=Paper=
{{Paper
|id=Vol-3758/paper-19
|storemode=property
|title=From Conventional to IoT-Enhanced: Object-Centric Event Logs for Real-Life Logistics Processes
|pdfUrl=https://ceur-ws.org/Vol-3758/paper-19.pdf
|volume=Vol-3758
|authors=Jia Wei,Chun Ouyang,Weiguang Ma,Deyou Jiang,Jianglan Xia,Arthur ter Hofstede,Ying Wang,Lei Huang
|dblpUrl=https://dblp.org/rec/conf/bpm/WeiOMJXHWH24
}}
==From Conventional to IoT-Enhanced: Object-Centric Event Logs for Real-Life Logistics Processes==
https://ceur-ws.org/Vol-3758/paper-19.pdf
From Conventional to IoT-Enhanced: Simulated
Object-Centric Event Logs for Real-Life Logistics Processes
Jia Wei1,* , Chun Ouyang1,* , Weiguang Ma2 , Deyou Jiang2 , Jianglan Xia2 ,
Arthur ter Hofstede1 , Ying Wang2 and Lei Huang2
1
Queensland University of Technology (QUT), 2 George St, Brisbane City QLD 4000, Australia
2
Beijing Jiaotong University (BJTU), No.3 Shangyuancun, Haidian District, Beijing 100044, China
Abstract
With the growth of Internet-of-Things (IoT) applications, integrating IoT data into business processes has received
increasing attention. IoT data, typically low-level, is unsuitable to be directly integrated with event logs that
capture high-level process information. Compared to XES event log representation, Object-Centric Event Log
(OCEL) 2.0 is better suited for integration as it captures intricate object-event relationships in processes. We
present two OCEL 2.0 logs simulating the cargo pickup process at a Chinese port: one for the traditional process
and the other incorporating IoT technology. These logs advance event log representations and research on
integrating IoT data into business processes.
Keywords
Object-Centric Event Log, Simulated Event Log, Logistics Process, Internet of Things
1. Introduction
As the number of IoT applications increases, more research focuses on integrating IoT data into business
processes. De Luzi et al. [1] conduct a systematic literature review of existing approaches to IoT-aware
business process management (BPM). Janiesch et al. [2] highlight the benefits and 16 challenges of
integrating IoT and BPM. Our work aims to address one of these challenges—“bridging the gap between
sensor data and event logs for process mining”. Since IoT data is usually low-level, and event logs
contain relatively high-level process execution information, it is often not suitable to integrate IoT data
directly into event logs.
In process mining, there are two types of event log representations: XES [3] and OCEL [4]. XES
logs are formatted as tables, each row representing an event related to a single object (a.k.a. case) and
each column specifying an event attribute. OCEL, on the other hand, is represented as a relational
database that captures the objects involved in a process and their interactions with events. The recently
proposed Object-Centric Event Data (OCED) meta-model [5] further extends OCEL by introducing
dynamic object attributes and relationships between objects.
Some existing IoT-enriched event logs [6, 7] integrate low-level IoT data into processes following the
XES standard. However, the XES format is limited to a single-object perspective, making it unsuitable
for capturing processes that involve multiple interacting objects, such as business entities and IoT
devices. Mangler et al. [8] propose an IoT-enriched event log converted from XES to OCEL 1.0. However,
this log fails to capture the relationships between business objects and their interactions with events.
Moreover, it is unclear how the interactions between IoT devices and business processes are represented
in such an event log.
In our work, we incorporate process-related information captured by IoT devices into event logs. We
adopt the OCEL 2.0 schema which is better suited for the integration as it captures object interrelation-
Proceedings of the Best BPM Dissertation Award, Doctoral Consortium, and Demonstrations & Resources Forum co-located with
22nd International Conference on Business Process Management (BPM 2024), Krakow, Poland, September 1st to 6th, 2024.
*
Corresponding author.
