# Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

## Abstract

**:**

## 1. Introduction

## 2. Related Works

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- Firstly, we try to investigate the state-of-the-art in the process analysis and simulation tools and systems, separately. The authors of [6] defined the concept of the structured process model and its properties, which are the properties that can be checked up by the system proposed in this paper, and described a taxonomy for analyzing unstructured processes, which are characterized by the properties of improper nestings or mismatched split-join pairings.
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- The authors of [18] gave a definition of the system of this paper, in which the authors described a template that was built in the simulation language, Arena. The positive effect of using the template is decreasing the gap between the conceptualization activities and their real process models to be analyzed.
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- The necessity of the system is given by [19], in which the authors emphasized the importance of the comparison analysis between designed processes and redesigned processes by using the process analysis and simulation tools and systems. The authors also discussed a number of analysis tools that are relevant for the business process field, evaluated their applicability for business process analysis and simulation, and formulated the technical recommendations and further research issues. The system proposed in this paper is based upon the theory of the information control net modeling methodology.
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- The authors of [20] proposed a fully automatic method to simplify BPML (business process model and notation) process models and described a two-phase iterative algorithm to achieve this simplification, which follows a heuristic approach that makes intensive use of a pattern repository.
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- The study of [21] introduced a goal-driven process evaluation method based on process mining for healthcare processes. The proposed method comprises the following steps: defining goals and questions, data extraction, data preprocessing, log and pattern inspection, process mining analysis and generating answers to questions, evaluating results, and initiating proposals for process improvements. Additionally, the authors applied the proposed method to the surgical process of a university hospital in Turkey as a case study.
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- The authors of [22] proposed three strategies (based on exhaustive search, genetic algorithms, and a greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. Additionally, they implemented these strategies and tested them in both business-like event logs, as recorded in a higher educational enterprise resource planning system, and a real case scenario involving a set of Dutch municipalities.
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- In [23], the authors introduced the method of Accimap from the discipline of accident analysis to analyze the diagnosis results of process mining by creating a complaint handling service process management Accimap model and using it across different system levels. Additionally, they performed a case study in a big manufacturing company in China to verify the proposed method and approach. The case study identified 42 complaint handling process management factors and created the complaint handling process management Accimap model as a final outcome.
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- In the study of system inference, the authors of [2] proposed an integrated approach with process mining and fuzzy methods to build a system structure of the fuzzy discrete event system specification (Fuzzy-DEVS) model from system behavior. The proposed approach consists of three stages: (1) extraction of event logs from data by using the system entity structure method; (2) discovery of a transition system, using process discovery techniques; (3) integration of fuzzy methods to automatically generate a Fuzzy-DEVS model from the transition system. Finally, it took a plugin in the process mining framework (ProM) environment for inferring a Fuzzy-DEVS model from an event log dataset and carried out a simulation by using the SimStudio tool.
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- Other research outcomes [24,25] based on the Petrinet-based process modeling and analyzing methodology, such as those from the research group of the Eindhoven first and now of the Aachen University, from the research group of the Melbourne School of Engineering, and from the research group of the University of Camerino, provided us the essential intuitions and the functional scopes and definitions for specifying the very useful functional requirements and specifications of the system implemented in this paper.

## 3. An Integrated Functional Architecture

## 4. Process Mining Functionality

#### 4.1. Structural and Behavioral Process Patterns

**Definition**

**1.**

**Structural control-flow patterns in structured information control nets.**The basic control-flow structure of a SICN process model is formally defined through 4-tuple $\mathsf{\Gamma}=(\delta ,\kappa ,\mathit{I},\mathit{O})$ over a set of $\mathit{A}$ activities (including a set of group activities), a set $\mathit{T}$ of transition conditions, where

