=Paper=
{{Paper
|id=Vol-3327/paper08
|storemode=property
|title=How to (re)design declarative process notations? A view from the lens of cognitive effectiveness frameworks
|pdfUrl=https://ceur-ws.org/Vol-3327/paper08.pdf
|volume=Vol-3327
|authors=Hugo A. Lopez,Vít Dexter Simon
}}
==How to (re)design declarative process notations? A view from the lens of cognitive effectiveness frameworks==
https://ceur-ws.org/Vol-3327/paper08.pdf
How to (re)design declarative process notations? A
view from the lens of cognitive effectiveness
frameworks
Hugo A. López1,* , Vít Dexter Simon2
1
Technical University of Denmark, DTU Compute. Richard Petersens Plads, 321, 2800 Kgs. Lyngby, Denmark
1
University of Copenhagen, Computer Science Department. Universitetsparken 1, 2100 Copenhagen, Denmark
Abstract
Declarative process modelling notations are a family of approaches where events obey a set of constraints
rather than specific flows. While declarative models can express in a few constructs a large set of process
behaviours, there is little adoption of declarative approaches compared to their imperative counterparts.
One possible reason is that while expressive, there has not been enough focus on the user-centred design
of declarative notations. In this paper, we explore how cognitive effectiveness frameworks could improve
the development of declarative notations that support novice and expert users. In this paper, we analyse
one representative declarative notation (DCR graphs) against two cognitive effectiveness frameworks.
Our analysis suggests thirteen areas of improvement. For notation developers, we provide theory-backed
guidelines, while for researchers in process models we outline hypotheses in the understandability of
process models for further empirical testing.
Keywords
Declarative Process Models, DCR graphs, Cognitive Effectiveness, Human-Computer Interaction, Physics
of Notations
1. Introduction
Process models are a fundamental piece in the understanding, analysis, optimisation and imple-
mentation of business processes, providing a common understanding for business and technical
users alike. While a plethora of notations supporting process models exists, it is commonly
accepted that most notations fall between the imperative and the declarative modelling spec-
trum [1]. Imperative notations such as BPMN facilitate the description of sequential process
flows, whereas declarative specifications like DECLARE [2], CMMN [3], or DCR graphs [4]
describe cause-effect relations between events. While semantic aspects of declarative processes
have been amply studied, there has been relatively less research on how to engineer declarative
notations such that they support model understanding, and how to equip notations with features
that allow for understanding when the complexity of models increases.
PoEM 2022 Forum, 15th IFIP Working Conference on the Practice of Enterprise Modeling 2022 (PoEM-Forum 2022),
November 23-25, 2022, London, UK
*
Corresponding author.
$ hulo@dtu.dk (H. A. López); pfs108@alumni.ku.dk (V. D. Simon)
http://lopezacosta.net/ (H. A. López)
0000-0001-5162-7936 (H. A. López)
© 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
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Figure 1: DCR example: the meeting booking process [6]
This paper studies the cognitive effectiveness of a declarative process modelling notation,
the Dynamic Condition Response (DCR) graphs. DCR is a graph-based notation where events
are constrained by behavioural constraints (similar to other formal notations, for example,
DECLARE). The choice of notation is not incidental: compared to DECLARE, DCR is actively
used by modellers in industry, and it is integrated into KMD Workzone, a case management
solution provided to central government institutions in Denmark [5], Australia and Japan.
Figure 1 introduces a simple DCR model. Intuitively, this process describes the interactions
between different organisations to book a meeting. Events (rounded squares) denote what can
happen in the process. Relations (arrows) impose constraints on when events can be executed.
