Financial process mining - Accounting data structure dependent control flow inference

2017 ◽  
Vol 25 ◽  
pp. 57-80 ◽  
Author(s):  
Michael Werner
Keyword(s):  
Data Structure ◽  
Process Mining ◽  
Control Flow ◽  
Accounting Data ◽  
Financial Process

scholarly journals Translating Workflow Nets to Process Trees: An Algorithmic Approach

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 279
Author(s):  
Sebastiaan J. van Zelst ◽  
Sander J. J. Leemans
Keyword(s):  
Process Modeling ◽  
Time Complexity ◽  
Explicit Knowledge ◽  
Process Mining ◽  
Behavioral Control ◽  
Control Flow ◽  
Algorithmic Approach ◽  
Modeling Formalism ◽  
Mining Algorithms ◽  
Process Tree

Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a sound workflow net is trivial. However, the reverse is not the case. Simultaneously, an algorithm that translates a WF-net into a process tree is of great interest, e.g., the explicit knowledge of the control-flow hierarchy in a WF-net allows one to reason on its behavior more easily. Hence, in this paper, we present such an algorithm, i.e., it detects whether a WF-net corresponds to a process tree, and, if so, constructs it. We prove that, if the algorithm finds a process tree, the language of the process tree is equal to the language of the original WF-net. The experiments conducted show that the algorithm’s corresponding implementation has a quadratic time complexity in the size of the WF-net. Furthermore, the experiments show strong evidence of process tree rediscoverability.

Determine Execution Time and Selection Probabilities in Process Mining via Petri Nets

2013 ◽  
Vol 760-762 ◽  
pp. 1951-1958 ◽  
Author(s):  
Huan Zhou ◽  
Chuang Lin ◽  
Yi Ping Deng ◽  
Zi Cheng Wan
Keyword(s):  
Decision Making ◽  
Petri Net ◽  
Time Perspective ◽  
Process Mining ◽  
Research Process ◽  
Path Selection ◽  
Control Flow ◽  
Selection Probabilities ◽  
Delay Prediction ◽  
Novel Algorithm

In process mining research, process discovery techniques can produce or rebuild models with the information from logs. There are already algorithms supporting control-flow perspective mining which focus on the order of events and provide understanding workflow paths. But few of them take time perspective and path selection probabilities into consideration, which are important in performance evaluating, delay prediction, decision making, as well as process redesigning and optimizing. This paper provides a novel algorithm which determines the information of time perspective and selection probabilities from a log and integrates them with the control-flow perspective. By applying this algorithm, a stochastic Petri net is provided which is useful in performance analyzing and process optimizing.

Control flow and data structure documentation

1982 ◽  
Vol 25 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Ben Shneiderman
Keyword(s):  
Data Structure ◽  
Control Flow

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

2016 ◽  
Vol 14 (1) ◽  
pp. 349 ◽  
Author(s):  
Riyanarto Sarno ◽  
Widyasari Ayu Wibowo ◽  
Kartini Kartini ◽  
Yutika Amelia ◽  
Kelly Rossa
Keyword(s):  
Flow Pattern ◽  
Process Model ◽  
Process Mining ◽  
Control Flow ◽  
And Control

scholarly journals An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

Process Logo: An Approach for Control-Flow Visualization of Information System Process in Process Mining

2021 ◽  
pp. 481-492
Author(s):  
M. V. Manoj Kumar ◽  
B. S. Prashanth ◽  
H. R. Sneha ◽  
Likewin Thomas ◽  
B. Annappa ◽  
...  
Keyword(s):  
Information System ◽  
Flow Visualization ◽  
Process Mining ◽  
Control Flow ◽  
System Process

scholarly journals Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Weidong Zhao ◽  
Xi Liu ◽  
Weihui Dai
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Fitness Function ◽  
Control Flow ◽  
Process Models ◽  
Programming Approach ◽  
Event Logs ◽  
Process Complexity

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

scholarly journals SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

2021 ◽  
Author(s):  
Stephan A. Fahrenkog-Petersen ◽  
Martin Kabierski ◽  
Fabian Rosel ◽  
Han van der Aa ◽  
Matthias Weidlich
Keyword(s):  
Process Mining ◽  
Control Flow

scholarly journals Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 1389-1393
Keyword(s):  
Business Process ◽  
Free Choice ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Event Data ◽  
Process Discovery ◽  
Event Logs ◽  
Complex Process ◽  
Mining Algorithms

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.

scholarly journals Call Graph and Model Checking for Fine-Grained Android Malicious Behaviour Detection

2020 ◽  
Vol 10 (22) ◽  
pp. 7975
Author(s):  
Giacomo Iadarola ◽  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Antonella Santone
Keyword(s):  
Data Structure ◽  
Model Checking ◽  
Private Information ◽  
Control Flow ◽  
Graph Data ◽  
Call Graph ◽  
Fine Grained ◽  
Call Graphs ◽  
Android Applications ◽  
Flow Graphs

The increasing diffusion of mobile devices, widely used for critical tasks such as the transmission of sensitive and private information, corresponds to an increasing need for methods to detect malicious actions that can undermine our data. As demonstrated in the literature, the signature-based approach provided by antimalware is not able to defend users from new threats. In this paper, we propose an approach based on the adoption of model checking to detect malicious families in the Android environment. We consider two different automata representing Android applications, based respectively on Control Flow Graphs and Call Graphs. The adopted graph data structure allows to detect potentially malicious behaviour and also localize the code where the malicious action happens. We experiment the effectiveness of the proposed method evaluating more than 3000 real-world Android samples (with 2552 malware belonging to 21 malicious family), by reaching an accuracy ranging from 0.97 to 1 in malicious family detection.

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