A Case Modelling Language for Process Variant Management in Case-based Reasoning

Andreas Martin - FHNW
A Case Modelling Language for
Process Variant Management in
Case-based Reasoning
Riccardo Cognini 2, Knut Hinkelmann 1 and Andreas Martin 1
AdaptiveCM 2015 – 31.08.2015
4th International Workshop on Adaptive Case Management and
other non-workflow approaches to BPM
1 FHNW University of Applied Sciences and Arts Northwestern Switzerland,
School of Business, Olten, Switzerland
2 University of Camerino, School of Science and Technology,
Computer Science Department, Camerino, Italy
A Case Modelling Language for Process Variant Management in Case-based
Reasoning
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Introduction
Problem / Research objective
Reasoning
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Problem
Knowledge work is not routine work …
 “[...] the sequence of actions depends so much upon the
specifics of the situation [...] necessitating that part of doing
the work is to make the plan itself" (Swenson, Palmer and Silver, 2011, p.8).
 Difficult to predict upcoming tasks.
 Hard to determine the type and scope of tasks.
 Sequence of tasks may vary due to already achieved results
and unforeseeable events.
Reasoning
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Problem
 Situation: As the term knowledge work implies, knowledge
worker need experience and (procedural) knowledge to find
a solution (approach, plan, activities) to specific situation /
problem.
 Underlying assumption: For knowledge work it seems useful
to take approaches that structure business processes just in
part as process fragments.
 “Process fragments are reflecting the partial and
intermittent knowledge one modeller [or a knowledge
worker] has at a certain time about a specific situation" (Eberle,
Unger and Leymann, 2009, p.399).
Reasoning
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Research objective
 Knowledge workers are required to make decisions based on
process fragments, which can only be made by the
knowledge workers themselves at run-time.
 Research objective: Therefore, a vocabulary and content
representation (modelling language) is needed that
support the manual planning, modelling, generation and
refinement of process fragments during run-time.
 Underlying method: We use case-based reasoning (CBR), to
put the experience management into the hands of the
knowledge worker and provide a way to retrieve and reuse
historic process fragments.
Reasoning
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What is Case-based Reasoning (CBR)?
 CBR can be seen as “reasoning by remembering”…
 and it is a technically independent methodology to humans
and information systems.
 “Case-based reasoning is both […] the ways people use cases
to solve problems and the ways we can make machines use
them”.
 CBR has been applied in business process contexts, e.g., for
workflow retrieval, adaptation, construction and monitoring.
Reasoning
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What is Case-based Reasoning (CBR)?
CBR- CYCLE (& CASE- BASE)
 Retrieve the most similar cases from the
case-base containing previous cases,
based on the problem description of the
new case using a similarity mechanism.
 Reuse the knowledge in the retrieved
case(s) in order to solve the current
problem – adapt the historical
knowledge to the new problem
(adaptation).
 Revise and test the suggested solution
e.g. by evaluating it under the real world
situation (evaluation).
 Retain useful experience for future reuse
and store a new case in the knowledge
base (case learning).
Reasoning
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Based on: A. Aamodt and E. Plaza, “Case-Based Reasoning : Foundational Issues , Methodological Variations , and System Approaches,”
Artificial Intelligence Communications, vol. 7, no. 1, pp. 39–59, 1994.

Application Scenario
Reasoning
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Master Study Admission Process
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(Sub-) Tasks based working
Used for data collection
 The end user are familiar with
an task / task pattern concept.
 The data used in this approach
was initially gathered in an EU
project called MATURE:
Reasoning
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CASE
CASE
Demanding*) example
1. In the first case the problem is solved by calling the university of the
applicant student asking for some information.
2. In the second case the university is called and then proof of the
existence of the university.
3. In the last case just the proof of the of existence of the university is
requested.
*) These three examples are not the demanding “thing”, it is the fact that an uncountable
and unknown variation of these process fragments can exist.
Reasoning
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CASE

Application Scenario Analysis
 Application Scenario Analysis: Knowledge workers (as shown
in application scenario) currently working with concrete
process fragments / (sub-) tasks.
 As improvement: Case generalization and abstraction can
reduce the complexity of the cases, increase the flexibility
and reduce the size of the case base to enhance the retrieval
efficiency.
 Therefore we introduce a case-based process fragment
modelling language that supports the manual generation
and refinement of generalized cases in CBR based
approach.
Reasoning
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Approach Comparison
Reasoning
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Drawbacks of: BPMN and other Imperative Languages
End users …
 do not have enough time,
knowledge and skills to model
or update a BPMN model.
 are able to specify which
activities should be performed
and by whom,
 but they are not able to
establish a temporal order,
 because they are focused
just on their own tasks.
Imperative languages …
 are designed to express
something that is fully defined
including all the possible
aspects.
 “is like modifying” a software
source code written by
someone else.
Reasoning
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CMMN, a declarative language
 is a designed to model non
predefined, partially
structured and non repeatable
business processes.
 Mandatory and optional
activities can be modelled
without
 specifying an execution
order or
 the situation in which an
activity can be executed.
Issue of CMMN …
 it is not possible to specify
complex execution criteria.
 E.g. it is not possible to
specify at least one of the
activities in a set has to be
executed.
 complex data elements are
not provided;
 it implies that just little
information about the
type of data or document
is available.
Reasoning
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Comparison of Modelling Languages
 We propose the use of BPFM notation as a language for case
representation in CBR. BPFM:
 permits defining the BP activities that must or can be performed with
and without including an execution order
 considers complex constraints and different types of data objects.
 is a configurable process model since it can encapsulate more than one
BP variant
Reasoning
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The Approach…
…Business Process Feature Model Notation
Reasoning
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Business Process Feature Model (BPFM)
 The Business Process Feature
Model is constituted by a tree of
related activities.
 The root identifies the main
services.
 Each internal (non-leaf) activity
denotes a sub-process that can
be further refined.
 The external (leaf) activity
represents an atomic task.
 BPFM allows using the same
meaning and graphical
representation given by BPMN 2.0.
 A BPFM model allows for the
defining constraints between
activities.
Reasoning
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Mapping BPFM Activities to BPMN
Reasoning
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Constraints in Business Process Feature Model (BPFM)
Constraints are used to express
 if child activities can or have
to be selected in the
configuration to be included
in the BP variant, and
 if they can or have to be
included in each execution
path of the BP variant.
Reasoning
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Mandatory Constraints
Reasoning
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 Mandatory Constraint requires that the connected child
activity must be inserted in each BP variant,
 and it must be included in each execution path.