For instance, the condition relation (i.e., →∙) says that the organization A cannot propose
dates unless (a user) has executed create case. With a response relation (i.e., ∙→) we impose
an obligation: once (organization A) has proposed dates, then (organization B) must accept
them. Events can be placed in or out of the context: an event out of context (denoted with a
dashed border) can be included using the inclusion relation (i.e., →+) and removed again using
an exclusion relation (i.e., →%). Event collections (i.e., arrange meeting) simplify the visual
notation by collapsing into one the multiple relations that enclosed events have to events from
the outside. As with many other software notations, the notation has evolved over time to
cover new business requirements. This has made the formalism and accompanying notation
change and grow from its original presentation in 2011 [4]. We use its most recent iteration
including all the extensions, with a total of 32 distinct visual elements. Also, industrial DCR
models are orders of magnitude larger than Figure 1, with models that can reach the hundreds
of constraints, events and collections1 . While research has focused on building a language that
is expressive and robust, how to represent concepts and control visual complexity has received
far less attention, and its official language documentation2 does not provide empirical evidence
regarding the design rationale, nor about how the design best fits the potential users and the
1
See, for example, the adoption consideration section in [7].
2
https://documentation.dcr.design/
modelling tasks at hand.
This paper presents an initial investigation of the factors affecting the understandability of
DCR graphs. In a purely empirical cycle, we focus on the observation stage, with the goal of
building a set of hypotheses that can be further tested via user studies. We conduct a systematic
analysis using two of the most widespread cognitive effectiveness frameworks: the Physics
of Notations (PoN) [8] and SEQUAL [9]. They provide a set of general, notation-agnostic
guidelines on how to maximise the perceptual properties of graphical notations and modelling
tools that are backed by empirical evidence. Thus, the result of this paper is 1) a critical analysis
of the current status of DCR according to cognitive effectiveness frameworks, and 2) a set
of guidelines for improvement of the notation. The chosen frameworks provide us with an
unbiased yardstick that can be used to compare against other notations and complements earlier
work on tailor-made DCR quality frameworks from the perspective of experts [10].
The paper is structured as follows. Preliminaries for DCR, PoN and SEQUAL are introduced
in Section 2. The analysis of DCR notation against PoN is presented in Section 3. The analysis
using the SEQUAL framework is presented in Section 4. Suggestions for improvement of the
notation are presented in Section 5. Related work is presented in Section 6. The paper concludes
in Section 7.
2. Preliminaries
2.1. DCR graphs and its extensions
DCR graphs [4] is a modelling notation where processes are collections of events and constraints.
We introduce DCR via a process model in execution (see Figure 2), using labels 𝐴, 𝐵, 𝐶, . . . to
refer to specific events. We refer to [4] for the formal definition. Figure 4 in Appendix shows
the metamodel of DCR and its extensions.
A DCR graph is a graph containing a set of events and event collections, a set of relations, and
a marking. Graphs contain static and dynamic components. Starting with the static components,
events can be atomic (e.g., 𝐴) or represent event collections (e.g., 𝐽). They can have 0, 1 or
multiple roles assigned to them (e.g., 𝐴, 𝑇, 𝑆). An event may have a data payload (e.g., 𝑂) [11],
execute computations (e.g., 𝐺), or determine their value via DMN tables (e.g., 𝐼) [12]. Finally,
events can be linked to other DCR graphs using networks (e.g., 𝑅) [13]. Relations constraint
events. DCR has ten types of relations. First, precedence relations such as condition (e.g., relation
𝐴→∙𝐵), milestone (e.g., relation 𝑆→◇𝐶), and precondition (e.g., relation 𝐵→♦𝐶), allow the
execution of the consequent once the precedent has been executed. Second, obligations such as
response (e.g., relation 𝑆∙→𝐵) and no-response (e.g., relation 𝐺∙→x𝐶) modify the pending state
of the consequent depending on the execution of the antecedent. Finally, alternative relations
such as include (e.g., relation 𝑇 →+𝐵), exclude (e.g., relation 𝐾→%𝐿) and logical include (e.g.,
relation 𝐺−→±𝐸) modify the inclusion state of each event depending on the execution of the
antecedent. Relations can be applied to the same element (e.g., relation 𝐵→%𝐵), or multiple
relations can link a group of events (e.g., 𝐵→∙𝐶 and 𝐵∙→𝐶). In addition, relations can have
𝐴>10
guarded expressions (e.g., relation 𝐺−−−→±𝐸), and conditions and responses may define how
𝑃 𝑇 1𝑆
long an event will be delayed (e.g., relation 𝐶 −−−→∙𝐷) or time boundaries when an event
𝑃 𝑇 5𝑆
should be executed (e.g., relation 𝐷∙−−−→𝐻) [14].
