1
BPM to Go: Supporting Business
Processes in a Mobile and Sensing World
Rüdiger Pryss, Manfred Reichert, Alexander Bachmeier, Johann Albach
Ulm University, Germany
Abstract
The growing maturity of smart mobile devices has fostered their prevalence
in a multitude of business areas. As a consequence, business process
management (BPM) technologies need to be enhanced with sophisticated and
configurable mobile task support. Along characteristic use cases from
different application domains (e.g., healthcare and logistics), this chapter will
give insights into the challenges, concepts and technologies relevant for
integrating mobile task support with business processes. Amongst others,
we will show how mobile task support can be enhanced with location-based
data, sensor integration, and mobile task configuration support. The latter is
based on a 3D model for configuring mobile tasks on smart mobile devices.
Introduction
In the computer industry, the emergence of smart mobile devices has opened
up new and exciting perspectives. Nowadays, we carry computers in our
pockets that wouldn’t have been out of place on a supercomputer ranking of
the 1990s. The ever increasing ubiquity of these smart mobile devices and
the dynamic nature of Business Process Management (BPM) technology
demand new concepts and systems that may execute tasks on these smart
mobile devices. For example, to assist a physician during her daily work
through sophisticated mobile task and process support may ease her work
significantly [19]. However, the smooth integration of smart mobile devices
into the BPM landscape has revealed a multitude of specific challenges. One
way to properly meet these challenges is through a sophisticated mobile task
execution framework that can be smoothly integrated with existing BPM
environments. A specific challenge in respect to such an integration
concerns the proper configuration of mobile tasks. In this context, the
chapter proposes an approach that enables the domain expert (i.e., the end
user) to configure mobile tasks by a 3D process model, which is displayed in
an augmented reality view on his smart mobile device.
Motivation
While smart mobile devices have evolved rapidly [15], they still show
limitations that need to be considered when integrating them with BPM
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systems [17]. Current limitations include, amongst others, limited battery
power, instantaneous shutdowns, data inconsistency, and unreliable
network connectivity. As a consequence, one cannot simply migrate the
execution of complete processes and their tasks onto mobile devices without
coping with these issues.
The last years have shown a divergence towards smart mobile devices, with
only a small number of tasks not ported to a smartphone or tablet in one
way or another. Accordingly, users more and more expect from their smart
mobile devices to assist them in fulfilling almost every task they have
processed on their stationary PC. BPM technology should pick up this trend,
not only due to emerging customer demands, but also because it opens up
new and promising opportunities.
In general, BPM serves as an approach to analyze, model, automate,
monitor, and optimize business processes in a variety of application domains
[11]. Particularly, BPM improves business IT alignment and serves as a glue
between information technology on one hand and the various business
stakeholders (e.g., staff, customers, and business partners) on the other [8].
In this context, BPM to Go represents our vision of coupling smart mobile
devices with BPM technology in order to enable flexible process and task
assistance of mobile (knowledge) workers. Amongst others, the following
areas need to be touched to make this vision a reality:
1. Mobile task execution & configuration
a. Smooth integration of mobile tasks in existing BPM
environments [19]
b. Tackling challenges of a mobile execution context (e.g.,
instantaneous shutdowns) [17].
2. Distributed mobile processes (e.g. cross-departmental as well as
cross- organizational scenarios) [7].
3. Collaborative mobile processes (e.g., mobile checklists for
collaborating knowledge workers) [16].
4. Mobile office in combination with BPM (e.g., using personalized smart
mobile processes) [4].
5. Cyberphysical systems and Internet of Things in combination with
BPM [5].
Contribution
This chapter focuses on mobile task assistance in general and mobile task
configuration in particular.
The primary goal of mobile task assistance is to enable end users to work on
their business tasks using smart mobile devices instead of stationary PCs.
Smart mobile devices not only allow performing these tasks almost
everywhere, but also enable measurements on the spot. Furthermore, the
interaction of mobile workers with smart mobile devices fosters process and
task flexibility as well as a faster completion of business processes. Figure 1:
Mobile task approaches depicts three approaches for integrating smart
mobile devices with BPM. The support of these mobile scenarios reveals
challenging issues that need to be properly addressed, e.g., in respect to
process exception handling [13] and task failure management [17].
