=Paper=
{{Paper
|id=Vol-3783/paper_217
|storemode=property
|title=Traceability in Process Analysis
|pdfUrl=https://ceur-ws.org/Vol-3783/paper_217.pdf
|volume=Vol-3783
|authors=Maike Basmer
|dblpUrl=https://dblp.org/rec/conf/icpm/Basmer24
}}
==Traceability in Process Analysis==
https://ceur-ws.org/Vol-3783/paper_217.pdf
Traceability in Process Analysis
Maike Basmer1
1
Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Abstract
The exploratory nature of process analysis requires the analysts to make decisions not only during the analysis
but also during data preparation, which affects the outcome of the analysis. This PhD project aims to support
traceability in process analysis, i.e., reconstructing the effect of the input data and the decisions made throughout
the process analysis pipeline on the outcome. To accomplish this, we plan to leverage established data management
capacities to integrate the models used for pre-processing and analysis.
Keywords
process analysis, traceability, database systems
1. Motivation
Process analysis often follows an exploratory approach: while scrutinizing the event data captured from
a process, analysts continuously build hypotheses and subsequently seek to falsify or validate them
based on the data. This potentially involves comparing different process mining algorithms or testing
different parameters. However, the exploration of the event data does not just start with the analysis,
but rather when the data is prepared, as the data extraction, transformation, and loading (ETL) may also
be subject to frequent change. Thus, decoupling the analysis from the data preparation possibly hides
the effect of the choices made during the ETL steps on the analysis outcome. That does not only make
it difficult to relate the results of the analysis to the original data, but also to judge the reliability of the
results at large. Database technology appears to have the means in store to address that challenge, as
they allow to integrate the ETL process and analysis using unified data models and query languages.
Instead of extracting the event data to a log, one may keep the data close to the source, thus allowing
to trace the analysis results to the source data. Ultimately, this affords the opportunity to reason on
the propagation or interplay of changes in the pre-processing phase with respect to the analysis. That
way, process analysts are supported in tracking and understanding the impact of decisions made during
data preparation and analysis, which enables them to justify those decisions. Furthermore, adopting
standard data models and query languages as the basis for this integration enables us to leverage the
capacity of database systems and the research on them spanning decades to support the process analysis.
Accordingly, the complex of problems that is going to be addressed in the PhD thesis can be summarized
as follows:
Objective of PhD Project
We aim at achieving traceability in process analysis, i.e., the ability to trace analysis results to the input
data. To accomplish this, we will integrate the models used for data pre-processing and data analysis,
and operationalize them using existing data management capacities.
ICPM 2024 Doctoral Consortium, October 14–18, 2024, Kongens Lyngby, Denmark
$ maike.basmer@hu-berlin.de (M. Basmer)
0000-0003-2124-0276 (M. Basmer)
© 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
2. Related Work
2.1. Database Technology in Process Mining
Within the relational realm, intermediate in-database representations and a native in-database operator
have been developed to accelerate process mining tasks [1, 2]. Furthermore, concepts from data
warehousing were adopted to facilitate multidimensional analysis [3, 4]. Schönig et al. [5, 6] implemented
declarative process discovery on relational databases. Riva et al. [7] considered different schemata
that have been proposed to represent event logs in the past and examined the effect of the schema
choice on the performance of declarative process mining. Besides that, modelling event data as labeled
property graphs [8] was proposed to enable graph-based understanding of multi-dimensional data and
to accommodate different analyses [9].
2.2. Supporting the Process Analysis Pipeline
A process analysis pipeline may encompass different pre-processing steps like integrating, transforming,
reducing, abstracting, filtering, or enriching the event log before the analysis [10], with abstraction
currently being the focus for our setting. Different approaches to event abstraction exist, mainly lifting
low-level events to activities according to the domain [11, 12]. Other types of high-level events may
also be discovered to enhance the analysis of processes [9, 13].
