=Paper=
{{Paper
|id=None
|storemode=property
|title=Workflow Support for Mobile Data Collection
|pdfUrl=https://ceur-ws.org/Vol-731/16.pdf
|volume=Vol-731
|dblpUrl=https://dblp.org/rec/conf/caise/Wakholi11
}}
==Workflow Support for Mobile Data Collection==
https://ceur-ws.org/Vol-731/16.pdf
Workflow Support for Mobile Data Collection
PHD Research Progress for CAISE 11 Doctoral Consortium
Peter K. Wakholi
University of Bergen, Norway.
peterokhisa@gmail.com,
www.infomedia.uib.no
Supervisor: Assoc Prof: Weiqin Chen
Abstract. Mobile devices have become popular for electronic data collection,
management and dissemination in low income countries. Many organizations are
considering the use of electronic devices rather than paper-based routines for
data collection. In data collection, time dependent and cumulative data, can be
regarded as forming a workflow process. Paper-based routines support this work-
flow process and the transition to a paperless environment poses some workflow
challenges that if not addressed could lead to failure to effectively use mobile
devices and to meet process-related requirements. The research seeks to explore
how challenges related to the use of workflows in mobile, disconnected and dis-
tributed environments can be addressed. The research uses an action research
approach to propose solutions based on case studies related to Mobile Health
(MHealth) and Clinical Trials. This paper presents the current status of research
by discussing ideas, methods and frameworks for workflows that provide the flex-
ibility and functionality modeled on paper-based routines.
1 Introduction
In organizations, the daily operation is governed by a set of cooperative business pro-
cesses, in which interactions with humans and information systems are involved. Work-
flow systems aim to automate a business processes in whole or part, during which doc-
uments, information and tasks are passed from one participant to another for action
according to preset procedural rules [1]. Workflow management provides the ability
to improve the efficiency of an organization by streamlining and automating coordi-
nated work activities over distributed environments. Organisations using mobile data
collection (MDC) aim to achieve this goal by replacing paper-based routines with mo-
bile devices. This research is based on MDC in clinical trials, which provides a good
use-case for replacing paper-based routines with mobile devices.
A Clinical Trial can be understood as a workflow process. The workflows are de-
fined based on study protocols, which specify the duration and structure of the study
and the standard guidelines which must be followed by all participants [2]. The data
collection process is most difficult as it involves field studies which could be in sin-
gle or multiple sites, often in remote rural environments. One of the core documents
in clinical trial for data collection is the Case Report Form (CRF). The CRF is a form
where the investigator enters all patients’ clinical and non-clinical data related to the
trial. There are three types of data: non-time dependent, time dependent and cumulative
data [3]. Non-time dependent data is the data collected at a snapshot in time. Such data
include subject demographics and medical history. Time dependent data is data col-
lected repeatedly over time through multiple visits. Cumulative data is data collected
over time but not linked to a specific visit.
If workflows using mobile-based routines are to replace paper-based routines, some
challenges need to be overcome. First, MDC in mobile/wireless computing environment
posts new challenges of the such as disconnections, slow connection links, and lim-
ited computing power which must be addressed when designing a mobile application.
A workflow solution for MDC should take into consideration these characteristics and
limitations by employing new computing paradigms. Second, paper-based routines pro-
vide the flexibility to work in remote and disconnected environments. Work conducted
in the field is often done on paper and filed at the end of a specified time. The workflow
system therefore needs to allow for this flexibility by enabling execution of multiple
tasks before connection to the server is required. This is a shift from the traditional
workflow systems provide for client-server architecture that requires synchronisation
before the next task is executed and in some cases web-based-distributed architecture
which require constant connection links [4].
This research focuses on the development of mobile systems for EDC by addressing
workflow-related problems in their use for clinical trials and mhealth projects. The ac-
tion research approach is used to develop appropriate methods, models and frameworks
for deployment and utilization of Workflow Management System (WFMS) for mobile
data collection. The research will be based on the OMEVAC (Open Mobile Electronic
Vaccine Trials) project [5] - a new mobile platform for conducting clinical trials, funded
by the Norwegian Research Council led by the Centre for International Health, Univer-
sity of Bergen. Case studies will be undertaken by testing and evaluating the prototypes
and tools in clinical trials being conducted in rural parts of Africa.
1.1 Research Questions
The main research question is to investigate ”how advances in mobile computing,
process-aware information systems can combine to enable the use of mobile devices for
data collection, management and dissemination in place of paper-based routines used
for field activities” In order to achieve new knowledge in this area, the following sub
questions will be investigated.
1 How can workflow systems be implemented in the highly constrained and resource-
limited mobile environments?
2 How can mobile workflows allow for the flexibility of paper-based routines in field
activities?
3 How can distributed workflows be enacted and synchronized in disconnected envi-
ronments?
1.2 Research Objectives
The research objectives are:-
1 To develop a framework for deployment of generic workflow systems to support
mobile data collection in resource constrained environments.
2 To develop methods for execution of multiple tasks before connection to a server is
required so as to allow for flexibility mirrored on paper based routines.
3 To test the concepts above by implementing an actual workflow based mobile data
collection, management and dissemination system for the OMEVAC project.
4 To demonstrate the value and potential of the mobile data collection in low-income
countries as a replacement for paper-based routines.