Beyond the collaborative and mobile aspect, the connectedness of smart
mobile devices and their sensors allows for the integration of additional
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context information and parameters with business process execution. For
example, a location parameter may be used to store information about the
location a particular task has been performed [13].
The usage of smart mobile devices needs not be limited to the execution of
single tasks. As demonstrated later in this chapter, the ability to perform
task configurations on the Go opens up new opportunities. Our idea for
mobile task configuration revolves around the concept of a 3-dimensional,
augmented reality view of processes, which is generated using a common
Android smart mobile device. Based on this view, domain experts and users
shall be enabled to configure and optimize mobile tasks on the Go.
To the best of our knowledge, there are no comparable approaches dealing
with mobile task execution and configuration. In particular, the
configuration of mobile tasks directly on a smart mobile device has not been
properly addressed so far. However, there exist approaches that characterize
a mobile context through a set of parameters [9]. Usually, only few contextual
parameters are considered and these mainly focus on process characteristics
[12]; e.g., to be able to decide what shall happen with a task if a device is
unavailable. In turn, no parameters are maintained to characterize the
different kinds of failures (e.g., low battery power).
The remainder of this chapter is structured as follows: First, we discuss
lessons learned in the context of real-world scenarios and relate them to the
“BPM to Go” vision. Second, we describe relevant aspects of mobile task
execution along an example from the healthcare domain. Finally, we present
an augmented reality engine being able to display a 3D representation of
processes on a smart mobile device to assist users in configuring mobile
tasks.
FIGURE 1: MOBILE TASK APPROACHES
Background Information and Considered Scenarios
In the context of our research on mobile processes [6, 17], a number of
approaches have been designed that provide robust ways to deal with the
challenges of mobile task support. To further evolve these approaches (cf.
Figure 1), both their feasibility and their limitations were investigated in a
number of real-world scenarios. The three considered approaches differ in
single mobile task handlingphysical process fragmentation logical process fragmentation
approach 1 approach 2 approach 3
statically
determined
statically
determined dynamically
determined
dynamically
determined
mobile
task
mobile
task
mobile
fragment
mobile
fragment central process :
coordinating the fragments
migration: dynamically
determined
migration: dynamically
determined
migration: dynamically
determined
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respect to the part of the process known to a device, the information
exchanged, and the way the tasks are synchronized between the devices.
To confirm the importance of mobile task support, different application
scenarios from the healthcare, automotive and psychology domains were
investigated. As will be shown in the course of this chapter, the benefits of
enhancing scenarios with smart mobile devices are manifold. For example,
through the use of smart mobile devices, the efficiency of how tasks are
performed can be improved. As another benefit, data can be collected and
recorded at the right time and place [18]. Furthermore, the occurrence of
erroneous data can be decreased since data can be processed and checked
on the device actually running the data collection procedure.
While some of the discussed challenges are known to mobile app developers
in general, the execution of mobile tasks in the context of business processes
particularly raises demands in respect to robustness and usability. We
present four categories of challenges to be addressed in this context.
Challenge 1: Process-related
Regarding process-related challenges, the focus should be on data
consistency. In general, network connectivity will be unstable, compared to a
physically immobile system like a workstation or server. During any point in
the execution or configuration of a process, network connection problems or
power issues might arise. Consequently, the challenge is to keep the data of
executed process consistent even when unexpected exceptions occur.
Challenge 2: User Behavior
An issue not specific to mobile environments, but of higher relevance
compared to stationary devices, concerns the user as a source of
irregularities. For example, a user might inadvertently shut down his device
or put an application currently executing a task into a sleeping state. Since
the multi-tasking capabilities of smart mobile devices are not yet up to the
standards known from other systems, the application will have to respond in
a safe way to, for example, an unexpected shutdown of the device. Note that
this is particularly important in the context of Challenge 1 (i.e., data
consistency) as well.
Challenge 3: Mobile Context
The mobile space opens up new opportunities regarding process execution.
Specifically, the location and time context may be utilized to foster process
and task support. In general, the location of smart mobile devices can be
determined within a reasonable margin of error using sensors like GPS. In
turn, this data provides a contextual factor of the process, which may be
utilized to restrict task execution to those devices located near the work
place this task shall be performed. Note that this might decrease the
distance to be covered by an actor in the process when performing individual
tasks.