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- $\mathit{I}$ is a finite set of initial input repositories, assumed to be loaded with information by some external process before execution of the model;
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- $\mathit{O}$ is a finite set of final output repositories, which contains information used by some external process after execution of the model;
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- $\delta ={\delta}_{i}\cup {\delta}_{o}$: control-flow structural attributeswhere, ${\delta}_{o}:\mathit{A}\u27f6\wp \left(\mathit{A}\right)$ is a multivalued mapping function of an activity to its set of (immediate) successors, and ${\delta}_{i}:\mathit{A}\u27f6\wp \left(\mathit{A}\right)$ is a multivalued mapping function of an activity to its set of (immediate) predecessors;
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- $\kappa ={\kappa}_{i}\cup {\kappa}_{o}$: transition-condition associative attributeswhere ${\kappa}_{i}:\mathit{T}\u27f6\wp \left(\mathit{A}\right)$ is a multivalued mapping function of an activity $(\alpha \in \mathit{A})$ to its incoming transition-conditions on each arc, $({\delta}_{i}\left(\alpha \right),\alpha );$ and ${\kappa}_{o}:\mathit{T}\u27f6\wp \left(\mathit{A}\right)$: is a multivalued mapping function of an activity $(\alpha \in \mathit{A})$ to its outgoing transition-conditions on each arc, $(\alpha ,{\delta}_{o}\left(\alpha \right))$.

#### 4.2. The Process Mining Approach: $\rho $-Algorithm

**mass**comes. The central idea of the rediscovery algorithm of the framework is exactly the same for the implication of the APL function, rho ($\rho $). The $\rho $-operator gives the masses (occurrences) of all the activities in the event log dataset of a corresponding process model. In the paper, the emphasis is placed on rediscovering very large scale and massively parallel process models that are usually built by a combination of the four types of primitive process patterns, linear, disjunctive (exclusive-OR), conjunctive (parallel-AND), and repetitive (iterative-LOOP) process patterns, which were formally defined in the previous section.

- STEP-1: Groups of Temporally Ordered Adjacent-Activity Pairs: The first step of the $\rho $-Algorithm is to mine a group of temporally ordered adjacent-activities pairs from temporal workcases of the process instance event logs. Additionally, each of the temporal workcases is formally represented by one of the workcase model types introduced in the conceptual framework. That is, a temporal workcase represents an ordered enactment sequence of activity event logs, each of which is formed with its activity identifier and its time-stamp extracted from its corresponding process enactment event log.
- STEP-2: Quantitative Adjacent-Activity Set and Process Pattern Graph: The STEP-2 of the $\rho $-Algorithm is to build all the groups of temporally ordered adjacent-activity pairs, each of which corresponds to a process instance event trace. The eventual output of this algorithm is a quantitative adjacent-activity set named adjacencyList $\beta $. This set is built from all the groups of temporally ordered adjacent-activity pairs through an internal transformation procedure.
- STEP-3: Rediscovering Structured Process Patterns: The final step (STEP-3) of the $\rho $-Algorithm is to discover all the primitive process patterns of a structured information control net process model from the mined process pattern graph discovered from all the groups of temporally ordered adjacent-activity pairs. The eventual goal of the $\rho $-Algorithm is accomplished through this step. Note that the structured information control net process model must be satisfied with the proper nesting and the matched pairing properties in forming gateway activities in each primitive process graph pattern.

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- First, the process mining framework is able not only to rediscover the process patterns but also to discover the enactment proportions [27] of the process patterns from a dataset of the IEEE XES-formatted enactment event logs of a corresponding process model.
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- Second, the process mining framework is theoretically supported by the information control nets modeling methodology [16] of process models.
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- Third, the essential algorithm of the process mining approach is named the $\rho $-Algorithm (rho-Algorithm) that is able to rediscover a structured information control net model with the enactment occurrences of the activities associated with an underlying process model.
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- Fourth, the $\rho $-Algorithm was firstly developed in the process management and mining literature as a process mining algorithm that discovered a structured process model theoretically supported by the structured information control net modeling methodology.
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- Fifth, the $\rho $-Algorithm is able to rediscover all the primitive process patterns, such as linear (sequential), conjunctive (parallel-AND), disjunctive (exclusive-OR), and repetitive (iterative-LOOP) process patterns, and discover the enactment occurrences and proportions of each branch of the process patterns.

## 5. Process Analyzing Functionality

#### 5.1. Architectural Components of the Process Analyzer

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- Structural properness verification: checking up whether the rediscovered process model is keeping the rules of proper-nestings and matched pairings when building its gateway-type activities.
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- Associative relationship verification: checking up whether the rediscovered process model is keeping the correct associative attributes when building activity-to-role associations, activity-to-program associations, activity-to-data associations, and role-to-actor associations.
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- Performative properness verification: checking up whether the rediscovered process model is maintaining efficiency when managing the dynamic aspects at the package level, process level, activity level, and the performer level (performative indicators).
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- Behavioral simulation verification: based upon the rediscovered process model with its enactment event histories, checking up the enactment histories by tracing a process instance of the corresponding model via step-by-step clicking operations.