Regarding traceability, there have been several proposals in the past. Probabilistic event abstraction
allows to keep track of alternative abstractions by capturing uncertainty when producing high-level
events [14]. For process mining on IoT data, Bertrand et al. [15] propose a schema for an event log
that caters to traceability concerns as well as different needs in granularity. Klinkmüller et al. [16]
examine the sensitivity of discovery results w.r.t. pipeline operations and parameters to debug process
discovery pipelines, encompassing the discovery procedure itself along with pre-processing steps like
abstraction or filtering. Data and provenance views were proposed to support explorative process
mining by tracking steps, goals, and intermediate results throughout the analysis process [17]. Beyond
process mining, further inspiration may be drawn from research on provenance [18], explanations [19],
debugging of pipelines [20], or probabilistic databases [21].
3. Overview of Research Project
In the course of the PhD project, several facets may be investigated, for example:
• Which data schema or data model should be used depending on the use case or the characteristics
of the data?
• Can we exploit properties of the data to support the process analysis?
We will focus on two specific use cases described below to grasp and better understand these questions
and the arising challenges.
3.1. Realization
3.1.1. Tracing the Effect of Abstractions
To target the traceability of abstractions and their effect on a given analysis, the concept of Event
Knowledge Graphs (EKGs) [8] implemented in graph databases [22] may come in handy, as they
integrate low-level events with high-level abstractions and enable graph-based querying. This capacity
may be extended to record event abstractions, such that the effect of abstractions during exploratory
process analysis can be tracked. To that end, we conceive the following framework: In a forward-manner,
the abstractions represented as queries in a given data preparation pipeline are treated as first-class
citizens of an EKG by recording them along with their relations to lower-level events. Considering an
alternative abstraction in the pipeline, the intermediary results of that alternative pipeline are computed
and recorded correspondingly. Differences in the analysis may be explained by the difference set of
nodes or edges between both possible “worlds” - either by their mere (non-)existence in one set or the
other or by the context they define (i.e., the features that distinguish those nodes or edges). We plan to
apply this idea to a pipeline for task analysis [23], as it involves several steps of abstraction. Interaction
mining [24] may also lend itself to evaluating this idea.
3.1.2. Multi-Dimensional Declarative Process Mining in Relational Databases
Similarly, the rich feature set of relational database systems may be employed to host process mining
tasks. We plan to focus on declarative process mining [25], especially in view of multiple dimensions [26,
27], as data-aware conditions relate to selection and navigating relations correspond to joins in the
relational model. Implementing conformance checking or process discovery for multi-dimensional
declarative process specifications encompasses finding an adequate representation of the event data,
encoding the task as a set of queries, and ideally leveraging database technology like materialized
views [28] to track and reuse intermediary results. Another aspect that could be exploited in case
of declarative process specifications is their apparent similarity to data dependencies in relational
databases. In that case, techniques from the domain of data profiling may be used as a basis for, e.g.,
the discovery of declarative constraints [29]. Beyond that, it might be interesting to investigate which
intermediary data representations like indices [30] or materialized views [28] or other developments
from database systems research like row pattern recognition [31] may be useful to realize process
mining tasks in-database.
3.2. Evaluation
Developments aiming at enhancing the efficiency of process analysis tasks may be evaluated empirically
in a set of experiments on data sets that are established within the process mining community. In
addition to that, synthetic data may serve to investigate the influence of specific data properties on the
interventions that are going to be devised during the PhD project. When it comes to evaluating the
traceability, one can either head into the direction of showing that the developed approach fulfills certain
properties or measure the capacity of the proposed approach to trace deviations in the analysis due to
abstractions. For example, it might be sensible to measure how compact these insights can be represented
if we assume a correlation between the compactness of the representation and understandability.
4. Conclusion
The proposed thesis sets out to integrate pre-processing of the event data with process analysis by means
of database technologies to achieve traceability. We outlined ideas how to approach this problem set,
e.g., through the lens of event knowledge graphs (in terms of database technology used) or declarative
process mining (in terms of process analysis).
References
[1] A. Syamsiyah, S. J. J. Leemans, Process discovery using in-database minimum self distance
abstractions, in: Proceedings of the 35th Annual ACM Symposium on Applied Computing, ACM,
Brno Czech Republic, 2020, pp. 26–35. doi:10.1145/3341105.3373846.