Challenge 4: Sensors
Nowadays, most smart mobile devices are equipped with a plethora of
sensors. This includes sensors to locate a device using a satellite positioning
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system, cameras and microphone. Other sensors available are heartbeat
sensors, thermometers, or blood sugar sensors. Smart mobile devices
equipped with them may be used to provide physical data during process
and task execution, mitigating the need to capture this data by using
specialized devices or – even worse – requiring manual user input.
The particularities of smart mobile devices should be considered as specific
challenges when targeting at a mobile task execution. However, the sensors
of these mobile devices are also able to provide valuable parameters in the
context of process execution. When dealing with real-world scenarios, several
of these parameters could be used as an integral part of the processes,
proofing their validity and importance in a business process context. When
investigating the real-world scenarios, we identified a large number of
parameters that can be related to the four categories mentioned above. To
give an impression, for each considered real-world scenario,
Table 1: parameter validty in real-world scenarios shows parameters of the
four categories that turned out to be relevant.
Scenario Data Consistency Shutd
owns
Location Camera
HEALTHCARE
(WARD ROUNDS)
✓ ✓ ✓ ✓
AVIATION
(AIRLINE CATERING)
✓ ✓ ✓ ✓
LOGISTICS
(WAREHOUSING)
✓ ✓ ✓ ✓
AUTOMOTIVE
(PRODUCTION)
✓ ✗ ✓ ✓
PSYCHOLOGY
(QUESTIONNAIRES)
✓ ✗ ✓ ✗
TABLE 1: PARAMETER VALIDTY IN REAL-WORLD SCENARIOS
Mobile Task Execution and Configuration Support
BPM not only deals with the modeling, configuration and execution of
business processes, but also with their monitoring and evolution. The
presented work deals with task configuration support, specifically the
implementation of this support on smart mobile devices. We have designed
an example of a process demonstrating how smart mobile devices and
features enabling mobile configuration support may serve to enhance
scenarios.
We consider a scenario involving a nursing home and a patient with
dementia. A nurse performs her scheduled rounds to check the status of all
patients she is responsible for. To assist her in accomplishing this
procedure, she is equipped with a smart mobile device that is linked with a
BPM system. Thereby, the procedure around a single ward round shall be
implemented as a process in the BPM system and its corresponding tasks be
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executed as mobile tasks on her smart mobile device. The simplified scenario
referring to a particular patient is depicted in Figure 2 (in terms of the BPMN
notation). The latter also shows how data is exchanged between the smart
mobile devices of the actors involved in the process.
After finishing the care of a patient, the nurse shall visit the patient
scheduled next, as displayed on her smart mobile device. As part of the
process, the device can display the tasks that need to be performed for a
particular patient. In particular, we consider patients suffering from
dementia and – most of them – from diabetes as well. To reflect the latter
one of the tasks involves a routine check of the patient’s blood sugar level.
When the nurse enters the room of a specific patient, an automated check is
performed as to the whereabouts of the patient. To facilitate this check, every
patient has been equipped with a small tracking device that permits indoor
and outdoor localization, thus decreasing the possibility of a disoriented
patient that might wonder off and get lost outside the area of the nursing
home.
Assume that the smart mobile device of the nurse queries the patient’s
tracking device and determines that the patient is currently not available in
her room. The task execution engine on the mobile phone is further able to
indicate to the nurse that the respective patient is currently in the cafeteria.
The nurse may now choose to send a notification to the patient’s device,
informing her that she is needed in her room. The nurse declines the request
and decides to walk to the cafeteria and take the patient with her back to the
room in order to be able to administer the required tests.
The nurse administers a blood sugar test on the patient using a
computerized measurement strip. The blood sugar level is then automatically
stored in the electronic patient record. At the same time, the blood sugar
value is compared with thresholds set by a doctor. Assume that the system
discovers that the levels are elevated, but not high enough to warrant
immediate action. As defined by the parameters of the process, the doctor
will be notified about the blood sugar levels and an appointment be
automatically added to calendar of the doctor.
The nurse finishes the test and proceeds with the next patient (i.e., the next
process will be started and executed). Without any interaction of the nurse,
an event was created by the device after the test strip had been used as the
number of test strips has reached a level below the threshold set. As part of
this event, an automatic request is sent to the person managing the devices
that the supply of test strips for this device needs to be replenished.