The dynamic components include the execution state. Each event/collection has a marking.
An event can be included (events using solid borders, e.g., 𝐴), excluded (events with dashed
borders, e.g., 𝐵), pending (events with “!", e.g., 𝐶), executed (events with ✓, e.g., 𝐷) or be in a
combination of states (e.g., 𝐹 ). It is the marking that defines whether an event can be executed
or not: An arbitrary event is enabled if i) it is included, and ii) all the events with precedence
relations towards the event have either been executed or are excluded. Also, part of the dynamic
aspects involves access control (e.g., 𝐻), which binds the role of an activity according to the
evaluation of an expression. Event collections include a stateless nesting construction (e.g.,
𝐽) that enables modellers to use a single constraint to bind multiple events [6], a subprocess
construction (e.g., 𝑀 ) that has the same marking as a regular event but whose state depends on
the marking of its enclosing events and a multi-instance subprocess (e.g., 𝑈 ) that creates copies
of each of the enclosed events and relations at runtime [15], bound to normal events via the
spawn relation. Finally, multiple events can be grouped into one data form (e.g., 𝑄) [11].
2.2. The Physics of Notations (PoN)
The Physics of Notations [8] is a scientific framework for the analysis of cognitive effectiveness
in visual notations. It considers nine dimensions:
2.2.1. Semiotic Clarity
studies how symbols and concepts relate. We consider this principle satisfied only if symbols
and concepts map 1:1. Misalignments can be categorised as: symbol redundancy: one concept is
represented by multiple symbols, symbol overload: multiple concepts are visualised by the same
symbol, symbol excess: at least one symbol has no semantics, and symbol deficit: no symbol
matches one or more concepts.
2.2.2. Perceptual Discriminability
measures the ease at which a user can distinguish graphical symbols apart. Low discriminability
renders diagrams less effective in conveying information and increases the cognitive load for
novice users [8]. Visual variables represent quantifiable properties of visual objects. Retinal
variables include shape, size, colour, brightness, orientation, texture, and planar variables include
horizontal and vertical positioning in a canvas. Their combination allows for an estimation of
the visual distance between symbols, as well as how easy is to differentiate them. This guideline
is satisfied if the combination of visual variables distinguishes each concept apart.
2.2.3. Visual Expressiveness
analyses the use of visual variables and their diversity across the visual notation focusing on
the visual distance and differences between individual symbols. The expression of a concept
using a range of visual variables results in a richer representation that exploits multiple visual
communication channels. We consider this guideline satisfied if there is more than one visual
variable differentiating concepts.
11
6 13
1 21 20 22
8 7 15
10 18
24
12
3
14
23
2
25
5
9
19 17
4 16
Events Relations Collections
1 Atomic Event 1
6 Condition 11 Spawn 16 Nesting
2 Data Event 7 Response 17 Single-Instance Subprocess
12 Logical Include
3 Computational Event 8 Include 18 Multi-Instance Subprocess
13 Pre-condition
4 Global Event 9 Exclude 19 Form
14 Value
5 DMN Event 10 Milestone 15 No-response
Event Modifiers Relation Modifiers
20 Excluded Event 21 Pending Event 22 Executed Event 23 Access Control 24 Timed Constraint 25 Data Guard
Figure 2: DCR graphs: notation example
2.2.4. Semantic Transparency
studies the intuitiveness of the notation. An intuitive symbol reduces the cognitive load placed
on the reader [8], who can shift the attention to harder-to-decode elements. Semantically
perverse symbols imply a different/opposite meaning than the concept. Opaque symbols have
an arbitrary meaning. Translucent symbols provide hints to their concept but need initial
explanations. Finally, semantic immediate symbols are linked with their concept without
explanations. We consider this guideline satisfied if all symbols are semantically immediate.
