=Paper=
{{Paper
|id=Vol-3115/paper3
|storemode=property
|title=Social-Collaborative Inductive Reference Model Mining in a Knowledge-Based Organization
|pdfUrl=https://ceur-ws.org/Vol-3115/paper3.pdf
|volume=Vol-3115
|authors=Andreas Sonntag
|dblpUrl=https://dblp.org/rec/conf/eewc/Sonntag21
}}
==Social-Collaborative Inductive Reference Model Mining in a Knowledge-Based Organization==
https://ceur-ws.org/Vol-3115/paper3.pdf
Social-Collaborative Inductive Reference Model Mining
in a Knowledge-Based Organization
Andreas Sonntag1
1 Saarland University, 66123 Saarbrücken, Germany
Abstract. The aim of reference model mining is to support the efficient
execution of process instances. Social network analysis has a great potential for
reference model mining as it reveals social and functional relations, which are
critical for the efficiency of collaborative business processes. This study
demonstrates and evaluates an approach to applying social network analysis to
a human aspect of reference model mining. The approach is based on a dynamic
performer network, which is an evolving social collaboration network in a
knowledge-based organization. For this purpose, agent-based simulation is
applied to a longitudinal dataset concerning researcher collaboration in an
internationally renowned 'center of excellence' for industry-oriented research in
the field of artificial intelligence. The resulting performer network can be used
as a reference model for efficient researcher collaboration, and it is reusable for
future process execution of similar organizations.
Keywords: Reference Model Mining, Human Aspects of BPM, Researcher
Collaboration.
1 Introduction
One important requirement for individuals who participate in a multitude of business
processes is the availability of business process models which can be executed
efficiently. A reference model should represent an efficient and reusable
implementation of processes in an organization, simplifying internal structures to
reduce the complexity and resources needed for business process mining [1], [2]. Our
research community ignores the influence of social collaboration between people
working on processes, called “performers” in the following. Since business processes
entail social processes, it becomes essential to employ social network analysis for
reference model mining in the knowledge work domain. Previous research on
collaboration around knowledge-based processes in the field of business process
management are the following: Tomasello and colleagues [3], [4] investigate the
formation and performance of collaboration networks by evaluating over time the link
formation events involving a knowledge flow between the collaborating parties. [5]
introduce a method to interpret the workflow using social networks inferred by
interviews and questionnaires with employees. The authors seek to identify gaps
between the management view on processes and their actual execution. [6] introduce
a concept for the derivation of a reference process model from knowledge-based
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2
process models. Thereby, a performer network with topological success structures,
holding for a corpus of given process models denotes a reference process model. [7]
introduce the concept of "generated performer networks" for optimizing the efficiency
of process models to determine "topological success properties" applying agent-based
simulation on a given event-driven process chain.
Originated on the static performer network concept of [6], we introduce a
procedure to derive a reference model from a process execution observed in a
knowledge-based organization. This reference model consists of a reference social
collaboration topology that should be similar to a real organization, independent of an
organization-specific performer assignment to process functions. The reference model
is able to explain the evolvement of the past organization and to show its re-usability
by predicting the organization’s future evolvement concerning social collaboration
topology and efficiency. The paper is structured as follows: First, we describe
fundamental terms and methods, then we explain our approach; this is followed by the
evaluation of our concept with the experimental design, its evaluation and the
discussion of our findings. Subsequently, a conclusion brings this work to a close.
2 Performer Networks
Social network analysis (SNA) investigates similar social structures in organizations,
especially in the communication behavior between individuals, applying mainly
methods of graph analysis [8]. SNA should be applied on aggregated data when
increasing the observation scales [9]. In addition, structural network properties are to
the fore towards the outcome of the actual human relationships [11]. Hence, the social
topology of people has, traditionally, a greater impact on the result of their
collaboration than certain aspects of their collaboration such as e-mailing frequency,
sympathy or locations. Many SNA theories and studies are based on simplified but
plausible and broadly examined social structures, particularly the formation of
clusters/groups, the emergence of hierarchy, sparsity and short paths [9], [12]–[15].