$ jia.wei@hdr.qut.edu.au (J. Wei); c.ouyang@qut.edu.au (C. Ouyang); 21113054@bjtu.edu.cn (W. Ma);
20113053@bjtu.edu.cn (D. Jiang); xiajianglan20020610@outlook.com (J. Xia); a.terhofstede@qut.edu.au (A. t. Hofstede);
ywang1@bjtu.edu.cn (Y. Wang); lhuang@bjtu.edu.cn (L. Huang)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
ships and their interactions with process events. Existing IoT-enriched event logs [6, 7, 8] record IoT
data into event logs following the XES standard or OCEL 1.0. Unlike their work, our work generates
event logs that not only conform to the OCEL 2.0 schema but also introduce extensions to integrate IoT
data into event logs.
In this paper, we present two OCEL 2.0 logs, both generated through simulations using CPN, that
aim to capture the cargo pickup process in one of the major ports in China. The two logs and the CPN
models used to generate them are available at https://github.com/JennyJiaW/OCELs_CargoPickup. The
first log aims to represent the conventional cargo pickup process, encompassing multiple object types
such as cargo, pickup plans, trucks, and silos. It also aims to capture the static and dynamic relationships
between objects as well as their interactions with process events. The second log builds upon the first
with the aim to integrate IoT data to capture relationships between IoT objects and business objects, as
well as between IoT device entities and process events.
By simulating a real-life process, the two OCEL logs produced from this work serve as valuable public
data resources for the BPM research community. These logs can provide the community with insights
to enhance OCEL log representations, and contribute to future research on integrating IoT data with
process event logs.
2. Description of Resources
We present the two aforementioned OCEL logs for the conventional cargo pickup process at a Chinese
port and its IoT-enhanced process, respectively. Table 1 lists the object types and their corresponding
attributes in both logs. Table 2 lists IoT device types used in the cargo pickup process.
Table 1
Object types and corresponding attributes involved in the cargo pickup process
Object Type Attribute Name
Pickup Plan PickupPlanID, CargoID, Num of trucks, Total Pickup Weight
TruckID, LPT(LicensePlateNo), Axles, PickupPlanID, CargoID,
Truck
Scheduled Pickup Weight, Truck Status, Truck Weight * , RFID No** , Is_normal **
Cargo CargoID, Cargo Type, Cargo Stock Weight (scheduled), SiloID
SiloID, Silo Status, Temperature** , Humidity ** , Silo Temperature** ,
Silo
Grain Temperature**
*
Attribute value can be captured manually or by IoT devices.
**
Attribute value is captured only by IoT devices.
Table 2
IoT device types and corresponding attributes involved in the IoT-enhanced cargo pickup process
IoT Device Type Attribute Name
Weight Sensor IoTDeviceID, Location, Type
Temperature Sensor
Humidity Sensor
IoTDeviceID, timestamp, Location, Value, (Measurement)Unit
IndoorTemperature Sensor
GrainTemperature Sensor
2.1. The conventional cargo pickup process
Figure 1 depicts an overview of the conventional cargo pickup process. The process begins when the
customer lodges a pickup plan to arrange trucks for cargo pickup. On the scheduled date, each truck
arrives at the port and is weighed to record its empty weight. The truck proceeds to the designated silo
to load the cargo. After loading, the truck is weighed again to record its loaded weight. The port then
issues a weighing ticket and a tally sheet, and the truck departs.
In this simulated OCEL log, we include four types of relational tables:
Figure 1: A value chain modelling an overview of the conventional cargo pickup process
• Event tables: Capture the temporal order of events and record event information, including
Event_id, Activity, Timestamp, and event attributes (if any).
• Object tables: Document static and dynamic attributes of each object, including Object_id,
Timestamp, Ocel_changed_field (indicating which attribute, if any, changed), and attributes.
The pickup plan object table is an exception, omitting the Ocel_changed_field since all attribute
values are generated at the start of the process.
• Event-to-Object relation (E2O) table: Records relationships between objects and events dur-
ing process execution, including Event_id, Object_id, and an E20_qualifier specifying the
semantics of each E2O relationship.
• Object-to-Object relation (O2O) table: Records relationships between objects, including
Source_object_id, Target_object_id, an O2O_qualifier specifying the semantics of each O2O
relationship, and a Timestamp, as these relationships may change during process execution.