2.2.5. Complexity Management
analyses the techniques used by a notation to manage large quantities of information without
overwhelming users [8]. The limitations of human perception and comprehension render
diagrams over a specific size less effective to use [16], in particular for novice users that do
not have clustering and decoding capabilities to work with highly complex graphs [17]. Two
techniques are considered: first, hierarchy allows systems to be represented at different levels of
detail, allowing modellers to control the complexity at each level. Second, with modularisation
large diagrams are divided into sub-systems, reducing the number of elements in scope and
encapsulating sub-systems as single elements. This guideline will be fully satisfied if both
techniques are present, partially satisfied if at least one technique is present, and not satisfied if
none can be found.
2.2.6. Cognitive Integration
studies combinations of diagrams and their understandability. Homogeneous integration occurs
when separate diagrams capture different areas of the system/different levels of abstraction.
Heterogeneous integration presents varying views of an area of interest. Cognitive integration
includes conceptual integration as techniques to link and contextualise diagrams, and perceptual
integration, which describes navigation between diagrams and the user’s ability to find their
destination. This guideline will be satisfied if both techniques are present in the notation.
2.2.7. Dual Coding
studies specific use cases where textual labels objectively improve the effectiveness of the
notation. PoN suggests that text should always be used as a supplement that provides further
clarification to existing encodings realised by one of the eight visual variables [8]. In particular,
PoN supports the use of texts in two cases: first, by adding annotations directly to diagrams,
users can obtain detailed descriptions without referring to documentation or another external
file. Second, in the usage of hybrid coding (text+graphics), users can reinforce and complement
the meaning of symbols. This guideline is fully satisfied if both techniques are present in the
notation, partially satisfied if one is implemented, and violated otherwise.
2.2.8. Graphic Economy
large symbol vocabularies are directly related to a decrease in notation effectiveness [18].
Moreover, large vocabularies are challenging for novice users who may be unable to understand
the diagrams without constant interaction with a legend or documentation. Thus, shifting
some aspects of the notation to textual descriptions should be considered. Strategies to reduce
complexity include: 1) reducing semantic complexity, 2) introducing symbol deficit, and 3)
increasing visual expressiveness. We consider this guideline partially satisfied if the symbol
vocabulary is below Moody’s threshold of 40 elements [8] and at least one graphic economy
technique has been implemented and fully satisfied if all techniques are present.
2.2.9. Cognitive Fit
studies how audiences require different representations of information for different types of
tasks. Novice users are susceptible to large vocabularies, whereas experts can cluster and parse
larger numbers of information as one. Providing an extended vocabulary for novices/experts
is not enough. Notation variants need to be chosen in a sensible way for each user type (e.g.,
using discriminated and transparent symbols in novice variants). Moreover, extending the
vocabulary to cater for different types of users can reduce the effectiveness for each user type in
isolation [8] This guideline is partially satisfied if the notation provides different representations
depending on user types, and fully satisfied if the variants of the language are discriminate and
implement design specific design principles for each user type.
2.3. SEQUAL
The SEQUAL framework [19] defines guidelines for the evaluation of modelling tools. We use
SEQUAL’s version for business process models [9], which considers seven dimensions. We
focus on those concerning graphical notations:
2.3.1. Empirical Quality
collects the traits of visual or textual communication used in models and their empirical impact
on understandability. SEQUAL highlights that extensive use of colour might confuse a large
minority of subjects with colour vision deficiency. Therefore, its range should be limited to
seven colours. Moreover, certain colours are designed to convey a specific type of information.
Red, for instance, is considered to have a negative connotation. Finally, SEQUAL suggests a
consistent use of a colour code to limit the association of different meanings to the same variable.
Other considerations in this dimension include emphasis mechanisms such as changes in the
size variable and the use of typographical emphasis in text labels.
2.3.2. Social Quality
this dimension studies the level of agreement on three dimensions: knowledge, interpretation,
and model. It evaluates each dimension as an absolute or a relative agreement. We will focus
on two aspects of social quality. First, the addition of naming conventions as an aid for users to
identify objects thanks to familiar forms. Second, language documentation - clear documentation
of language concepts, grammar and visual notation improves the users’ understanding of the
language and inherently increases the chances of agreement in individuals’ comprehension of
provided models.