A process model (short PM) is represented as an event-driven process chain. This
is a directed graph structure, consisting of a set of edges that indicates an order
between a set of process functions and operators. Each process function has a label,
which is an expression in natural language and describes the action(s) to be done at
this point in the process flow. Events and other (meta) information that might be
provided is not considered. A performer network (short PN) extends a PM by a social
network of collaborating performers. Performers are agents working on process
functions with a set of capabilities in a PN. Capabilities for the purposes of this study
are simplified as a set of mappings between a process function and a number
indicating the extent of being capable/efficient to work at this function. Every
mapping in [0;1] is possible, even mappings with nonexistent functions in order to
represent ineffective performer/capability combinations. PNs are formalized as social
networks which contain performers as nodes having a unique ID, social connections,
capabilities and the ability to work at a process function to which he is assigned to;
and two kinds of edges: social edges that connect performers and functional edges
that connect performers with process functions (assigned_nodes). The other arrows
3
are process edges, connecting process nodes. Additional definitions: A path is a
sequence of edges that connects two nodes. The degree of a node is the number of
directly connected neighbors. The mean degree of a PN is the averaged degree over
all performers. The average clustering coefficient is the actual number of edges
between an agent’s neighbors divided by its possible number, averaged over all
2𝑚
performers. A PN’s density is defined as with n the number of performers
(𝑛(𝑛−1))
and m the number of social edges in the PN.
[16] see process efficiency as time and money saving potential for the process
execution. [16] require, all processes to follow a sequence of tasks over time
timepoints. From a resource-oriented perspective, efficiency can be seen as the
minimal use of resources for a certain goal. A comparable definition for the efficiency
of an organization is summarized by [17], defining the term "organizational
effectiveness" as more than a financial profit but also as an efficient work of
employees and managers for the outcome of the organization. Social collaboration is a
resource in the scope of organizational structure. In the context of process execution
in an organization, a goal is the completion of a sequence of tasks that is based on the
process design. The time needed for the goal to be reached depends on the
coordination of the actors participating in the effort to complete the tasks. Other
resources that cannot (inter)act autonomously such as inanimate goods (steel, paper
etc.) can be assumed to have a constant influence on the goal achievement as they can
only be used and not contribute an effort. This means that only actors can influence
their utilization. Actors in turn need collaboration to utilize resources, especially if
different resources require different capabilities to deal with them. As an indicator for
the impact of hierarchy on a PN, we measure the number of key performers for each
year. A key performer is, adopted from the hub definition by [18], defined as an
individual with a degree over three standard deviations above the mean degree.
Hierarchy is constituted by a very small minority of key performers, standing against
a vast majority of performers with a degree around the mean degree. This structure is
often observed in real social networks [13].
A formal and quantitative effort/efficiency definition of a PN is introduced and
explained in detail by [7] and [6] as an algorithm based on social network analysis
and agent-based simulation: The efficiency of a PN can be computed and optimized
only in combination with a PM or a set of PMs. Efficiency in this formal context
means how little effort the performers need to undertake to complete one or several
tasks simulated simultaneously through a given PM. The effort for the performers to
complete the tasks is the sum of capabilities needed by the performers to reach the
tasks effort function by function following the process control flow. Neighboring
performers help each other with half their common capability. PN effort describes the
sum of effort for the whole PN to complete all tasks. The efficiency of a PN in
combination with a PM, called “PN efficiency”, is defined on the basis of [7] as 1-
PNeffort/(max_effort) with max_effort as the maximal possible PN effort referring to
a PN consisting of only one incompetent performer, who has a universal capability of
0.1, assigned to all process functions. The PN efficiency definition punishes a PN
with many capable performers. A different definition of efficiency, which we outline
as activity intensity, comes from [3], [4] as the number of collaboration events (tasks
4
in our context) on which an agent worked in a time window in ratio to the total
number of collaboration events involving all agents in the network in the same time
window, averaged over all agents. Finding an efficient PN around PM(s) requires
finding an efficient combination of social topology, functional topology and
capability distribution. We establish a social topology using the power cluster
network generator by [19] which is an extended version of the Barabasi-Albert model
[12] that replicates all of the plausible social structures mentionned above. This
network generator was tested by [7] to be very effective for generating efficient PNs
around a variety of process models.