A logistics expert is notified about the situation and proceeds with the
nursing home’s storage facility to gather the required test strips (i.e., the
device may be restocked at the next opportunity). He is also equipped with a
smart mobile device that includes augmented reality features. Once notified
about the task, the device determines the location of its owner and can, if
requested, show the location of these test strips in the warehouse and
navigate him to the aisle in question.
Once the logistics expert reaches the location of the test strips, the camera of
his smart mobile device is able to scan the barcode in the field of view of the
camera. Once the camera recognizes the barcode of the needed package of
test strips, the package is marked on the augmented reality view. This
feature serves to minimize the possibility that a wrong set of test strips is
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taken from the warehouse. The expert takes the visually marked package of
test strips and delivers them to the desk, where the blood sugar testing
device can be restocked in the near future.
In summary, the scenario makes use of the following parameters:
Mobile Context: Location of patient, nurse, logistics expert
Mobile Context: Date and Time
Mobile Context & Process Related: Device crashes
Sensors: Blood sugar level
Sensors: Camera (product barcodes)
User Behavior: Instantaneous shutdowns
FIGURE 2: NURSING HOME SCENARIO
REGARDING THE PARAMETERS AND CHALLENGES ADVOCATED IN THE
PREVIOUS SECTION,
Figure 2: Nursing home Scenario shows specific parameters (numbered
rectangles) that may be applied to this scenario. We briefly summarize their
significance as follows:
(1) (Mobile Context) The location of the nurse and the patient needs to be
determined as required by the possible situations described for the
scenario (e.g., patient is not in her room).
(2) (Sensors) Blood sugar levels are determined using a smart mobile
device.
(3) (Mobile Context & Process Related) During the transfer of the blood
sugar levels, the smart mobile device of the nurse might crash.
Regarding this scenario, data consistency is crucial as incomplete or
erroneous data might have severe consequences on the patient’s
health. Once the device is safely restarted, therefore, the data must
be completely transmitted to the BPM backend.
Nurse
Doctor Logistics
Specialist
Visit patient
Send
location
request to
patient
Measure
blood sugar
level
Leave
patient
Receive high
blood sugar
level
Locate test
strips
Request new
test strips
Replenish
test strip
supply
Create
appointment
Location
Blood sugar
level
Camera
Device crash
Shutdown
1
2
3
4
5
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(4) (Sensors) The logistics actor uses the camera of a smart mobile device
to correctly identify the correct test strips.
(5) (User Behavior) Assume that, when using the smart mobile device,
the doctor receives the required data to make an appointment, the
doctor shuts down his device. In such a case, exception handling
techniques must be applied to determine whether the appointment
has been created correctly once the device becomes available again,
or another doctor must be notified about the situation.
As the scenario illustrates, the use of smart mobile devices provides new
possibilities in terms of available process parameters. This scenario only
serves to illuminate the task of executing processes on smart mobile devices.
In general, in many business processes scenarios various difficulties and
exceptions must be tackled at the time a process leaves the planning stage
and is implemented for real-world execution. In this context, particularly for
mobile scenarios, the sheer number of parameters and execution anomalies
are often not foreseeable during design time. By equipping users with the
possibility to view and modify their specific tasks on the smart mobile
devices, the quality of processes can be significantly improved. In this
context, the ability to modify, remove or add parameters becomes an issue.
In particular, we propose that the parameterization may improve overall
process execution in case the parameter operations (i.e., add, remove, and
modify) may be applied at the place a task will be executed. Such a
configuration scenario constitutes mobile task configuration on the Go.
As we envision mobile task configuration on the Go, a scenario comprising
the following three steps can be realized:
(1) A user is working on a task and wants to modify one of the
parameters since she discovered settings which are not properly
configured. Therefore, she uses her smart mobile device to identify
the task corresponding to the process she is working on, using the
current location and known parameters. Based on this information, a
model is created which provides information about the process and
the mobile task the user is working on as well as configured
parameters.
(2) Using the model, the user may modify, remove or add parameters.
(3) Once the parameters are changed, the user may view the modified
task in the context of the process. Following this, the parameters can
be discarded or saved. In the letter case, all new process instances
based on the modifications will be executed.
