=Paper=
{{Paper
|id=Vol-2673/paperDC1
|storemode=property
|title=Ontology-based Business Process Chaining in Heterogeneous Systems
|pdfUrl=https://ceur-ws.org/Vol-2673/paperDC1.pdf
|volume=Vol-2673
|authors=Anbumunee Ponniah
|dblpUrl=https://dblp.org/rec/conf/bpm/Ponniah20
}}
==Ontology-based Business Process Chaining in Heterogeneous Systems==
https://ceur-ws.org/Vol-2673/paperDC1.pdf
Ontology-based Business Process Chaining in
Heterogeneous Systems
Anbumunee Ponniah
BITS Pilani, K K Birla Goa Campus, India
p20170418@goa.bits-pilani.ac.in
Abstract. Large companies deal with complex business processes that
span across multiple applications. Process Mining (PM) across the ap-
plications is not straight forward due to differing content, format, and
context of the event logs. Ontology-based techniques can capture im-
portant metadata about applications and processes. The proposed work
aims to use the relationship between metadata to identify processes that
run across applications. We intend to apply the domain context for ex-
tending similarity measures within and across ontologies.
Keywords: Business Knowledge Representation · Domain-Specific On-
tology · Event Logs · Knowledge Graph · Natural Language Processing
· Ontology Similarity Measures · Process Mining
1 Research problem
Large companies manage many complex business processes that work together
to achieve business goals [1]. The advent of IT enables business processes to
implement in computers across multiple purpose-built applications [2]. Due to
the evolving nature of the business, applications developed at different times
by multiple teams follow diverse sets of standards and formats [3]. Standardis-
ing the event logs [4] requires expensive and disruptive coding and maintenance
effort. This makes the discovery, analysis, and optimisation of such business pro-
cess a technically challenging problem to solve [5]. Temporal, structural, and
context-based techniques are available to analyse event logs to solve ordering,
correlation, and scoping entries of the logs [6][7][8]. However, for large processes
that run across heterogeneous applications, current techniques can only discover
components in each application. There is a need for a Process Mining technique
and a toolkit that factors in the number and variety of applications to discover
and optimise complex business processes. We performed a literature survey us-
ing keyword-based search to extract candidate literature which was filtered for
domain-specific application and manually grouped into general process mining,
knowledge representation techniques, and intelligent decision systems. Listed be-
low are the gaps identified by us with those addressed by our research project
highlighted in boldface font.
1. Use of noise and outliers for context and detecting rarely occurring processes.
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 Anbumunee Ponniah
2. Modeling process drifts and overlaps of dynamically evolving processes.
3. Mining from diverse logs that lack structural or transitional rela-
tionships
4. Automating ontology enrichment, validation and downstream rule updates.
5. Using domain context, Key Performance Indicators and cross-
ontology relations for PM and ontology pruning
6. Extending distance metrics of ontologies to include domain and
implementation-specific factors
2 Approach
Fig. 1. Overall Research Design Illustrating the Sequence of Activities and Flow of
Research.
Ontology-based techniques use metadata in the form of domain concepts or
process descriptions [9][10] when identifying and sequencing tasks. Our research
is primarily a software application problem that uses semantic web and ontology
techniques to help process mining for complex processes. We propose to merge
event-log based PM techniques with ontology creation and distance metrics-
based concept association techniques to discover processes across applications.
We further propose expanding the syntactic, structural and semantic similarity
measurement techniques [11] to establish relationships between concepts within
and across ontologies.
Figure 1 captures the flow of our research. Our research involves the analysis
of event logs from each application separately using available PM techniques. We
intend to create granular ontologies for each application combining pieces of dis-
covered processes, domain concepts, application-specific concepts, and heuristics
about the process. We plan to apply similarity measures to identify relationships
Ontology-based Business Process Chaining in Heterogeneous Systems 3
that span across the ontologies and derive process associations across heteroge-
neous logs.
3 Methodology
The proposed research will adapt Design Science Research (DSR) [12] for apply-
ing ontology-based techniques to mine complex processes. Per the ED Process
[13], we propose to follow an iterative approach consisting of design, prototype,
and evaluation for making our research available in a toolkit. We will use the
mixed-methods research methodology for data collection from event logs (quan-
titative) and heuristic information from domain experts (qualitative). We will
further use PM specific data preprocessing techniques [3] to cleanse, remove
noise, and fix errors in data such as incomplete traces, missing events, mashed
processes and many more.
