Graph-Based Algorithms for Discovering a Process Model Containing Invisible Tasks

Sarno, Riyanarto; Sungkono, Kelly Rossa; Johanes, Reynaldo; Sunaryono, Dwi

doi:10.22266/ijies2019.0430.09

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…The complete SOP can be used as a reference to detect anomalies in a running process and existing data that may contain fraud. Analyzing the anomalies in a business process can be done using process mining techniques [18][19][20][21][22].…”

Section: Types Of Fraudmentioning

confidence: 99%

Anomaly detection in business processes using process mining and fuzzy association rule learning

2020

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Many corporations worldwide use an enterprise resource planning (ERP) system to manage their business process, which continuously changes due to dynamic business requirements [1]. Because the processes run continuously, ERP produces a considerable log of processes. Manual observation will have difficulty monitoring the sizeable log, especially detecting anomalies. It needs the method that can detect anomalies in the huge log. Standard business processes are usually incorporated into standard operating procedures (SOP), which are used as a reference to find any deviations. Deviations or anomalies in the business process can be caused by variations or operation errors [2]; however, some of the anomalies may be the result of fraudulent behaviours [3]. Fraud can be committed in many ways and can lead to significant losses. In 2012, the Association of Certified Fraud Examiners (ACFE) reported that there had been 1.388 fraud cases in 96 countries, which have incurred US$1.4 billion in losses [4]. On average, organizations

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Section: Types Of Fraudmentioning

confidence: 99%

Anomaly detection in business processes using process mining and fuzzy association rule learning

2020

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“…The discovered model can be beneficial, starting from inspection and finding valuable insights to observing the conformance with the reference model. Several methods are known for discovering business process models from event logs, including alpha miner [2], heuristic miner [3], inductive miner [4], fuzzy miner [5], split miner [6,7], and graphbased miner [8,9]. Graph-based miner algorithms outperform others in lower time complexity [10].…”

Section: Introductionmentioning

confidence: 99%

“…In previous graph-based miner studies, several algorithms have been introduced to find process models, including detecting parallel processes [8,11], recognize and insert the invisible task [9], and discovering Non-free Choice [14].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Waspada¹,

Sarno²,

Sungkono³

2020

IJIES

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The existing method of graph-based process model discovery has weaknesses in detecting parallel relationship (XOR, AND, and OR). The algorithm only works on a particular graph structure, so it must be reconfigured when applied to other different structures. To answer this problem, this paper proposes an improved method of parallel model detection, which is designed in two phases. The first one consists of three steps; firstly is to count and record the value of relationship frequency into every node in a graph model. Then, the second step implements the algorithm to discover the concurrent relationship. The third step detects all possible split and join relationships. Based on the first phase, then a consistent and robust parallel discovery algorithm can be developed. The first parallel algorithm is to identify the XOR relationship. This algorithm is designed with the rule that the XOR pattern cannot have a concurrent relationship between its branch nodes. Next, the algorithm for detecting AND and OR must detect the existence of any concurrent relationship in its branches. Then, AND and OR pattern is differentiated by their unique characteristic of relationship frequency at branch nodes. To verify the ability of the proposed methods in which the existing method fails, we have designed four scenarios. Scenario 1 and 2 consecutively were arranged with two and three branches parallel model. Scenario 3 located the AND and OR inside the XOR pattern. In scenario 4 the sequence relationships were inserted between split and join of parallel patterns. The experimental results show that the proposed method successfully recognizes and differentiates XOR, AND and OR patterns correctly in all scenarios. It also sounds in all discovered model and get 100% fitness.

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“…The algorithms of process discovery have been developed rapidly to discover several types of relations [8][9][10][11][12][13]. Table 1 describes the existing algorithms which can discover types of relations depicted by the columns.…”

Section: Introductionmentioning

confidence: 99%

“…Existing graph-based process discovery methods [13,[20][21][22] do not directly interact with the system, so those methods import the event log of the system and convert the log to graph-database. The conversion process causes ineffective computing time.…”

Section: Introductionmentioning

confidence: 99%

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

Sarno

Sungkono

Taufiqulsa’di

et al. 2020

Preprint

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Process discovery helps companies to automatically discover their existing business processes based on the huge, stored event log. The algorithms of process discovery have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by α $ method, is a type of relation that combines the non-free choice and the invisible task. α $ proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of α $ check the combination of invisible task within non-free choice. The checking processes is time consuming, and results in high computing times of α $. This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relations of the graphs. The analysis of the graph relations by rules of GIT is more efficient than the iterative process of checking combined activities by α $. This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Storing a streaming event log in a graph-database has the lowest computing time than storing in other databases, i.e., SQL and MongoDB. Discovering a process model by GIT algorithm has less time complexity than that by α $, wherein GIT obtains O(n3) and α $ obtains O(n4) . In terms of computing time, GIT algorithm is 0.89 faster on batch event log and 0.85 seconds faster on streaming event log than α $. Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

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Graph-Based Algorithms for Discovering a Process Model Containing Invisible Tasks

Cited by 23 publications

References 17 publications

Anomaly detection in business processes using process mining and fuzzy association rule learning

Anomaly detection in business processes using process mining and fuzzy association rule learning

An Improved Method of Parallel Model Detection for Graph-Based Process Model Discovery

Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

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