3D Mobile Task Configuration Support
Our implementation of this vision is based on a 3D augmented reality engine
that can display processes and parameters and that is able to offer the
ability to modify these processes directly on the smart mobile device.
Currently, we provide two ways to determine the tasks to be modified. First,
for tasks which are currently executed, we use the parameters and the
current location of a user. Second, for tasks which are not executed, the
smart mobile device uses a marker approach (1) to recognize a task. At the
time, a task is known to the smart mobile device for mobile configuration, it
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can use its internal or server side storage to fetch the data for this task and
display this task on the mobile screen (2). Using gestures, the user is now
able to view the task and its process based on the 3D augmented reality
engine (3).
This section focuses on our implementation of a 3D augmented reality
engine that can be used to display a process using a marker or running
process information to determine the process that needs to be displayed. We
provide an in depth explanation of the architecture and the underlying
computational model. We show how our engine can display processes and
tasks using a system of markers as task identifiers. Additionally, we present
the way users can modify the representation of the model, including the
possibilities of zooming into specific sections of the model. Furthermore, the
task configuration view is shown. Finally, we discuss what aspects we
revealed when using our approach in practice.
Architecture
The architecture of the prototype is shown in Figure 3. It can be divided into
three parts: (1) an augmented reality framework, (2) a graphics engine and a
(3) process parser. The AR-component allows us to recognize different
markers (e.g., QR-Codes) using the smart mobile device’s camera system. We
restrict our explanations to this variant of task detection due to the lack of
space. As the next step, after the marker is recognized, a process is loaded,
parsed and computed. After this procedure, it can be visualized by the
graphical component. Each instance of those components operates in its
own thread (one for the AR-component, one for every parsed process, and
one for the renderer). In practice, this approach became necessary to deal
with user demands regarding the interaction response quality. Usually, after
the process is loaded and parsed, only up to two threads are running.
Consequently, there are still enough capabilities for fluid interactions,
considering that many contemporary smart mobile devices make use of a
quad core CPU architecture.
FIGURE 3: ARCHITECTURAL MODEL
Computational Model
Before a user may interact with a graphical representation of a process, it is
necessary to create separate objects, which - once linked together - will form
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a 3D representation of a process graph. To simplify this task, objects can be
produced using a variety of 3D modeling tools. The current prototype is able
to load the exported *.OBJ model format (cf. Figure 4), which stores a
model's geometries and its materials in two plaintext files along with the
needed textures. Those files are loaded in the prototype's own parser and
represent single nodes of a process graph. The second input information are
XML files that contain the BPM data (we currently read ADEPT [2] process
models), comprised of nodes with several attributes and edges controlling the
flow. The parser for such graphs creates single objects out of the BPM's
components, fills them with their attributes, computes them properly to
minimize crossovers in edges and notifies the renderer that new graph data
exists. Once the renderer gets informed, the loading of 3D models that
correspond to the process graph's nodes is initialized and edges between
them are established using lines. Once a previously linked marker is visible
to the camera, the process graph can be drawn onto the screen. Thereby, a
mapping between graph data and its visual representation exists. This
mapping is used to get additional information about visible objects, e.g., on
touch events, or vice versa.
FIGURE 4: COMPUTATIONAL MODEL
Process Representation
The visual representation of the graph consists of the previously loaded 3D
models, which represent the nodes of the graph (cf. Figure 5). Each node has
its own visual counterpart, thus every node type has a different visual
representation. At the moment, four types are used: start/end, conditional
join/split, parallel join/split and plain tasks. Additionally, a second model in
front of nodes contains the name of the node using a bitmap texture. All
nodes are connected by edges according to the edge type.
All nodes and edges are placed on the plane, spanned by the x- and y-axis.
Only backwards directed edge types are placed behind a node's plane. This
allows us to provide the user with the complete graph without overlapping
edges on the first plane, and limiting the possible overlaps on the second
one.
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FIGURE 5: EXAMPLE PROCESS WITH CORRESPONDING MARKER
Interaction Dimensions
As we lifted the two dimensional graphical process representation to the
third dimension, we gained additional interaction possibilities (cf. Figure 6).
