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****************************** General info *******************************

The dataset contains a collection of experiment results and event logs 
generated. The experiment comprises a job-shop scheduling problem, 
implemented in a discrete-event simulation model. The raw experiment 
results are given from which event log files can be generated by following 
the steps as described in this data paper or the referred paper. A 
collection of event log files is given, as well as the raw files. The logs 
include the filtered part of the case study as presented in the paper "An 
agent-based process mining architecture for emergent behavior analysis" by 
Rob Bemthuis, Martijn Koot, Martijn Mes, Faiza Bukhsh, Maria-Eugenia Iacob,
and Nirvana Meratnia. 

[paper abstract]
The abstract of the academic paper (see References) where this dataset 
paper is based on is as follows: 

Information systems leave a traceable digital footprint whenever an action 
is executed. Business process modelers capture these digital traces to 
understand the behavior of a system, and to extract actual run-time models 
of those business processes. Despite the omnipresence of such traces, most 
organizations face substantial differences between the process 
specifications and the actual run-time behavior. Analyzing and implementing
the results of systems that model business processes tend, however, to be 
difficult due to the inherent complexity of the models. Moreover, the 
observed reality in the form of lower-level real-time events, as recorded 
in event logs, is seldom solely explainable by higher-level process models.
In this paper, we propose an architecture to model system-wide behavior by 
combining process mining with a multi-agent system. Digital traces, in the 
form of event logs, are used to iteratively mine process models from which 
agents can learn. The approach is initially applied to a case study of a 
simplified job-shop factory in which automated guided vehicles (AGVs) carry
out transportation tasks. Numerical experiments show that the workflow of a
process mining model can be used to enhance the agent-based system, 
particularly, in analyzing bottlenecks and improving decision-making. 

[paper keywords]
Multi-agent System; Process Mining; Emergent Behavior, Enterprise 
Architecture; Supply Chain Logistics; Job-shop; Internet of Things

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********************************** Erratum*********************************

Update 8th of January 2020: the used plugin in step 4.1 of the example 
contained a mistake, it should be "Replay a Log on Petri Net for 
Conformance Analysis" and not "Replay a Log on Petri Net for Performance/
Conformance Analysis"

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********************************** Files **********************************

The raw results from the simulation model are presented in the repository 
[RawData/Events.txt] and the filtered event logs are stored in the 
repository [FilteredFiles/Experiment.xes]. 

**[RawData/Events.txt]**
Describes the event data generated as output of the simulation study 
conducted. Upon request, we can provide more experimental results, for that
please contact the corresponding authors. 

* File naming convention *
Experiment{XYZ}.tzt, where:
	X = number of vehicles {4,5,6}
	Y = vehicle driving direction {1 = forward; 2 = backward; 3 = forward and backward}
	Z = vehicle dispatching rule {1 = random; 2 = longest waiting time; 3 = nearest vehicle}

* File format *
.txt with tab-separated values. 

* File content *
ID = unique identifier of event;
Timestamp = YYYY/MM/DD HH:MM:SS.MS;
Product = type of product {Console; Helicopter; Robot} followed by a unique identifier;
Type = type of product {Console; Helicopter; Robot};
Event = activity {Arrival; Drain; Drilling; Painting; Sawing; Transport; Welding};
Status = life cycle {Blocked; Complete; In progres; Start; Waiting};
Resource = additional information about a utilized resource such as entering source (e.g., JobShop.Source; JobShop.Buffer), departing sink (e.g., JobShop.Drain), vehicle (e.g., AGV:1; AGV:2), machine entrance buffer (e.g., JobShop.Welding.Input), machine process (e.g., JobShop.Welding.Machine), and machine exit buffer (e.g., JobShop.Welding.Output). 

* Additional remarks *
*All experiments are conducted with the same random seed values
*Only one replication is conducted per experiment
*The run length of one experiment is 24 hours
*A warm-up period is not taken into account
*.MUs = moveable units. 

**[FilteredFiles/Experiment.xes]**
The {Experiment.xes} files are the input files for determining the quality 
metrics of the process models and the key performance indicators (e.g.,
throughput times).