#### 5.2. Generation of the Rediscovered XPDL Process Models

#### 5.3. The Structural Analysis of Rediscovered XPDL Process Models

`<package> ⋯ </package>`. The following are the analytical statistics to be analyzed and produced by the structural analyzer, which ought to be extensively represented in the rediscovered XPDL process models through their corresponding extended tags:

- (1)
- Package-level Structural Analysis Statistics
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- The number of process models in a corresponding process package;
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- The number of activities in each model and their usage ratios;
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- The number of roles in each model and their involvement ratios;
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- The number of invoked applications in each model and their usage ratios;
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- The number of relevant data types in each model and their usage ratios;
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- The number of subprocesses in each model and their usage ratios;
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- The usage ratio of each model as subprocesses.

- (2)
- Process-level Structural Analysis Statistics
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- Species of structural patterns and their usage ratios;
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- The number of participants (actors or performers) and their participation ratios;
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- The number of roles and their involvement ratios;
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- The number of invoked applications and their usage ratios;
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- The number of relevant data types and their usage ratios;
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- The number of subprocesses and their usage ratios.

Algorithm 1 The XPDL process model generation algorithm. | |

Require: An activity graph of a rediscovered SICN process model, $\mathsf{\Gamma}$; | |

Ensure: The XPDL process model, $\gamma $ of $\mathsf{\Gamma}$; | |

1: Initialize $\gamma $←∅; | |

2: $\gamma .InsertHeader\left(\right)$; ▹ Insert the header format of XPDL ver 2.1: <xpdl:Pakage...>, <xpdl:PackageHeader> | |

3: $\gamma $.InsertTagAttribute(”xpdl:Processes”); | ▹ Insert the processes attribute to $\gamma $ |

4: $\gamma $.InsertTagAttribute(”xpdl:Activities”); | ▹ Insert the activities attribute to $\gamma $ |

5: for ( ∀ node $\eta $ in $\mathsf{\Gamma}$ ) do▹ Read all the nodes (actvities or gateways) in $\mathsf{\Gamma}$ and then add all of their tags to $\gamma $ | |

6: $\gamma $.InsertTagAttribute(”xpdl:Activity”); | ▹ Insert the activity attribute to $\gamma $ |

7: currentNode ←$\eta $.GetCurrrentNodeInfo(); | |

8: routeType ← defaultRouteType; shapeType ← defaultShapeType; gateType ←∅; | |

9: if (currentNode is OrGateway) then | |

10: routeType ← “Exclusive-OR”; | |

11: shapeType ← “Exclusive-OR Gateway”; | |

12: else if (currentNode is AndGateway) then | |

13: routeType ← “Parallel-AND”; | |

14: shapeType ← “Parallel-AND Gateway”; | |

15: else if (currentNode is LoopGateway) then | |

16: routeType ← “Iterative-LOOP”; | |

17: shapeType ← “Iterative-LOOP Gateway”; | |

18: end if | |

19: if (currentNode is OpenGateway) then | |

20: gateType ← “Split”; | |

21: else if (currentNode is ClosedGateway) then | |

22: routeType ← “Join”; | |

23: end if | |

24: $\gamma $.InsertAttribute(type=activity, currentNode, routeType, shapeType, gateType); | |

25: end for | |

26: $\gamma $.InsertTagAttribute(”xpdl:Transitions”); | ▹ Insert the transition attribute to $\gamma $ |

27: for ( ∀edge $\theta $ in $\mathsf{\Gamma}$ ) do | |

28: currentEdge ←$\theta $.GetEdgeInfo(); | |

29: fromActivityID ← currentEdge.GetFromID(); | |

30: toActivityID ← currentEdge.GetToID(); | |

31: $\gamma $.InsertAttribute(type=transition, fromActivityID, toActivityID); | |

32: end for | |

33: Return$\gamma $; | ▹ Finally output the XPDL process model |

#### 5.4. The Behavioral Analysis of Rediscovered Process Models

Algorithm 2 The behavioral sequence analysis algorithm. | |

Require: A rediscovered SICN process model, $\mathsf{\Gamma}$; | |

Ensure: A Set of Behavioral Sequence Nets, $\mathsf{\Omega}$ of $\mathsf{\Gamma}$; | |

