symbols refer to:…”

Section: Iii1 Mining the Dependency Graphmentioning

confidence: 99%

“…The second step is to count the dependency measure using Eq. (5). The formula produces the dependency measure matrix.…”

Section: Iv21 Mining the Dependency Graphmentioning

confidence: 99%

“…Recently Process Mining has been implemented in many fields, such as business [4], environment [5], [6], smartphone [7], and fraud [8], [9]. Each method has its own disadvantages resulting in the activities of the organization not being represented effectively in the discovered model [10].…”

Section: Introductionmentioning

confidence: 99%

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Modified Time-Based Heuristics Miner for Parallel Business Processes

Sarno

Effendi

Haryadita

2016

IRECOS

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Process Mining, or Process Discovery, is a method for modeling the workflow of a business process from event logs. Business process models contain sequential and parallel traces. In this paper, a modification of the frequently used process-mining algorithm Heuristics Miner is proposed. The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log. It can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns; the latter cannot be discovered by the original Heuristics Miner. The threshold intervals are determined on the basis of the average dependency measure in the dependency graph. The experimental results show that the proposed algorithm is able to discover concurrent business processes formed by parallel (AND) and conditional (OR) patterns, whereas the existing Heuristics Miner algorithm can only discover concurrent business processes formed by parallel (AND) patterns. This paper also provides an evaluation of validity and fitness of the discovered process model.

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“…Eq. (5) is used to decide the dependency relation between the activities in the control-flow network: (5) where the symbols refer to:…”

Section: Iii1 Mining the Dependency Graphmentioning

confidence: 99%

“…The second step is to count the dependency measure using Eq. (5). The formula produces the dependency measure matrix.…”

Section: Iv21 Mining the Dependency Graphmentioning

confidence: 99%

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Modified Time-Based Heuristics Miner for Parallel Business Processes

Sarno

Effendi

Haryadita

2016

IRECOS

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“…Process models have been widely utilized for measuring the performance of processes in the company and pointing issues related with those processes [1]. Several areas of issues are deceptions [2][3], communications [4], and environments [5]. Process models can be drawn manually by analysts or can be generated based on the event logs.…”

Section: Introductionmentioning

confidence: 99%

Modified Alpha++ Algorithm for Discovering the Hybrid of Non-Free Choice and Invisible Task of Business Processes

Hakim¹,

Sarno²,

Sungkono³

2019

IJIES

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Process discovery is a concern in analyzing business processes. Process discovery methods handle many aspects, e.g. a hybrid of non-free choice and invisible task. A hybrid of non-free choice and an invisible task is a condition that needs additions of invisible tasks in non-free choice relationships. Process discovery methods, e.g. Alpha++, Alpha#, Graphical method, Time-based Alpha Miner, cannot discover the hybrid of non-free choice and invisible task, especially invisible tasks in the overlapping pattern. To modeling a hybrid of non-free choice and invisible tasks, this research proposes an algorithm of adding invisible tasks, including invisible tasks of overlapping pattern, automatically in the event log and processing the event log with the rules of Alpha++ algorithm. To test the proposed algorithm, namely Invisible Task Log with Alpha++, the experiment compares it with Alpha++, Alpha#, Graphical Method, Time-based Alpha Miner, Hidden Markov Model-Parallel, and Coupled Hidden Markov Model-Invisible Task. The experiment results proved that Invisible Task Log with Alpha++ algorithm obtains higher precision than the obtained precision by comparison methods.

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“…These issues exist in any field, e.g. business [1], environment [2,3] smartphone [4], and fraud [5].…”

Section: Introductionmentioning

confidence: 99%

Determining Process Model Using Time-Based Process Mining and Control-Flow Pattern

et al. 2016

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Determining right model of business process from event log is the purpose of process discovery. However some problems i.e the inability to discover OR, noise IntroductionDetermining model of business process from event log is the main purpose of process discovery. Process discovery is a challenging task in process mining. It is a set of techniques which automatically construct a model of an organization`s current activities and its major activities variations. These techniques use event log of activities within an organization. The business process model is analyzed to show the complexity of issues in activities and how to solve them. These issues exist in any field, e.g. business Process discovery comes up with many algorithms, e.g. alpha, alpha+, alpha++ [6]. The alpha, alpha+, and alpha++ cannot deal with noise, incompleteness issues and OR conditional. Heuristic miner algorithms [7,9] come up to solve the noise problem. However, most of the algorithms are unable to find OR conditional model. The existing algorithm frequently discovers the OR conditional as AND parallel or XOR conditional. The thought of parallel model discovery will change the result of activities [8]. When "wait and see" behavior model synchronization is occured, it needs OR parallel to model the parallel split and join. The "wait and see" behavior model synchronization occured when the actor can choose only one activity, all activity, or more than one activity in parallel split and join. In this paper we proposed ideas to discover OR conditional within business process model.One of important things from process mining is the idea of completeness which is related to noise. Incompleteness leads to false parallel relations discovery, e.g the discovered parallel relation is XOR but the right parallel relation in business process is OR. The new representation of OR-split uses combination the existing XOR-split and AND-split to make the model easier to be analyzed [13]. In other hand temporal activity-based algorithm [8] and control-flow pattern can handle discovery of business process model with incompleteness and same frequency noise issues. Non-linear dependence in temporal activity-based algorithm is used to solve incompleteness problem since it can discover more relation than linear dependence. Controlflow pattern is used to solve same amounts of noise frequency issues because it discovers relation based on transaction function of activity, therefore it can choose non noise relation in business process model.

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Modeling hydrochory effects on the Tunisian island populations of Pancratium maritimum L. using colored Petri nets

Cited by 15 publications

References 26 publications

Modified Time-Based Heuristics Miner for Parallel Business Processes

Modified Time-Based Heuristics Miner for Parallel Business Processes

Modified Alpha++ Algorithm for Discovering the Hybrid of Non-Free Choice and Invisible Task of Business Processes

Determining Process Model Using Time-Based Process Mining and Control-Flow Pattern

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