=Paper=
{{Paper
|id=Vol-3299/Paper05
|storemode=property
|title=Transformer for Predictive and Prescriptive Process Monitoring in IT Service Management (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3299/Paper05.pdf
|volume=Vol-3299
|authors=Marc C. Hennig
|dblpUrl=https://dblp.org/rec/conf/icpm/Hennig22
}}
==Transformer for Predictive and Prescriptive Process Monitoring in IT Service Management (Extended Abstract)==
https://ceur-ws.org/Vol-3299/Paper05.pdf
Transformer for Predictive and Prescriptive Process Monitoring
in IT Service Management (Extended Abstract)
Marc C. Hennig 1
1
University of Applied Sciences Munich, Lothstr. 34, Munich, 80335, Germany
Abstract
Increasingly complex IT environments, requirements, and organizations in IT service
management make the development of advanced predictive technologies necessary. Therefore,
this paper outlines a Ph.D. project to develop a pipeline supporting novel IT service
management approaches using state-of-the-art predictive and prescriptive process monitoring
based on transformer neural networks.
Keywords1
IT Service Management, ITSM, AITSM, AIOps, Transformer
1. Introduction
IT service management (ITSM) [1] is the processual approach to providing information technology
support and services assisting the key activities in organizations and therefore serving the overall
organizational goal achievement. ITSM processes define a continuous improvement process framework
[1] and operating model for IT organizations by determining services based on customer requirements
that are provided by IT infrastructure components. Legal and economic dimensions are added to the
services as contractual obligations in service level agreements (SLA). Adherence to SLAs and continual
improvement are among the core success factors in ITSM [2]. ITSM processes are complex and difficult
to analyze since they are usually embedded in a multi-layered technical and organizational landscape
with a high level of specialization [3], [4].
Two emerging fields are assisting in the successful provision of IT services: artificial intelligence-
driven ITSM (AITSM) [5] and artificial intelligence for IT operations (AIOps) [6]. AIOps is a data-
driven approach for analyzing data to provide operators with the information required to operate
complex IT systems efficiently. At the same time, AITSM is the automation, support, and improvement
of ITSM processes using machine learning (ML). Insights into processes are important requirements in
both approaches.
2. Motivation and Problem
As a collection of interconnected processes embedded in a multi-faceted environment, ITSM is
challenged by different stakeholders and demands that must be aligned. Therefore, process instances in
ITSM are often characterized by a high degree of flexibility, dependencies, and knowledge intensity,
which are necessary to react to disruptions and changes in services and the socio-technological IT
organization. The organization comprises so-called configuration items (CI), which are mainly human
resources with their responsibilities and hard- and software components. The configuration
management database (CMDB) is one of the core elements in an ITSM ecosystem and contains
information regarding CIs and their interdependencies and hence offers valuable contextual
information. In ITSM, especially the assessment and improvement of the processes and their proper
orchestration are central pain points since extracting practical insights on the processes are difficult to
obtain which hinders process maturity and hence the service resilience and quality [4], [5]. CMDBs
ICPM 2022 Doctoral Consortium and Tool Demonstration Track, October 23–28, 2022, Bolzano, Italy
EMAIL: mhennig@hm.edu
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
22
have been used to analyze some parts of ITSM processes [7]–[9] but have not yet been systematically
utilized in combination with event log data.
Due to the unique properties of ITSM processes as complex service processes, this Ph.D. project
shall enable transformer neural networks [10] in ITSM and establish a connection between predictive
and prescriptive process monitoring and the intersecting fields of AIOps and AITSM. This is
particularly interesting since the results which are usually delivered by process monitoring
solutions [11], including the next event, its point of occurrence, and the overall duration of a process
instance are valuable insights for these fields to provide operational support. Furthermore, concrete
recommendations and interactive optimizations on these variables can be derived using prescriptive
process monitoring [12] to enable improvements in ongoing instances. Despite the thematic overlaps
between predictive and prescriptive process monitoring, AIOps, and AITSM, systematic studies on the
intersection are missing.
Predictive and prescriptive process monitoring could benefit from transformer models, a relatively
novel approach in ML mainly used in natural language processing (NLP) and other sequence-related
tasks. They have outperformed traditional recurrent neural networks and their derivatives in several
areas. However, in predictive and prescriptive process monitoring, transformers have only been covered
sparsely so far [12]–[14].