3. Cognitive Effectiveness of DCR against PoN
We carried out a systematic analysis of DCR against the PoN and SEQUAL dimensions described
in the previous section. The analysis included the definition of metrics for the analysis (that are
not provided in the frameworks studied) followed by an independent analysis, that was revised
until reaching an agreement with a second author, that has five years of experience teaching
DCR graphs.
3.0.1. Semiotic Clarity
Events and collections are depicted using the same shape variable. The adherence to the 1:1
correspondence in the context of events is not met as the notation is overloaded to represent
both the existence of events and the effects of both computations and data events. Figure 2
presents two examples: data events O and X have different types but are represented equally,
and computational events G and Y compute different expressions. Continuing with relations,
DCR contains ten relation types, and each type is depicted by a unique combination of a coloured
arrow and an arrowhead (c.f. Figure 2). Note that while the semiotic clarity principle is observed,
the symbols used to differentiate relations are very similar (c.f.: perceptual discriminability).
Overall, we found that DCR notation presents a symbol deficit corresponding to the semantic
concepts used. However, we consider this choice justified and supported by the graphic economy
principle, restricting the vocabulary in favour of understandability.
3.0.2. Perceptual Discriminability
Events and relations are differentiable using the shape variable. However, a slight modification
of the rectangular shape is used to differentiate events and (sub)processes (see legends 1, 18 and
19 in Figure 2). At the same time, events get their borders, icons and colours modified to denote
a change in their type, their connection with other graphs, and their execution state (see legends
1, 2, 3, 4, 5, 18, & 20). While events, processes and sub-processes are identified by their shape and
colour variables, colour is user-editable, removing it from the determinant variables. Focusing
on shape only, all types of events use rounded squares, making them indistinguishable. Notice
that while events may use text annotations to describe their semantics, this has zero visual
distance according to PoN. Relations are depicted using shape and colour variables including a
customised arrowhead. However, arrowheads are small and very similar in some cases (e.g.,
+ for include and ± for logical include). The small size of the symbols makes their effect on
the user’s pre-attentive processing of the elements debatable. Moreover, some occurrences
of symbol overload are identified where a symbol (e.g., a pre-condition →♦) has the same
behaviour as the combination of others (e.g., a milestone →◇ and a condition →∙). Colour is
the second variable used to distinguish rules. In most cases, the used colours are far in their
colour spectrum, aiding distinguishability. We can document two pairs of relations where colour
variables are the same: logical include → − ± & include →+, and pre-condition →♦ & condition
→∙. These rules share similarities from a semantics perspective which is likely the motivation
behind their identical colour representation.
3.0.3. Semantic Transparency
DCR graphs is a conceptual modelling notation, thus, its notation does not include real-life
objects. Entities are represented by geometric shapes with additional modifiers. The choice of
modifiers ranges from translucent to perverse:
• Semantic transparent: an executed event is visualised with a green checkmark. This
combination of shape and colour is attributed to successful execution.
• Semantic opaque or perverse: a data event is depicted with a “turning page” modifier,
whose visual effect may be confusing as users may interpret it as an event having multiple
steps or a document. Moreover, the use of the same notation for relations (the same arrow)
may be perverse as users might associate the same meaning to all relations. Relations in
DCR graphs denote constraints with different semantics, however, their representation as
arrows might confuse novice users with a process modelling background, where arrows
represent direct-follow-relations (e.g., BPMN [20]).
On several occasions, event modifiers use text in their visual notation. For instance, grouping
types (forms, sub-processes, and nesting) are highlighted by the presence of the initial letter of
its type. While PoN does not explicitly evaluate the use of text in semantic transparency, we
consider the choice of letters as an example of semantically perverse objects when considering
the cultural background. Initial letters may generate recall for native English speakers only and
create confusion for other audiences.
Relations in DCR graphs describe behavioural constraints, and the choice of representation via
arrows might be misleading as explained above. The other variable used in their representation
is colour. The use of green and red colours for the include and exclude relations provides a hint
of a positive or a negative effect of the rule; nonetheless, they can be classified as semantically
translucent at best. Other colours, such as brown and violet, are clear examples of semantically
opaque elements. Similarly, the choice of characters as arrowheads (see uses of “→%" in Figure 2)
are classified as semantically opaque. Finally, relations can combine events and event collections
(see Figure 3, left-hand side). They use a spatial arrangement to express the relationship between
the child and the parent event. Using the principle of the spatial enclosure is known to be highly
semantically transparent compared to connecting lines [8].