* File format *
.xes = Extensible Event Stream

* File naming convention *
{XYZ}.xes, similar to naming convention of **[RawData/Events.txt]**


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******************************** Example **********************************

This part describes an example of going from the raw simulation output 
files to process model quality metrics and the throughput time KPI. This 
example considers a manual execution of all tasks. 

0. We consider in this example the experiment 411. Used tool: ProM Lite 
1.2; Experiments are conducted on the 31st of May 2019;

1. Convert the event logs from .csv to .xes format:
	1.1. Import the .txt file into ProM
	1.2. Convert .txt to .xes using the 'Convert CSV to XES' filter
		1.2.1. In the Configure Conversion from CSV to XES form, select as Case Column = Product; Event Column = Event; Start Time = Timestamp and erase the Completion Time declaration
		1.2.2. Open Show Expert Configuration and set XES Extension for Type to "concept:instance (Concept)", for Status to "lifecycle:transition (Lifecycle)", and for Resource to "org:resource (Organization)"
		1.2.3. Check: processes = 1; cases = 1450; events = 47575; event classes = 24; event types = 4; originators = 19
	
2. Filter Log using Simple Heuristics plugin
	2.1. Apply log filter to all event types
	2.2. Start events = Arrival+Start
	2.3. End events = Drain+Complete
	2.4. Event filter = include all events
	2.5. Check: processes = 1; cases = 1296; events = 44880; event classes = 24; event types = 4; originators = 19
	
3. Construct the Petri-net by using the Alpha Miner plugin
	3.1. Configure Alpha Miner: Event Classifier = (Event Name AND Lifecycle transition); Version = Alpha
	
4. Replay the log on the Petri-net for performance/conformance analysis
	4.1. Use the 'Replay a Log on Petri Net for Conformance Analysis' plugin with the constructed Petri-net and Filtered event logs
	4.2. Create a final marking as follows: Add ([Drain+Start],[Drain+Complete]) to the Candidate Final Marking list
	4.3. Select as classifier in the 'Map Transitions to Event Classes' the 'Event Name'. 
	4.4. Verify that Measuring fitness is checked. Do not penalize improper completion. 

5. Open the visualizer
	5.1. In the Inspector go to the tab Info and select Global Statistics (non-filtered traces)
	5.2. Verify that the Trace Fitness is 0.9997733410493826
	5.3. Change the visualization to Time Between Transition Analysis (PNetAlignmentAnalysis)
	5.4. Check that the average time from Arrival+Complete to Drain+Complete is 73.34 minutes

6. Go further by using as input (1) Replay result, (2) filtered event logs, and (3) constructed Petri-net
	6.1. Select the 'Measure Precision/Generalization' plugin
	6.2. Verify that the precision equals 0.21834 and the generalization equals 0.77938


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******************************* References ********************************

Bemthuis, R.H., Koot, M., Mes, M.R.K., Bukhsh, F.A., Iacob, M.-E, & 
Meratnia, N. An agent-based process mining architecture for emergent 
behavior analysis. In 2019 IEEE 23rd International Enterprise Distributed 
Object Computing Workshop (EDOCW). IEEE, in press. 

Bemthuis, R.H., Koot, M., Mes, M.R.K., Bukhsh, F.A., Iacob, M.-E, & 
Meratnia, N. (2019). Data underlying the paper: An agent-based process 
mining architecture for emergent behavior analysis. Dataset, University of
Twente - 4TU.Centre for Research Data. 
https://doi.org/10.4121/uuid:9e430177-1dd0-40e9-b48a-8eb39124ef4c


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***************************** Contact details *****************************

Corresponding authors: 

R. H. Bemthuis (Rob)
r.h.bemthuis@utwente.nl
University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands

M. Koot (Martijn)
m.koot@utwente.nl
University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands


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**************************** Acknowledgements *****************************

This project is funded by the Netherlands Organization for Scientific 
Research (NWO) (under grant 628.009.015). The authors thank all partners of
the DataRel project. 