3 Approach
Our goal is to optimize the efficiency of the social collaboration topology, measured
by means of PN efficiency [7], to let a set of performers collaborate to accomplish all
pregiven tasks over a period of time. Our approach is based on agent-based simulation
[20] and social network analysis [21], [22]. For the subsequent experimental design,
we make the following assumptions for the approach, which are also limitations:
1. All performers work 100 percent on all of their tasks. They have a constant
minimal competence for other tasks.
2. An organization is reduced to performers, tasks, social- and functional assignments.
3. All tasks follow the same process model; the model topology implies serial and
parallel work.
4. All tasks have the same effort of 1.0, simulated or not.
5. An organization grows at a linear pace in terms of number of tasks and performers.
6. The efficiency of social collaboration is measured with activity intensity [4] and
PN efficiency [7].
7. Time periods are discrete and equidistant, independent of the exact effort needed.
Algorithm 1. Simulates a Timeline of Performer Networks (PNT[t : timepoint]) to
Fulfill a Given Set of Tasks
With simulateTimeline(initial_Pnumber,tasks) the evolvement of the social links
between the performers, the distribution of their capabilities and process assignments
are simulated. initial_Pnumber is the number of performers in initial_timepoint. tasks
is a set of tasks where a task is a process instance or, in other words, one complete
execution of a process model. As many performers are created as in the observed
organization at initial_timepoint. The tasks to do here are those that were done by the
observed organization at initial_timepoint (tasks[1988]). The procedure of simulating
the evolvement of social edges is based on the extension of the BA model by [19].
Now, the particular tasks are simulated through the generated PN following the
approach of [7]. Therefore nesesary capabilities are distributed over the performers
(distributeCapabilities). Every performer’s capability for a process function is normal
distributed with N(0.5,1) and constrained co-domain to [0;1]. This gives every
performer a chance of 65.5 percent to be more competent than the minimum
competence of 10 percent. The assumption here is that every employee has always a
minimal knowledge of all process functions. Thus, the flexibility of employees in
organizations to substitute other colleagues is taken into account. Next, the performers
5
are assigned to the process functions (assignPerformers). Thus, a performer can pick
his process assignment according to his best capability and other assignments with the
probability proportional to the extent of the corresponding capability. With
distributeTasks(tasks, performers), now the tasks are uniformly distributed over the
|𝑡𝑎𝑠𝑘𝑠|
performers, so that each task is assigned to performers. Then,
|𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑒𝑟𝑠|
optimizeEfficiency determines and optimizes/minimizes the PN effort of the PN at that
time point with the implementation of [7] and [6]. The optimization reestablishes the
social edges by tuning the network generation parameters from [19]. In the next step,
the algorithm continues with the next time point (initial_timepoint + 1) in which a
new performer network is generated.
4 Experimental Design
We implemented our approach into a Java-based research prototype. The hardware
configuration for the execution of the evaluation scenario comprises 64 AMD
Opteron(TM) 6272 processors @ 1.40GHz and 16GB of RAM. As an evaluation
scenario, we employ all scientific publications of the German Research Center for
Artificial Intelligence from its foundation with 12 authors in 1988 until 31.12.2017.
The organization is the biggest research center in the area of artificial intelligence
worldwide, both in terms of number of employees and of external funds; it belongs to
Germany’s prestigious "Centers of Excellence". According to the most recent data, it
has 480 highly qualified researchers and 376 graduate students from more than 60
countries; they are working on 180 research projects. Many of those research projects,
which produced a total of 7520 scientific publications, written by a total of 6704
authors, are co-operations with industry.
Fig. 1. Process Model for Research Publication
The number of publications rose from 8 in 1988 to 370 in 2017, with an annual
average increase of 12.48 (standard deviation: 41.82). The PM in figure 1 describes
the minimal requirements for the typical publishing research papers process which are
explicitly documented in the organization. Only authors are performers. Each
6
publication is a task for the performers to fulfill. With simulateTimeline(12,tasks), for
each publication, a PN is derived from the present co-authorship data (not with the
social network generator) consisting of the authors involved and the year of
publication. The set tasks corresponds to all 6904 publications mentionned above. The
first PN has 12 performers as there were 12 authors in 1988. For each year,
performers are connected if they are co-authors. As a result, we have a timeline of
PNs consisting of one PN of all co-authorships for each year. In the following, this
timeline is called “observed PNT”. simulateTimeline(12,tasks) is also executed in the
same parameterization but with generating the social edges. The result is referred to
as reference PNT. To pronounce the design implication again, both PNTs are
simulated with simulateTimeline, each with the same assumptions.