2.2. The IoT-enhanced cargo pickup process
As shown in Figure 2, the cargo pickup process in this real-world scenario has evolved with the adoption
of IoT technologies. Activities in italics relate to the IoT devices listed in Table 2. These activities could
be new process activities arising from the use of IoT devices or existing process activities enhanced
by incorporating IoT devices. For example, the “weigh the empty truck” and “weigh the loaded truck”
activities utilise real-time data from weight sensors. In addition, two new activities, “Check empty truck
weight abnormality” and “Determine the continuance of the pickup”, have been introduced due to IoT
integration.
When a truck enters the weighbridge, an RFID tag on its windshield is read, recording past empty
truck weights and manufactured weight. By comparing the current empty weight with the historical
average, weight anomalies can be detected in real-time, preventing fraudulent deliveries at ports.
Furthermore, because of the inclusion of real-time data from temperature and humidity sensors in silos,
an activity is introduced to determine if the current pickup meets the continuation criteria. For instance,
if a truck is picking up rice, silo staff will verify if the rice meets discharge criteria by ensuring the
grain’s temperature is higher than the dew-point temperature, which is calculated from atmospheric
temperature and humidity.
As a result, in addition to the four types of tables in the previous log, this simulated OCEL log contains
two new relational tables:
• IoTDevice-to-Object relation (IoT2O) table: Records the relationship between IoT devices and
business objects, and consists of columns: IoT_object_id, Object_id, an IoT2O_qualifier
specifying the semantics of each IoT2O relationship and a Timestamp, as IoT2O relationships
may change during execution of the process.
• IoTDevice-to-Event relation (IoT2E) table: Records the relationship between IoT devices
and events in the process and is comprised of columns: Event_id, IoT_object_id and
IoT2E_qualifier to specify the meaning of their relationship.
Moreover, in the IoT-enhanced cargo pickup process, there are two types of interactions between IoT
devices and business processes: push and pull interactions [9].
Figure 2: A value chain, annotated with involvement of IoT devices, modelling an overview of the IoT-enhanced
cargo pickup process
• Push Interaction: IoT devices automatically send data to the business process. For instance,
when a truck arrives at the platform of the weighbridge, a weight sensor makes the real-time
weight of the truck available to the process.
• Pull Interaction: Data collected by IoT devices are requested on demand. That is, interactions
are triggered by the business processes. For instance, environmental sensors continuously
measure the temperature or humidity of the environment; only when the activity “Determine the
continuance of the pickup” is executed are the aggregated temperature and humidity data made
available to the process.
3. Preliminary Analysis
3.1. Generation of Simulated Event Logs using CPN
A simulation approach to generate the two event logs was used as though the cargo pickup process
originates from a real-world scenario, obtaining real data directly from the port system is challenging.
For each business process, two CPN models were created using CPN Tools1 , one concerned with object
initialisation and definition of static and dynamic attributes (referred to as CPN𝑖 and CPN𝑖IoT resp.) and
one modelling the business process (referred to as CPNbp and CPNbp IoT resp.). These four CPN models
were then used to generate the two simulated event logs correspondingly.
The simulated values for all static and dynamic attributes follow a normal distribution with parameters
informed by domain knowledge. Dynamic attributes were initially set to 0.0 or null, depending on their
data type. In addition, the time frame and frequency of truck arrivals at the port are designed according
to domain knowledge, with truck arrival following an exponential distribution. The “process” CPN
models (CPNbp and CPNbp IoT ) capture dynamic attribute changes and when these occur. For CPNIoT ,
bp
the IoT device types and their attributes (see Table 2) serve as inputs for certain process activities (see
Figure 2), simulating the interactions between the business processes and the IoT devices.
The two logs, the CPN models used to generate them, and the documentation on how to generate
these simulated logs are available at https://github.com/JennyJiaW/OCELs_CargoPickup.
3.2. Basic Statistical Analysis
In this subsection, we compare some basic statistics of the two simulated OCEL logs as shown in Table 3.