[2] R. Dijkman, J. Gao, A. Syamsiyah, B. van Dongen, P. Grefen, A. ter Hofstede, Enabling efficient
process mining on large data sets: Realizing an in-database process mining operator, Distributed
and Parallel Databases 38 (2020) 227–253. doi:10.1007/s10619-019-07270-1.
[3] W. M. P. Van Der Aalst, Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data
for Process Mining, in: Asia Pacific Business Process Management, volume 159, Springer, 2013, pp.
1–22. doi:10.1007/978-3-319-02922-1_1.
[4] T. Vogelgesang, H.-J. Appelrath, PMCube: A Data-Warehouse-Based Approach for Multidimen-
sional Process Mining, in: Business Process Management Workshops, LNBIP, Springer, 2016, pp.
167–178. doi:10.1007/978-3-319-42887-1_14.
[5] S. Schönig, C. Di Ciccio, F. M. Maggi, J. Mendling, Discovery of Multi-perspective Declarative
Process Models, in: Service-Oriented Computing, volume 9936, Springer, 2016, pp. 87–103.
doi:10.1007/978-3-319-46295-0_6.
[6] S. Schönig, A. Rogge-Solti, C. Cabanillas, S. Jablonski, J. Mendling, Efficient and Customisable
Declarative Process Mining with SQL, in: Advanced Information Systems Engineering, volume
9694, Springer, 2016, pp. 290–305. doi:10.1007/978-3-319-39696-5_18.
[7] F. Riva, D. Benvenuti, F. M. Maggi, A. Marrella, M. Montali, An SQL-Based Declarative Process
Mining Framework for Analyzing Process Data Stored in Relational Databases, in: Business
Process Management Forum, volume 490, Springer Nature Switzerland, 2023, pp. 214–231. doi:10.
1007/978-3-031-41623-1_13.
[8] S. Esser, D. Fahland, Multi-Dimensional Event Data in Graph Databases, Journal on Data Semantics
10 (2021) 109–141. doi:10.1007/s13740-021-00122-1.
[9] E. L. Klijn, F. Mannhardt, D. Fahland, Aggregating Event Knowledge Graphs for Task Analysis,
in: Process Mining Workshops, LNBIP, Springer Nature Switzerland, 2023, pp. 493–505. doi:10.
1007/978-3-031-27815-0_36.
[10] Y. Liu, V. S. Dani, I. Beerepoot, X. Lu, Turning logs into lumber: Preprocessing tasks in process
mining, in: J. D. Smedt, P. Soffer (Eds.), Process Mining Workshops - ICPM 2023 International
Workshops, Rome, Italy, October 23-27, 2023, Revised Selected Papers, volume 503 of LNBIP,
Springer, 2023, pp. 98–109. doi:10.1007/978-3-031-56107-8\_8.
[11] S. J. Van Zelst, F. Mannhardt, M. De Leoni, A. Koschmider, Event abstraction in process min-
ing: Literature review and taxonomy, Granular Computing 6 (2021) 719–736. doi:10.1007/
s41066-020-00226-2.
[12] K. Diba, K. Batoulis, M. Weidlich, M. Weske, Extraction, correlation, and abstraction of event data
for process mining, WIREs Data Mining and Knowledge Discovery 10 (2020) e1346. doi:10.1002/
widm.1346.
[13] B. Bakullari, J. van Thoor, D. Fahland, W. M. P. van der Aalst, The Interplay Between High-Level
Problems and The Process Instances That Give Rise To Them, 2023. arXiv:2309.01571.
[14] B. Fazzinga, S. Flesca, F. Furfaro, E. Masciari, L. Pontieri, Efficiently interpreting traces of low level
events in business process logs, Information Systems 73 (2018) 1–24. doi:10.1016/j.is.2017.
11.001.
[15] Y. Bertrand, S. Veneruso, F. Leotta, M. Mecella, E. Serral, NICE: The Native IoT-Centric Event Log
Model for Process Mining, in: LNBIP, Springer Verlag (Germany), Rome, 2023.