3.0.4. Complexity Management
We observe one complexity management mechanism: event nesting3 . Nesting defines a visual
hierarchy between a container and its enclosed events (see Figure 3). While nesting does not
remove the essential complexity of the model, it does reduce accidental complexity (that is, the
complexity originating from the graphical representation), and it can improve the clarity of the
model.
3.0.5. Cognitive Integration
Homogeneous integration is present via the introduction of global events that synchronise
the execution network of DCR models. While there is a formal execution engine capable of
integrating networks of DCR graphs [13], the graphical notation provides only symbols to denote
when an event is shared between graphs, lacking explicit constructs to denote networks of graphs.
Moreover, DCR presents several remotely similar heterogeneous integration mechanisms. With
3
The notation mentions an additional type “transactional subprocesses” but its usage could not be replicated in our
tests
|Events| = 5 |Events| = 4
|Relations| = 6 |Relations| = 10
Figure 3: Complexity management: the use of the nesting construct (left-hand side) reduces the number
of relations without affecting expressiveness
the Process Highlighter [21]. each processing element is linked with textual annotations. Other
integrations include the link between simulation and DCR notation.
DCR networks would play a significant role in developing complexity management mecha-
nisms. Developing a layer-based set of homogeneous diagrams with varying degrees of detail,
similar to a process architecture diagram [22] will help both conceptual and perceptual integra-
tion. Notably, having a “root” diagram provides the user with a starting point and navigation
capabilities to deeper layers of the process hierarchy [8]. Finally, we do not observe navigation
capabilities for DCR graphs. While in imperative process notations there is a clear notion of
start/end events, in declarative notations like DCR, the ability to execute events in DCR is
entirely dependent on the marking and the constraints of a given event. This complicates the
definition of layouts that could be fitted to users’ reading behaviour, requiring users to perform
a full graph exploration to orient themselves on what can be done and how to achieve their
goals.
3.0.6. Visual Expressiveness
Shape, and in the case of relations, colour, is the sole information-carrying variable used by
DCR. While colour should not be the only deciding variable, it is highly effective in reducing
cognitive load. Other variables such as texture, horizontal and vertical position and size, are
either used seldom (e.g., dashed texture for excluded events) or are free variables. The next
variable used is text. However, PoN discourages the use of text as means to encode semantic
elements: while textual elements allow to precisely label objects, they increase the cognitive
load on the users’ side and provide no value during pre-attentive processing. Moreover, labels
are often crucial on the sentence level where they distinguish individual object instances. This
cannot be achieved if the notation uses text labels to identify symbol types.
3.0.7. Dual Coding
The principle of dual coding is covered in annotations and symbol/text combinations. Annota-
tions are not directly represented in the notation, and users need to shift focus onto different
artefacts in order to integrate additional information. However, while the notation does not
support annotations, tool support provides users with integrations with source information
and event explanations, that have a positive impact on process model comprehension [23].
Regarding hybrid text/symbol combinations, the use of hybrid representations in DCR notation
is limited to timed relations and guards, however, since both annotations are using the same
visual variables (colour), it is possible that users find lower perceptual discriminability between
them.
3.0.8. Graphic Economy
By March 2022, DCR contains a vocabulary of thirty-two distinct visual elements. This vo-
cabulary is below PoN’s threshold for excessive vocabulary. DCR introduces a symbol deficit
to reduce some of the complexity in the graph (c.f. Section 3.0.1). No partition of semantic
complexity is evidenced. As mentioned in Section 3.0.2 shape is the sole information-carrying
variable in DCR and adding multiple visual variables to differentiate concepts will increase
visual expressiveness, thus increasing perceptual discriminability.