These statistics were obtained from tables generated from the CPN simulation, stored in an SQLite
database, and analysed using the pm4py package2 .
Cargo theft may be enabled by modified trucks. In the conventional process, truck weights are
recorded manually, while IoT technology (weight sensor and RFID tag for each truck) allows their
1
https://cpntools.org/
2
https://pm4py.fit.fraunhofer.de/
Table 3
Some basis statistics of the simulated OCEL Logs for the conventional cargo pickup process and its IoT-enhanced
version
Description IoT-enhanced Conventional Event occurrences IoT-enhanced Conventional
Number of events 3611 3447 Lodge Pickup Plan 10 10
Number of objects 80 70 Assign Truck 491 491
Number of activities 13 8 Enter the port 491 N/A
Number of object types 4 3 Weigh the Empty Truck 491 491
E2O relations 3621 3457 Check the Empty Truck Weight Abnormality 491 N/A
O2O relations 883 992 Fail to Weigh 300 N/A
IoT2E relations 2619 N/A Arrive at the Silo 191 N/A
IoT2O relations 1637 N/A Determine the Continuance of the Pickup 191 N/A
Objects occurrences (number of objects) Load Truck 191 491
Truck 50 50 Weigh the Loaded Truck 191 491
Cargo 10 10 Evaluate the Truck Exit 191 491
Pickup Plan 10 10 Input the Tally Sheet 191 491
Silo 10 N/A Print the Weighing Ticket 191 491
automatic capture and comparison with their historical weights to detect whether there is a significant
deviation from the past. If so, the weighbridge alerts the port and the truck is prevented from picking
up the cargo. Table 3 shows that “Fail to Weigh” and “Weigh the Loaded Truck” occurred 300 resp. 191
times, hence around 39% of pickups were successfully completed.
4. Conclusion
We present two OCEL logs generated to simulate the cargo pickup process in a Chinese port as an
example of real-life logistics processes. Unlike existing process event logs incorporating IoT data, we
focus on generating logs that conform to the OCEL 2.0 schema as well as integrating process-related
information captured by the IoT data. The two OCEL logs produced from this work serve as valuable
public data resources for the BPM research community. In future work, we plan to extend these IoT-
enriched event logs by incorporating additional IoT data and analysing the resulting logs to understand
how IoT data impacts process performance. We aim to use the insights from this study to inspire the
community to advance event log representation for real-life processes and to further research on the
integration of IoT data with process event logs.
References
[1] F. De Luzi, F. Leotta, A. Marrella, M. Mecella, On the Interplay Between Business Process Manage-
ment and Internet-of-Things, Bus. Inf. Syst. Eng. (2024) 1–24.
[2] C. Janiesch, A. Koschmider, M. Mecella, B. Weber, A. Burattin, et al., The Internet of Things Meets
Business Process Management: A Manifesto, IEEE SMC 6 (2020) 34–44.
[3] IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and
Event Streams, IEEE Std 1849-2016 (2016) 1–50.
[4] A. Berti, I. Koren, J. N. Adams, G. Park, B. Knopp, N. Graves, M. Rafiei, L. Liß, et al., OCEL
(Object-Centric Event Log) 2.0 Specification, arXiv preprint arXiv:2403.01975 (2024).
[5] W. M. van der Aalst, Object-Centric Process Mining: Unraveling the Fabric of Real Processes,
Mathematics 11 (2023) 2691.
[6] L. Malburg, J. Grüger, R. Bergmann, An IoT-Enriched Event Log for Process Mining in Smart
Factories, 2023. URL: https://doi.org/10.6084/m9.figshare.20130794.
[7] J. Mangler, M. Ehrendorfer, XES Chess Pieces Production (4.0), 2023. URL: https://doi.org/10.5281/
zenodo.7958478.
[8] J. Mangler, M. Ehrendorfer, J.-V. Benzin, OCEL Chess Pieces Production (1.0), 2023. URL: https:
//doi.org/10.5281/zenodo.8139445.
[9] P. Valderas, V. Torres, E. Serral, Modelling and executing IoT-enhanced business processes through
BPMN and microservices, JSS 184 (2022) 111139.