[16] C. Klinkmüller, A. Seeliger, R. Müller, L. Pufahl, I. Weber, A Method for Debugging Process Discov-
ery Pipelines to Analyze the Consistency of Model Properties, in: Business Process Management,
volume 12875, Springer, 2021, pp. 65–84. doi:10.1007/978-3-030-85469-0_7.
[17] F. Zerbato, A. Burattin, H. Völzer, P. N. Becker, E. Boscaini, B. Weber, Supporting Prove-
nance and Data Awareness in Exploratory Process Mining, in: Advanced Information Sys-
tems Engineering, volume 13901, Springer Nature Switzerland, 2023, pp. 454–470. doi:10.1007/
978-3-031-34560-9_27.
[18] B. Glavic, Data Provenance, Foundations and Trends® in Databases 9 (2021) 209–441. doi:10.
1561/1900000068.
[19] B. Glavic, A. Meliou, S. Roy, Trends in Explanations: Understanding and Debugging Data-driven
Systems, Foundations and Trends® in Databases 11 (2021) 226–318. doi:10.1561/1900000074.
[20] R. Lourenço, J. Freire, E. Simon, G. Weber, D. Shasha, BugDoc, The VLDB Journal 32 (2023) 75–101.
doi:10.1007/s00778-022-00733-5.
[21] D. Suciu, Probabilistic databases, in: Encyclopedia of Database Systems, Second Edition, Springer,
2018. doi:10.1007/978-1-4614-8265-9\_275.
[22] Graph Data Management: Fundamental Issues and Recent Developments, Data-Centric Systems
and Applications, Springer, 2018. doi:10.1007/978-3-319-96193-4.
[23] E. L. Klijn, F. Mannhardt, D. Fahland, Multi-perspective concept drift detection: Including the
actor perspective, in: Advanced Information Systems Engineering - 36th International Conference,
CAiSE 2024, Limassol, Cyprus, June 3-7, 2024, Proceedings, volume 14663 of Lecture Notes in
Computer Science, Springer, 2024, pp. 141–157. doi:10.1007/978-3-031-61057-8\_9.
[24] A. Senderovich, A. Rogge-Solti, A. Gal, J. Mendling, A. Mandelbaum, The ROAD from Sensor
Data to Process Instances via Interaction Mining, in: S. Nurcan, P. Soffer, M. Bajec, J. Eder
(Eds.), Advanced Information Systems Engineering, Lecture Notes in Computer Science, Springer
International Publishing, Cham, 2016, pp. 257–273. doi:10.1007/978-3-319-39696-5_16.
[25] C. Di Ciccio, M. Montali, Declarative Process Specifications: Reasoning, Discovery, Monitoring, in:
Process Mining Handbook, Springer, 2022, pp. 108–152. doi:10.1007/978-3-031-08848-3_4.
[26] A. Burattin, F. M. Maggi, A. Sperduti, Conformance checking based on multi-perspective declarative
process models, Expert Systems with Applications 65 (2016) 194–211. doi:10.1016/j.eswa.
2016.08.040.
[27] W. M. P. van der Aalst, G. Li, M. Montali, Object-Centric Behavioral Constraints, 2017.
arXiv:1703.05740.
[28] R. Shirkova, J. Yang, Materialized Views, Foundations and Trends® in Databases 4 (2011) 295–405.
doi:10.1561/1900000020.
[29] X. Chu, I. F. Ilyas, P. Papotti, Discovering denial constraints, Proceedings of the VLDB Endowment
6 (2013) 1498–1509. doi:10.14778/2536258.2536262.
[30] T. Kraska, A. Beutel, E. H. Chi, J. Dean, N. Polyzotis, The Case for Learned Index Structures, in:
Proceedings of the 2018 International Conference on Management of Data, ACM, Houston TX
USA, 2018, pp. 489–504. doi:10.1145/3183713.3196909.
[31] D. Petković, Specification of Row Pattern Recognition in the SQL Standard and its Implementations,
Datenbank-Spektrum 22 (2022) 163–174. doi:10.1007/s13222-022-00404-3.