3.0.9. Cognitive Fit
DCR presents a relation palette for novices and another for experts. Currently, there is no clear
segmentation between the two modes, and the relations offered to novices add to the constraints
offered to experts. When considering the representational medium, sketches of DCR models
in whiteboards renounce the use of colour variables. This change is compensated by adding
emphasis to the shape variable of constraints, for instance, by modifying the sizes of symbols in
constraints to facilitate perceptual discriminability.
4. Cognitive Effectiveness of DCR against SEQUAL
4.0.1. Empirical Quality
We observe a mixed application of SEQUAL guidelines: relations use a fixed palette, and events
and subprocesses have editable colours. Such liberty allows users to endow events with different
meanings than the intended one (e.g., events can be coloured as subprocesses and vice-versa).
The use of red-green colours to represent exclusion-inclusion constraints relates to SEQUAL
guidelines to the transparent colour coding. Furthermore, we observe that the size variable is
fixed for events, and there is little use of typographical emphasis tools. Bold typefaces are used
in the description of roles. This differentiation likely helps users separate ownership of events
from their content. Positioning of labels is used for guards and delays. However, positioning is
likely to be insufficient once relations link events in a vertical plane (c.f. relation 𝐷∙→𝐻 in Fig
2), and, in some cases, perverse to the understanding of the graph (e.g., the colour label overlaps
with a coloured relation). With respect to event positioning, DCR does not offer a guideline
on how to layout them, but it is likely that experts define their own layout scheme following
reading conventions [10].
Dimension Summary of the analysis
Physics of Notations
Semiotic clarity Partially satisfied (symbol deficit)
Perceptual Partially satisfied
discriminability
Transparent: executed events, spatial enclosure in event collections.
Semantic Translucent: colour choice for include/exclude relations.
transparency Opaque or perverse: complex events (subprocesses, nesting, transactional, data),
colour code in other relations.
Complexity Partially satisfied: reduced accidental complexity but limited modularization
management support.
Cognitive Partially satisfied: homogeneous integration is absent. Heterogeneous integration
integration is evidenced. Conceptual & perceptual integration capabilities are absent.
Visual Partially satisfied. The shape is the only primary variable, but the colour variable
expressiveness is bound in some cases.
Dual coding Partially satisfied (text annotation is not supported, hybrid coding is supported in
special relations).
Graphic economy Partially satisfied. Visual vocabulary is not excessive and symbol deficit reduction
techniques are. A partition of semantic complexity is absent.
Cognitive fit Partially satisfied (novice/expert relation palette is suggested, but notation variants
are not discriminable).
SEQUAL
Empirical quality Partially satisfied. Manageable colour spectrum, but inconsistent colour usage.
Colour choices are not recommended for colour vision deficiency users. Limited
use of typographical emphasis.
Social quality Not satisfied.
Table 1
Cognitive dimensions in DCR: summary of findings in Sections 3 and 4.
4.0.2. Social Quality
Naming conventions aid users in identifying objects thanks to familiar forms (e.g., a linguistic
pattern “verb+object" signifies an activity performed in a process). We noticed that such a
pattern is not always used in DCR. Events are placeholders for both actions that participants
perform, and external sources of change (e.g., a deadline is reached, a message is received [10]).
The differentiation of controllable and uncontrollable events has been studied in literature [24]
and their graphical representations are different in other process modelling notations [20]. This
overload of the notation for two different concepts might likely affect users’ understandability.
The application of linguistic patterns will probably help if there is a clarification of what consti-
tutes an event in DCR. Regarding language documentation, the only source of documentation
comes from a company website4 , and there is no standardised documentation. The volatility
and likely change of the sources inhibit the generation of a common understanding and limit
the creation of other tools that can support the notation.
4
https://documentation.dcr.design/
Nr. Improvement suggestion Concerning dimensions
1 Relations between events should use more visual variables than shape and Perceptual discriminability, visual ex-
colour. pressiveness, empirical quality
2 Other symbols different than arrows should be used in order to facilitate Semantic transparency,
discriminability and understanding of relations. perceptual discriminability
3 Adding symbols to denote delays, timeouts and data guards will facilitate Perceptual discriminability, semantic
their identification and differentiation. transparency
4 Adding additional symbols to denote controllable and uncontrollable events Visual expressiveness, graphic econ-
will support model understanding. omy, social quality
5 A colour-neutral version of DCR graphs will support colour-blinded minorities Empirical quality, social quality
and relate to analogue versions of DCR models
6 Colours and shapes should not be reused to denote the different types of Perceptual discriminability
relations.