In order to validate, the results of our experimental evaluation scenario must be
reproducible (reliability), must explain the model quality (internal validity) and must
produce a result that is generalizable/transferable (ecological validity) [23]. The
reliability is reached by test-retest, the repetition of all PN efficiency simulations
within the approach. The model quality is quantified by the statistical effect between
the starting parameters and the model parameter evolvement. For proofing the
generalizability of the approach, the significant correlation between both efficiency
measures (see section 2) of the observed PNT and the generated reference PNT has to
be shown. We also predict the organization’s future evolvement by the same
procedure as the reference PNT is developed. For the prediction, the observed PNT
from 2010 until 2017 forms the basis for the reference PNT to be evolved from. The
prediction is limited to 2022 as our memory capacity is reached at this point of
computation effort.
5 Evaluation
The scenario described above yields two simulation results, observed- and the
reference PNT, both covering the timespan in which the evolved organization existed.
As stated in section 1, we want to explain the evolvement of efficiency in the
observed organization with a reference PNT, generated by the simulation described in
section 3. Figure 2 compares activity intensity, PN effort and PN efficiency between
reference (ref) and observed (obs) PNT over time. Topological properties (average
clustering, density and mean degree) between reference and observed PNT are also
compared. The average slopes of all efficiency values and the topological properties
have, over all years, the same sign. The PN effort for the task execution correlates
with the number of tasks over time (r = 0.97, p < 0.01) for both PNTs. The PN effort
increases for both PNTs but is much greater for the observed PNT. In the observed
PNT, density and PN efficiency correlate with r = −0.99 (p < 0.01). This correlation
for the reference PNT amounts to r = −0.91 (p < 0.01). The correlation between
activity intensity and PN efficiency in the observed PNT amounts to r = 0.49 (p <
0.01). The same correlation for the reference PNT amounts to r = 0.35 (p < 0.05). The
independence of the PN efficiency from individual time windows has to be ensured
because we want to show that the efficiency of performer collaboration depends on
the social collaboration structure and not on the arrangement of single time windows.
7
Fig. 2. Reference (ref) vs Observed (obs) Performer Network Timeline with Fit Line
Therefore, we tested hypothesis that the mean of all PN efficiency values for each
time point is significantly different to the rolling mean over all possible time windows
of a 3-years width. The hypothesis was rejected with p < 0.05. The number of key
performers evolve almost linearly over time in the observed and the reference PNT.
On average per year, the number of key performers grows by 0.21 (standard
deviation: 9.6) in the observed PNT. In the reference PNT, 0.1 key performers
supervene as a mean (standard deviation: 1.74). The more tasks are to be done, the
more key performers appear in both PNTs. For both PNTs, the following variables
have an impact on the number of key performers (in descending order): tasks, PN
effort, activity intensity. All influences are strongly positive r > 0.7 (p < 0.05). The
PN efficiency has no significant influence of r = 0.25 (p < 0.1) on the number of key
performers for the observed and the reference PNT. This insignificance is caused by
the PN efficiency to suddenly and over-linearly increasing with more than 2 key
performers.
We predict the organization’s future evolvement from 2010 until 2022 and
compare it to the observed evolvement in the same time window. The observed PN
efficiency stays almost constant at 0.9999 between 2010 and 2017. Our predicted
PNT becomes 0.0003 percent more efficient over time. The activity intensity
increases linearly over time for the observed and the predicted PNTs. The absolute
8
values however are different. In our prediction, the activity intensity is always greater
than 0.93, whereas the observed activity intensity lies between 0.0003 and 0.0007.
The PN effort decreases from 6384 to 4905 in our prediction. In the observed PNT,
the PN effort ranges from 25096 to 36756. The average clustering coefficient in the
predicted PNT is on average 123 percent higher than in the observed PNT between
2010 and 2017. Density is falling from 0.0061 to 0.0057 in the predicted PNT and
increasing from 0.02 to 0.03 in the observed PNT. The average mean degree increases
by 0.002 percent for the predicted PNT and 33 percent for the observed PNT.