Special Case Constraints
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 A Special Case Constraint allows for the connected child activity
to be inserted in each BP variant.
 When it is inserted it must be included in each execution path.

Inclusive Constraints
Reasoning
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 An Inclusive Constraint requires that at least one of the connected child
activities be inserted in each BP variant,
 and at least one of them must be included in each execution path.

One Selection Constraints
Reasoning
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 A One Selection Constraint requires that exactly one of the
connected child activities be inserted in each BP variant,
 and it must be included in each execution path.

Data Objects in BPFM
Reasoning
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 BPFM manages all types of BPMN 2.0 data objects, including
data object states, with the same modelling notation

Data Mapping to BPMN
Reasoning
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User Acceptance?
Reasoning
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User Acceptance?
Reasoning
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User Acceptance?
Reasoning
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The Approach…
…BPFM in Case-based Reasoning (CBR)
Reasoning
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Back to Case-based Reasoning (CBR)
THE CASE
 Traditional CBR terminology: a case consists
of a problem space that is used for
describing a certain solution space.
 Our CBR terminology: a case consists of a
case characterization (derived from
Bergmann) that is used for describing a
certain case content.
Reasoning
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Characterization
Content
CASE

Retrieval
Description
Case-based Reasoning Cycle - Retrieval
Reasoning
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Retrieve
CASE BASE
NEW Situation
How is: the
configuration
(model) and
the retrieval
realized ?

 ArchiMEO is an enterprise ontology
based on ArchiMate and is extended
with selected concepts from other
enterprise ontologies.
 ArchiMEO is implemented using
RDF(s) and OWL.
 ArchiMEO has been developed by
several team members of the FHNW
Information and Knowledge
Management Research Group (IKM).
 ArchiMEO is licensed under a
Creative Commons Attribution-
ShareAlike 3.0 Unported License.
 ArchiMEO is available for download
as TTL- files (Terse RDF Triple
Language) under:
ikm-group.ch/archimeo
Reasoning
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ArchiMate is a technical standard from The Open Group.
RDF(S) / OWL is a W3C standard.
The enterprise ontology ArchiMEO is based on ArchiMate.

Ontology-based Case-based Reasoning (OBCBR)
Reasoning
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Case Characterization describing Process Knowledge
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Further elements:
 TaskObjective: The task objective element describes the goal of the task itself. This is
similar to the name and/or description of an BPMN activity.
 TaskRole: The task role element is used to describe the role of the involved person of
the task. Through the inclusion of an enterprise or domain ontology, it is possible to
reuse an existing enterprise specific role / organizational model.
 TaskUser: The task user elements is used to indicate the person who described the
case.

Demonstration
Reasoning
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Learning
Retrieval
Adaptation
Description
Extended Case-based Reasoning Cycle
Reasoning
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Reuse
Retrieve
CASE BASE
RetainRevise /
Generalize
Revise
NEW Situation
Evaluation

Concrete CASEs
Reasoning
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Characterization
Content
CASE A
Characterization
Content
CASE B

Generalized CASE
Reasoning
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Characterization
Content
Generalized CASE

Implementation
Reasoning
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 ICEBERG Toolkit
 BPFM Modelling
Platform

Interlinked Case-based Reasoning (ICEBERG)
Reasoning
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 ICEBERG Approach & Toolkit: Using interlinked (ontology-
based) case-based reasoning to bring hidden knowledge to
the surface.

ICEBERG - System architecture
 Technology:
 Apache Jena: open source
Java framework for building
Semantic Web applications
 OpenDolphin: open-source
library for a lightweight
remote model-view-
controller separation.
 JavaFX: GUI framework
 TopBraid Composer: an
ontology engineering
software (paid)
Reasoning
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Adapting case models / content
BPFM Modelling Platform an OMiLAB Project
Reasoning
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Conclusion and Future Work
Reasoning
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Conclusion and Future Work
 This paper presented an approach to model cases in
knowledge-intensive BPs.
 The approach merges CBR with BPFM notation in order to
represent cases.
 We applied the approach to a concrete case in a public
administration scenario in order to show its suitability.
Future Work: We plan to …
 … make further evaluation with the respect to usability of
different modelling languages / elements.
 … transfer the approach to a different scenario.
Reasoning
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A Case Modelling Language for Process Variant Management in Case-based Reasoning

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