Hence, this project aims to understand the fields AITSM and AIOps as novel approaches in ITSM
and to integrate predictive and prescriptive process monitoring based on transformer models therein.
3. Research Questions
The success factors for ITSM, as outlined in the previous sections, namely the ability to conform to
predefined time and quality-bound SLAs, are directly influenced by process intelligence and the ability
to derive actionable insights.
To address these challenges, an end-to-end pipeline for process monitoring using transformer
models shall be envisioned, which initiates three research questions. First, the data must be collected
and preprocessed; second, the event log must be fed into the transformer model to receive predictions;
finally, workable insights should be derived. The insights should benefit the service quality by
providing operators with immediate AIOps information to handle events and long-term AITSM support
to foster change, resilience, and improvement across the service’s life cycle.
Initially, there is the collection and preprocessing of the data, including the extraction and
preparation of information from concurrent process instances, like incident management and change
management, and other ITSM sources, e.g., the graph-like CMDBs. The inclusion of exogenous data
[15], [16] in addition to the event log is deemed necessary to fully capture how an IT organization’s
performance is affected by the workload and events occurring in different processes and process
instances and to identify the key influence factors of service resilience and performance. Additionally,
this might be required to attain a sufficient model quality [17]. Therefore, it must be figured out how
the data can be optimally prepared for predictive tasks in transformer models and how event data and
contextual information from other sources like CMDBs can be integrated.
Research Question 1: What is the proper way to collect and preprocess the event log to account for
the complexities of ITSM processes and leverage additional data sources like CMDBs to allow for
further processing in transformer models?
Secondly, an architecture using transformer models is to be developed based on the previous
research [13], [14]. This architecture should accommodate the event logs’ unique sequential properties
and underlying processes. Specifically, the non-continuous time and the non-equidistant time intervals
between the discrete events make processes different from usual timelines and pose an interesting
challenge for transformers. Other approaches than the often-used positional embedding and a special
trace encoding [18] might be necessary to make the dependencies between activities and timestamps
workable for transformer models [19].
23
Research Question 2: How can a transformer architecture be designed to be suitable for ITSM event
logs and additional data sources to provide predictions and optimizations with continuously generated
events from real-world applications?
Finally, the core factors influencing the performance of process instances in ITSM must be detected
based on the data, hence adding explanations to the mostly black box [20] results of transformer models.
Explainability is essential to enable proactive improvement of the process, the organization, and the
analysis of problem sources [21], which has not yet been achieved in prescriptive process
monitoring [12].
Research Question 3: How can the root causes impacting the performance within process instances
be derived from complementing the predictions and be used for continuous improvement and
operational support?
The results of these steps shall then be combined into a pipeline for process monitoring on ITSM
event logs that can be used to support IT operations as an AITSM and AIOps solution.
4. Research Methodology
This Ph.D. project will follow the design science research process [22]. The different research
questions will be worked on iteratively to individually create tangible artifacts and assess their impact
on the predictive performance of transformers. The development of artifacts starts with the
preprocessing, progresses to the model that provides descriptive predictions, and finalizes with the
extraction of explanatory insights.
First, the exact challenges of each research question will be identified in detail to infer tangible
objectives, which serve as the base for the requirement definition and tracking of goal attainment. These
objectives are then synthesized into a system design employing literature research from online databases
to extract the latest field findings. During the literature analysis, recent and relevant conference papers
will be preferred, followed by other publications and preprints. This literature research will draw special
attention to other ML domains, like NLP, to see whether knowledge from these areas can be leveraged
in this use case.
The theoretical views on the problem and system design are subsequently used to develop the
artifacts and establish a practical understanding of the narrowed problems defined by the research
questions and their solutions. The artifacts will then be demonstrated on real-world data to prove their
applicability and usefulness to the problem domain.
To conclude the projected approach, the artifacts are tested using evaluation strategies appropriate
for the artifacts [23]. The evaluation includes the use of quality measures for ML tasks to ensure that
the training and results are valid. In benchmarks against other published and available ML models in
predictive and prescriptive process monitoring, it will be verified whether improvements could be
reached. The evaluation shall be done on different event logs to ensure the applicability and
transferability of the models. Finally, the artifact is evaluated from a functional perspective by
comparing the defined aims and requirements with the created artifact to ensure the goals are reached.
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