4 Intended solution
Fig. 2. A High-Level Framework Diagram for Overall Solution. Illustrating the Com-
ponent Solution Architecture of the Research.
Figure 2 presents our solution as a combination of PM and Ontology tech-
niques. The research project will discover processes from heterogeneous event
logs in the chosen domain. We plan to use ProM as the PM tool due to its wide
selection of discovery algorithms [14]. We will create ontologies to represent the
domain context of the applications. We have shortlisted Protege as the tool for
4 Anbumunee Ponniah
ontology creation and analysis, due to its domain-friendly support for creating
knowledge models [15].
Ontology-based technique have proven useful in PM [16][17]. Similarity mea-
sures is a common technique to find the relationship between concepts across
ontologies [18]. We propose to devise PM specific distance and similarity mea-
sures to establish the relationship between the concepts within and across on-
tologies. We further plan to use a combination of concept-level and ontology-level
relationships to correlate processes in heterogeneous applications.
5 Contributions to BPM research
The issue of PM across heterogeneous applications is not fully explored, which
confirms our identified research gap. Our research focuses on arriving at similar-
ity measures that work across multiple domain-specific ontologies. We propose
to use those measures to discover and analyse complex processes that flow across
heterogeneous applications. The techniques developed will also aid organisations
[19] in change management, predicting disruptions and achieving better optimi-
sation across their business. We plan to create a toolkit and sample implemen-
tations that would help future researchers in creating multiple domain-specific
ontologies.
6 Project status and challenges
Current state of the research project is presented in Table 1.
Table 1. Illustrating the Current State of the Project and Next Phases along with
their Tasks.
Phase Task Status
Literature survey Survey across PM, ontology techniques and intel- Completed
and Analysis ligent decision systems.
Data Preparation Identify, analyse and preprocess datasets. In Progress
Process Mining Baseline using standard PM techniques and tools Pending
Ontology Creation Creating and refining domain-specific ontologies Pending
with process-specific concepts
Establishing Simi- Exploring existing similarity measures and extend- Pending
larity Measures ing them to use PM and domain-specific features
Evaluation Evaluating usefulness of ontologies and similarity Pending
measures using available techniques
Toolkit Develop- Design and Implement Toolkit Pending
ment
Our work of applying ontology and distance measures to discover processes
can help detect process drifts. It can also aid organisations in identifying overlaps
and hidden relationships of processes across various applications. By adding KPI
information, techniques developed in our research can be useful in automating
downstream rule updates.
Ontology-based Business Process Chaining in Heterogeneous Systems 5
References
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6.1 Glossary of Terms
1. Business Process: A collection of tasks or activities performed by people
or equipment to achieve a concrete goal in an organisation.
2. Business Process Analysis: Methodology to understand the health of
different operations in a business.
3. Business Process Chaining: Establishing the relationship between parts
of processes across system and application boundaries.
4. Domain-Specific Ontology: An ontology or series of ontologies that rep-
resent a set of concepts and relationships specific to the domain.
5. Event Logs: Digitized data about events executed by IT applications.
6. Heterogeneous Applications: Information Technology (IT) applications
implemented using different technologies, following different standards and
formats for data and logic.
7. Intelligent Decision System (DSS): A class of computerised information
system that supports business and organisational decision-making activities
through rules and learning algorithms.
8. Intelligent Process Automation: An emerging set of new technologies
that combines fundamental process redesign with robotic process automa-
tion and machine learning. It is a suite of business-process improvements
and next-generation tools that assists the knowledge worker by removing
repetitive, replicable, and routine tasks.
9. Ontology: Ontology is the formal representative of concepts and their re-
lationships. This enables computers and humans to interpret semantic rela-
tionships among the concepts and infers implicit knowledge [20].
10. Process Mining (PM): Approach and techniques to discover, monitor and
improve real processes (i.e., not assumed processes) by extracting knowledge
from event logs readily available in today’s (information) systems. PM in-
cludes (automated) process discovery (i.e., extracting process models from an
event log), conformance checking (i.e., monitoring deviations by comparing
model and log), social network/organisational mining, automated construc-
tion of simulation models, model extension, model repair, case prediction,
and history-based recommendations [19].
Ontology-based Business Process Chaining in Heterogeneous Systems 7
11. eXtensible Event Stream (XES): An IEEE specification for a tag-based
language to capture event logs and event streams. Approved in Nov 2016
[4], the specification achieves interoperability in event logs to enable easier
process discovery and analysis.