1: initialize $\mathsf{\Omega}$←∅; | |

2: Procedure BSAFunc ( in s $\leftarrow \left\{{\alpha}_{I}\right\},\mathsf{\Omega}$ ); | ▹ Recursive procedure for discovering $\mathsf{\Omega}$ |

3: begin | |

4: $\nu \leftarrow s;\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{\nu \right\}$; | |

5: while ( $u\leftarrow {\delta}_{o}\left(s\right)$ != ${\alpha}_{F}$ ) do | |

6: if ( u is a sequential activity? ) then | |

7: $w\leftarrow u$; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{w\right\}$; | |

8: $\mathsf{\Omega}.{\varrho}_{o}\left(\nu \right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(w\right)\leftarrow \nu $; $\mathsf{\Omega}.{\beta}_{o}\left(\nu \right)\leftarrow {\kappa}_{o}\left(s\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(\nu \right)\leftarrow {\kappa}_{i}\left(s\right)$; | |

9: ${\varphi}_{o}\left((\nu ,o\in {\varrho}_{o}\left(\nu \right))\right)\leftarrow $ occurrences of $o\in {\varrho}_{o}\left(\nu \right)$; | |

10: ${\varphi}_{i}\left((\u03f5\in {\varrho}_{i}\left(\nu \right),\nu )\right)\leftarrow $ occurrences of $\nu $; | |

11: else if ( u is a conjunctive AND-split-gateway activity? ) then | |

12: $w\leftarrow u$; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{w\right\}$; | |

13: $\mathsf{\Omega}.{\varrho}_{o}\left(\nu \right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(w\right)\leftarrow \nu $; $\mathsf{\Omega}.{\beta}_{o}\left(\nu \right)\leftarrow {\kappa}_{o}\left(s\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(\nu \right)\leftarrow {\kappa}_{i}\left(s\right)$; | |

14: for ( each of $\forall \alpha \in {\delta}_{o}\left(u\right)$ ) do | |

15: $x\leftarrow \alpha $; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{x\right\}$; | |

16: $\mathsf{\Omega}.{\varrho}_{o}\left(w\right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(x\right)\leftarrow w$; | |

17: $\mathsf{\Omega}.{\beta}_{o}\left(w\right)\leftarrow {\kappa}_{o}\left(u\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(w\right)\leftarrow {\kappa}_{i}\left(u\right)$; | |

18: ${\varphi}_{o}\left((w,o\in {\varrho}_{o}\left(w\right))\right)\leftarrow $ occurrences of $o\in {\varrho}_{o}\left(w\right)$; | |

19: ${\varphi}_{i}\left((\u03f5\in {\varrho}_{i}\left(w\right),w)\right)\leftarrow $ occurrences of w; | |

20: end for | |

21: for ( each of $\forall \alpha \in {\delta}_{o}\left(u\right)$) do | |

22: call Procedure BSAFunc ( in $s\leftarrow \alpha ,\mathsf{\Omega}$ ); | |

23: end for | |

24: else if ( u is a disjunctive OR-split-gateway activity? ) then | |

25: $w\leftarrow u$; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{w\right\}$; | |

26: $\mathsf{\Omega}.{\varrho}_{o}\left(\nu \right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(w\right)\leftarrow \nu $; $\mathsf{\Omega}.{\beta}_{o}\left(\nu \right)\leftarrow {\kappa}_{o}\left(s\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(\nu \right)\leftarrow {\kappa}_{i}\left(s\right)$; | |

27: for ( each of $\forall \alpha \in {\delta}_{o}\left(u\right)$ ) do | |

28: call Procedure BSAFunc ( in $s\leftarrow \alpha ,\mathsf{\Omega}$ ); | |

29: end for | |

30: else if ( u is either an OR-join-gateway or an AND-join-gateway activity? ) then | |

31: $w\leftarrow u$; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{w\right\}$; | |

32: $\mathsf{\Omega}.{\varrho}_{o}\left(\nu \right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(w\right)\leftarrow \nu $; $\mathsf{\Omega}.{\beta}_{o}\left(\nu \right)\leftarrow {\kappa}_{o}\left(s\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(\nu \right)\leftarrow {\kappa}_{i}\left(s\right)$; | |

33: ${\varphi}_{o}\left((w,o\in {\varrho}_{o}\left(w\right))\right)\leftarrow $ occurrences of $o\in {\varrho}_{o}\left(w\right)$; | |