7 Different types of events (e.g., nesting, form, computation, subprocess) should Perceptual discriminability
be identifiable via a change in the retinal variables
8 Events and event collections (nesting, and the different types of subprocesses) Perceptual discriminability
should differ in the choices of the shape variable.
9 Novice/expert modes need to be clearly separated, thus reducing the number Cognitive fit
of constraints shown at each mode.
10 High-level, non-executable representations of DCR graphs might help users Complexity management
to navigate over complex process architectures.
11 Adding the principle of cognitive integration for networks of graphs may help Cognitive integration
in the contextualisation of process architectures
12 Users should be able to complement the graphical notation with their own Dual coding
comments & annotations.
13 Provide a standardised reference model for the graphical notation. Social quality
Table 2
Improvement suggestions according to cognitive dimensions
5. Implications for Research and Practice
We suggest a roadmap for the improvement of DCR notation based on the analysis presented. It
suggests that notation extensions should make an effort in designing perceptually differentiable
elements by novice users, using colours that are inclusive to visually impaired subjects. Moreover,
it suggests the importance of supporting novices in their learning process by allowing them to
annotate their models (dual coding), navigate complex models (complexity management) and
even provide model transformation techniques between expert and novice views (cognitive
fit). These suggestions, as well as the suggestions in Table 2, can be operationalised by tool
developers, or used as research hypotheses to be further tested via user studies. Moreover, the
analysis executed in perceptual discriminability, semantic transparency and social quality can
be transposed to the DECLARE process modelling notation [2], as it uses similar symbols to
denote events and relations as DCR graphs.
6. Related Work
While previous works on the cognitive effects of imperative process modelling notations using
PoN and SEQUAL exist [25, 9], to the best of our knowledge, this paper presents the first system-
atic analysis of a declarative process modelling notation using general cognitive effectiveness
frameworks. Other works use PoN partially. PoN has been used to study the semiotic clarity
of CMMN [26]. Figl et al [27] conduct empirical experiments on the semantic transparency of
DECLARE models. Their assessment suggesting that the representation of constraints as arrows
is semantically perverse to users is in line with our analysis in Sec. 3.0.3. PoN’s graphic economy,
cognitive integration, complexity management and semiotic clarity dimensions have been used
to drive novel graphical representations of DECLARE [28]. With respect to SEQUAL, the role
of layout and edges in the understandability of imperative process models [29]. Regarding
notations for declarative processes, Colombo Tossato et al. [30] proposed a visual notation for
norms, conditional and defeasible rules including a larger set of visual variables (texture and
shape). The representations of relations might be especially useful for DCR as the semantic
concepts are similar. Finally, Andaloussi et al. [10] use personal construct psychology to define
a quality framework from experts in DCR graphs.
7. Conclusion
We provided a systematic analysis of the cognitive effects of DCR graphs in the context of two
general frameworks for the understandability of notations and provided a set of 13 guidelines that
could improve the cognitive effectiveness of the notation. We believe that the implementation of
such guidelines will benefit DCR graphs, as PoN principles have been demonstrated to positively
influence a notation’s perceived usefulness [31]. It is worth mentioning that even if PoN and
SEQUAL provide guidelines for evaluation, they gave no operational support [32]. Even when
our paper describes the analysis metric used, a purely unbiased and objective observation is not
possible using the current setup unless users are involved. Moreover, some of the guidelines
may have counterarguments and further empirical analysis needs to be conducted. This is the
case for modularization: the application of modularization might require an additional mental
effort due to the need to switch between sub-processes and integrate information [33]. In future
work, we would like to complement this work with larger user studies and extend it to other
declarative notations, such as DECLARE and CMMN.
Acknowledgements We would like to thank Amine Abbad Andaloussi and Barbara Weber
for their comments in earlier versions of this paper.
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A. DCR graphs metamodel
Figure 4: DCR graphs metamodel