The third dimension allows us to move a process, like it's two dimensional
counterpart on two axes and zoom into it, which corresponds with a
translation on the third axis. Additionally, it is possible to rotate the
complete process visualization along those three axes, which provides a
useful feature (reported by users) to interact with process graphs. In
particular, this feature provides a better overview on the backwards directed
edge types on the second plane (and behind the actual graph). Furthermore,
with a single touch, it is possible to extract information about nodes and
present the details for mobile task configuration on the screen.
To provide a better experience during the interaction with those processes,
an option to pause the marker detection was introduced. The user is not
required to keep pointing the device's camera towards the marker, and may
operate with the processes more easily.
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FIGURE 6: USER INTERFACE TO MODIFY PROCESS REPRESENTATION
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Task and Process Operations
FIGURE 7: TASK VIEW WITH CORRESPONDING MARKER
The framework includes basic procedures to extract information from tasks
that are mapped with its visually represented 3D nodes. At the moment, this
information consists of attributes and parameters stored for the mobile tasks
and being extracted by the parsing procedure.
Lessons Learned
The currently implemented 3D model has a few limits. Models, seen as single
objects, should have a suitable size. If models are exported with wrong
scales, the renderer will use those without rescaling, resulting in a
misleading or miscalled representation. Additionally, the parser for the *.OBJ
file format does not support the full feature set offered by the exported
models. This means that single geometries of those models should have one
material with at least a diffuse map as texture. Furthermore, single texture
maps should be stored in a format supported by android. Only the PNG
format was used during testing, but JPEG and many other formats should
work as well, depending on the Android version and its support for the
format in question.
Looking at a complete graphical screen consisting of multiple models, the
GPU usage has to stay within limits. The texture sizes and triangle counts of
geometries should stay within a reasonable limit to maintain a fluid
experience during usage.
Mobile Task Configuration Using 3D Models
The architecture (cf. Figure 3) and computational model (cf. Figure 4) we
presented for realizing 3D models for process graphs on smart mobile
devices have shown their feasibility in practice. In particular, we were able to
meet the requirements of a fluid experience for users working with our
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approach to configure mobile tasks. Furthermore, the overall approach has
revealed the following other aspects:
3D models are a proper technique to show process graphs on smart
mobile devices. In particular, domain experts have reported that
using such models combined with the interaction possibilities meet
their requirements to configure mobile process tasks as well as
improve the overall process execution quality.
Our overall approach has shown its flexibility. We started with
process models based on ADEPT [3]. Currently, we integrate process
models based on BPMN [11] and learned that this could be easily
done. To be flexible in the context of BPM is a major challenge [1, 10]
which must be properly addressed.
The integration of a marker component has been reported as very
useful. Assume that within a warehouse, a warehouseman wants to
check whether a shelf in his warehouse is related to a task (e.g., to
manage goods for this shelf). Furthermore, he wants to check the
parameters of the corresponding mobile task of the process that
manages the goods of the warehouse. For this purpose, the shelf has
a QR-code which will be recognized by a smart mobile device that
shows based on the information of the QR-code the corresponding
mobile task and its parameters.
However, in practice we learned that many more challenges have to be
tackled. For example, at the moment, we only support the Android mobile
operating system. Other mobile operating systems must be addresses as
well. In addition, the different ways of implementing mobile applications [14]
must be also considered carefully in this context to meet the requirement of
integrating sophisticated mobile task execution as well as configuration with
existing BPM environments.
Conclusion
This chapter introduced an approach for configuring mobile task of business
processes. The presented approach using 3D models allows for a completely
new support of users to configure mobile tasks. Providing a sophisticated
mobile task configuration support is challenging. We have shown that such a
support is demanded by many BPM scenarios. The particular challenges in
this context stem from the four presented categories, which we identified in
the context of the investigated real-world scenarios. We have shown that
particularly for mobile scenarios, a sheer number of parameters and
execution anomalies are required to capture the mobile context properly.
Furthermore, we have shown that in a mobile context these parameters are
often not foreseeable at build time. Consequently, new techniques must be
provided to configure these parameters on the Go. Moreover, we have learned
that user acceptance is crucial in the context of mobile task configuration
support. Although we have demonstrated the feasibility of our approach,
many other challenges need to be tackled as well. We consider mobile task
execution and configuration as shown in this chapter as one of the most
challenging as well as most promising aspects of BPM to Go. Finally, our
future work will consider distributed and collaborative processes in the
context of BPM to Go as well.
BPM TO GO
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