6 Discussion
Activity intensity and effort increase almost linearly for the observed and reference
PNT which means that the reference PNT reaches, for each point in time, an equal or
even higher efficiency reached by a lower effort than the observed PNT. All topology
parameters, as they are plotted in figure 2, indicate the same slope over time for the
observed and reference PNT. The mean degree for the observed PNT increases
linearly over time while its reference counterpart reaches its maximum already after
10 years. For both PNTs, the density decreases over time and correlates with the
increasing PN efficiency. The density in the observed PNT is much greater than in the
reference PNT for all time points while the density within the observed clusters is
much higher than in the reference clusters. The reference PNs have sparse clusters,
which are but densely inter-connected. Because of the significant correlation between
density and PN efficiency, the density of inter-connection between clusters drives the
collaboration efficiency, more than the intra-connectivity of teams. The significant
correlation between activity intensity and PN efficiency speaks for the
generalizability of our approach as both efficiency measures indicate an increase of
effective collaboration effort. That means, processes in a knowledge-based
organization can be modelled efficiently based on the connection of social and
functional reference topologies found by this approach.We predict the organization’s
future evolvement until 2022. The positive evolvement of activity intensity and PN
efficiency over time indicates the evolvement of an efficient social topology around
the publication process based on the observed PN in 2017. During the same period,
the PN effort decreases in the predicted PNT, in contrast to the observed PNT.
Our explanation for this contrast is the PNs in the predicted PNT to have much
more social edges between clusters than the observed organization has. Meanwhile, in
the observed organization, on average, more key performers appear than in our
reference PNT. That implies the observed performers reached their tasks with a
similar efficiency but with more PN effort and more densely connected key
performers. Our reference PNT, including the predicted PNT, reaches a smaller PN
effort by more social edges between clusters. That way, less collaboration effort
respectively fewer social edges are needed between the performers at a common
process function to be efficient. The key performers seem to play an important role in
the organization’s collaboration coordination. Most key performers are managers, for
example research group leaders, which means that the team-overarching cooperation
between managers is a critical structure for efficiency. This means, that the social link
9
generation from our PNT simulation procedure (see algorithm 1) can be used to
reproduce an efficient collaboration topology in an evolving knowledge-work
organization. Our PN simulation can thus be seen as a reference for the efficient
placement of personal around a process to be executed in an evolving organization.
Translated into a recommendation for modelling efficient collaboration, a performer
network should attract more team members around managers and become less dense
over time. This evolvement is supposed to lower the costs for social communication
by shorting paths in a growing organization.
Our assumptions for the PN efficiency simulation, the simplification of the co-
authorship process and the constrained generalizability of the co-authorship towards
knowledge-based business processes in general entail limitations of our PN model and
our findings. The social environment of the authors, their resource/knowledge
allocation and transfer are not taken into account. In addition, our approach has no
explicit time limit for the end of the PNT. This means that the simulated organization
can take a longer or a shorter time to complete all given tasks.
7 Conclusion
In this paper, the performer network concept of [7] is applied on a set of tasks
executed by real collaborative knowledge workers in order to generate a dynamic
performer network that completes the given tasks efficiently. By the comparison of
the performer network efficiency to a different measure of collaboration efficiency,
the activity intensity [4], topology structures of collaboration, similar to the observed
over time, were replicated. We tend to regard the performer networks replicating this
topology as generalizable references, which may be used by practitioners as
guidelines for inferring efficient performer networks around process models of other
knowledge working organizations. Furthermore, our approach can quantify the trade-
off between team size vs density vs hierarchy vs efficiency-critical social links for the
(re)design of processes in such organizations. In particular, this includes the practical
issue of determining the number and position of team/division leaders and
knowledge/information transfer hubs necessary for a certain process. Applying our
"reference topology", this issue can be resolved before the process is even established.
Thus, the risks and costs for the process execution become more transparent and
controllable.
In future work, we aim to compare our results to further real-world performer
networks by using a larger and more diverse data set such as evaluating event logs of
the execution of business processes. In particular, we want to understand how exactly
process model topologies affect the performance of the assigned performers.
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