34: ${\varphi}_{i}\left((\u03f5\in {\varrho}_{i}\left(w\right),w)\right)\leftarrow $ occurrences of w; | |

35: end if | |

36: $s\leftarrow u$; $\nu \leftarrow w$; | |

37: end while | |

38: $w\leftarrow u$; $\mathsf{\Omega}.{A}^{bs}\leftarrow \mathsf{\Omega}.{A}^{bs}\cup \left\{w\right\}$; | ▹ where u is the final activity, ${\alpha}_{F}$ |

39: $\mathsf{\Omega}.{\varrho}_{o}\left(\nu \right)\leftarrow w$; $\mathsf{\Omega}.{\varrho}_{i}\left(w\right)\leftarrow \nu $; $\mathsf{\Omega}.{\beta}_{o}\left(\nu \right)\leftarrow {\kappa}_{o}\left(s\right)$; $\mathsf{\Omega}.{\beta}_{i}\left(\nu \right)\leftarrow {\kappa}_{i}\left(s\right)$; | |

40: ${\varphi}_{o}\left((w,o\in {\varrho}_{o}\left(w\right))\right)\leftarrow $ occurrences of $o\in {\varrho}_{o}\left(w\right)$; | |

41: ${\varphi}_{i}\left((\u03f5\in {\varrho}_{i}\left(w\right),w)\right)\leftarrow $ occurrences of w; | |

42: Return$\mathsf{\Omega}$; | ▹ Finally output a set of behavioral sequence nets |

**Definition**

**2.**

**Behavioral sequence net**of a rediscovered SICN (or XPDL) process model model. Let Ω be a

**BSN**, a behavioral sequence net that is formally defined as $\mathsf{\Omega}=(\varrho ,\kappa ,o)$ over the discovered workitems, ${\mathit{A}}^{bs}$, and the discovered control-transitions, ${\mathit{T}}^{bs}$, where

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- $\varrho ={\varrho}_{i}\cup {\varrho}_{o}$ where, ${\varrho}_{o}:{\mathit{A}}^{bs}\u27f6\wp \left({\mathit{A}}^{bs}\right)$ is a multivalued mapping of a workitem to its immediate successor, and ${\varrho}_{i}:{\mathit{A}}^{bs}\u27f6\wp \left({\mathit{A}}^{bs}\right)$ is a multivalued mapping of a workitem to its immediate predecessor;
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- $\beta ={\beta}_{i}\cup {\beta}_{o}$ where, ${\beta}_{i}$: ${\mathit{A}}^{bs}\u27f6{\mathit{T}}^{bs}$ is a single-valued mapping of a workitem to its connected control-transition, τ, $(\nu \in {\varrho}_{i}\left(\alpha \right),\alpha )$∈${\mathit{T}}^{bs}$; and ${\beta}_{o}$: ${\mathit{A}}^{bs}\u27f6{\mathit{T}}^{bs}$ is a single-valued mapping of a workitem to its connected control-transition, τ, $(\alpha ,\nu \in {\varrho}_{o}\left(\alpha \right))$∈${\mathit{T}}^{bs}$, where $\alpha \in {\mathit{A}}^{bs}$;
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- $o={o}_{i}\cup {o}_{o}$ where, ${o}_{i}$: ${\mathit{T}}^{bs}\u27f6\mathit{N}$ is a single-valued mapping of an incoming control-transition (τ∈${\beta}_{i}$) to its number of occurrences; and ${o}_{o}$: ${\mathit{T}}^{bs}\u27f6\mathit{N}$ is a single-valued mapping of an outgoing control-transition (τ∈${\beta}_{o}$) to its number of occurrences.

#### 5.5. Implementation of the Integrated Functional Architecture

## 6. Experimental Validation Studies

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- Experimental Dataset-1: Mining and analyzing a structured information control net process model from the enactment event log dataset of the large bank transaction process model:
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- Title: Large Bank Transaction Process;
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- Creator: Munoz-Gama. J. (Jorge);
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- Date accepted and published: 2014-08-21;
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- Description: Synthetic Bank Transaction Process;
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- Models: Petri net, large, stand-alone, and SESE-aided decomposed;
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- Logs: large, with and without noise, and two particular scenarios;
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- Additional: model diagram, decomposition diagram, and activity re-naming.

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- Experimental Dataset-2: Mining and analyzing a structured information control net process model from the enactment event log dataset of the loan application example (Configuration 1) process model:
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- Title: Loan Application Example, Configuration 1;
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- Creator: Buijs, J.X.A.M.;
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- Date accepted and published: 2013-04-16;
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- Description: A collection of artificial event logs describing four variants of a simple loan application process. Variant 1 is the most complex process with parallelism and choices. The other three variants have simpler, more sequential control flows, and some activities of Variant 1 are missing or split into two. These event logs are used to test different approaches of discovering a configurable process model from a collection of event logs.

#### 6.1. Validation of the Rediscovered SICN Process Models

#### 6.2. Experimental Validation I: The Process Mining with Structural Analysis

#### 6.3. Experimental Validation II: The Process Mining with Behavioral Analysis

## 7. Conclusions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Mining and analyzing activities on a process-aware enterprise: the rationale of functional integration of process mining and process analyzing.

**Figure 2.**The ultimate architecture of the functional integration of process mining and process analyzing.

**Figure 3.**The structural control-flow primitives in structured information control net process models.

**Figure 7.**The interactive and visual analysis of behavioral sequences of the rediscovered process model.

**Figure 10.**The rediscovered structured information control nets (SICN) process model from the Experimental Dataset-1.

**Figure 11.**The rediscovered SICN process model with activities’ occurrences from the Experimental Dataset-2.

**Figure 12.**The quantitative analysis of structural constructs and business activities in the rediscovered SICN process model from the Experimental Dataset-1.

**Figure 14.**The interactive and visual tracing of behavioral sequences on the rediscovered SICN process model from the Experimental Dataset-1.

**Figure 15.**The rediscovered SICN process model with very large-scale and massively parallel business activities.

Package | Process | Activity | Transition | Application | Data-Field | Participant |
---|---|---|---|---|---|---|

-Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. | -Identifier -Name -Descrip. -Ex_Attr. |

-XPDL -Source -Creation -Version -Author -Country -Publ_St -Conform -Priority | -Cre_Date -Version -Author -Code_P -Country -Publ_St -Priority -Limit. -Val_Fr -Val_To | -A_Mode -Split -Join -Priority -Limit. -St_Mode -Fi_Mode -Deadline | -Data_Ty | -Par_Ty | ||

-Respon. -Ext_Pac | -Params. -Respon. | -Perf. -Tool -Subflow -Act_Set -A_param | -Conditi. -From_Tr -To_Tr | -Params. | -Ini_Val. | |

-Docum. -Icon | -Docum. -Icon | -Docum. -Icon | ||||

-Cost_Unt | -Dur_Unt -Duration -Wait_T -Work_T | -Cost -Duration -Wait_T -Work_T |

BS Species ID | Number of BSs | The BS IDs in the Species | Number of Events |
---|---|---|---|

BS-1 | 1 | 92 | 8 |

BS-2 | 12 | 44,45,46,47,48,49,50,51,52,53,54,55 | 8 |

BS-3 | 26 | 56,57,58,59,60,61,62,63,64,65,66,67, 68,69,70,71,72,73,74,75,76,77,78,79, 80,81 | 8 |

BS-4 | 2 | 97,98 | 8 |

BS-5 | 4 | 93,94,95,96 | 8 |

BS-6 | 8 | 82,83,84,85,86,87,88,89 | 7 |

BS-7 | 38 | 6,7,8,9,10,11,12,13,14,15,16,17,18, 19,20,21,22,23,24,25,26,27,28,29,30, 31,32,33,34,35,36,37,38,39,40,41,42,43 | 8 |

BS-8 | 6 | 0,1,2,3,4,5 | 8 |

BS-9 | 1 | 99 | 7 |

BS-10 | 1 | 90 | 7 |

BS-11 | 1 | 91 | 8 |

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## Share and Cite

**MDPI and ACS Style**

Kim, K.P.
Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets. *Appl. Sci.* **2020**, *10*, 1493.
https://doi.org/10.3390/app10041493

**AMA Style**

Kim KP.
Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets. *Applied Sciences*. 2020; 10(4):1493.
https://doi.org/10.3390/app10041493

**Chicago/Turabian Style**

Kim, Kwanghoon Pio.
2020. "Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets" *Applied Sciences* 10, no. 4: 1493.
https://doi.org/10.3390/app10041493