2 Current Research and Preliminary Ideas
2.1 Workflow Support for Mobile Data Collection
This research (submitted and accepted by CAISE BPMDS 2011) aims to answer the
first research question identified. It examines the challenges of deploying (WFMS) in
generic data collection tools and proposes a framework for integration. The WFMS is
based on YAWL [6] with a generic MDC tool called openXdata [7], [8]. It describes
the design and implementation of a workflow adapter that acts as a bridge between
the mobile device, data processing applications and workflow engine. Through this im-
plementation, we have been able to provide a distributed architecture that enables the
ordering of tasks linked to mobile devices and web-based applications. In addition, we
provide an example where this framework has been used in an MHealth project of a
vaccination registry that uses mobile devices to collect data on child immunisations.
2.2 Allowing for Offline Behaviour
In order for mobile devices to be used for data collection in disconnected environments,
there is need to allow for offline behaviour. This can be achieved by reducing the number
of connections required in-between tasks by assigning as many tasks as possible for
each connection. Therefore tasks need to be grouped and assigned to a user - assuming
that there are no outside data and resource dependencies for execution of the next task.
In WFMS, multiple execution of tasks have been implemented using worklets [9].
Worklets are subnets that are executed like subroutines of a main program and are cre-
ated at design time. In this research we explore the possibility of dynamically identi-
fying and generating worklets which can then be a assigned as a bundle of tasks to be
executed by the mobile user. In order to achieve this, there is need to (1) determine
which set of tasks to group; (2) ensure that the tasks assigned are performed as required
and (3) synchronise with the server once tasks are completed. In order to determine
which set of tasks to group we propose a framework for multiple task assignment that
can be used to check the process model to create worklets. To ensure that the worklets
are executed on a mobile phone as intended, we propose designing a mobile client to
have a light workflow engine that receives a worklet. Tasks in a worklet may be fully or
partially completed. We therefore propose a protocol that tracks the status of execution
on the mobile client and updates server. This synchronisation protocol would be run
every time a connection is established.
C E
T4 D T5
B H
A T7 T8 I
T1 T2
F G
T6
T3
Fig. 1: Running example of a process model
Fig. 2: unfolded process model
3 Theoretical Basis for Proposed Solutions
3.1 Dynamic Generation of Worklets
We propose to use model checking to automatically test a process model and determine
whether this model meets a given property. Model checking aims to simulate every
execution of the model of the system in order to obtain a labelled reachability graph
describing all its behaviors. The graph is the checked against a set of properties to
determine if the system performs according to a specified property. We use the model
unfolding technique as proposed by [10], [11] to determine the causality, conflicts and
concurrency properties. [10] define these properties as; two nodes x and y of a net,
are causally retated denoted by x ≤ y , if there is a path of arrows from x to y. The
nodes are in conflict, denoted by x 6= y, if there is a place z, different from x and y,
from which one can reach x and y, exiting z by different arrows. Figure 2 provides an
illustration of this approach.
Figure 1 gives a workflow net showing tasks T1 to T6 that are executed using the
basic control flow patterns initially proposed by the WFMC [12] as Sequence, Parallel
Split, Synchronization, Exclusive Choice and Simple Merge. The net presented can be
unfolded as shown in 2. The groups 1,2 and 3 present the possible sets of tasks whose
grouping would not affect the execution of the patterns above. Based on these groups,
the following behavioral observations of the nets can be made:
1 The nodes in group 1 (C,D,E) are in causal relationship and connected by more
than one event.There are no arc emerging or entering this group.
2 The nodes in group 2 and 3 (B,C,D,E,G,H) (B,H,I) are a set on a path that has
exactly one input condition and one output condition. There are no arcs leaving or
entering the group except at node B (the initial node).
From these observations one can conclude that in as long as there exists one input arc
and one output arc into a set of nodes from the initial node to the final node, then
a worklet is possible. Input or output arcs are permisible on the initial or final node.
Therefore given an unfolded model, a worklet can be created from a set of nodes that
are not in contradiction with any node outside the set. In addition, for the set of tasks,
all the conditions must be causal (i.e. system should transition to another state in the
group) and all events must be strongly connected (i.e. There exists a path from first
event to the last event).
Proposition 1. Given an unfolding of a workflow net A = hS; T ; α; β; isi, such that
S = {s0 , s1 , ....., sm } is a set of states and T = {t0 , t1 , ....., tn } is a set of tran-
sitions, a worklet W = hS´; T´; α; β; isi where T´ = {ti , ti+1 , ....ti+m } ⊂ T and
S´= {si , si+1 , ....si+n } ⊂ S is possible if:
1. ∀S´∃ {si ≤ si+1 } where 0 ≤ i ≤ m (Causality)
2. ∀S´∃si such that si ¬ =
6 {S´∈
/ S} (no conflict)
3.2 Event-log based Synchronization
Synchronisation takes place once a connection between a server and mobile client is
established. Any synchronisation solution should aim to ensure appropriate utilisation
mobile phone processing and memory. We therefore propose keeping a log of all the
events on a mobile device and synchronising with the server. For synchronisation,
reachability analysis can be used to compare the workflow net and event log. For a
transition system like a workflow net, we provide a definition of a state called reached
marking as a basis for tracking current work status.
Definition 1 Reached Marking: Let p be a state in P and
α = (a1 , a2 .......am ) ∈ A be a set of executed tasks for the process definition of P . State
p0 is the initial state of the process and pn is the final state. α are reached markings if
ai +1
and only if there exists states s0 , s1 , ....., sm such that p0 = s0 and si −− −→ si+1 for
am +1
all for all 0 ≤ i ≤ m. The next execution task satisfies the relation sm −−−−→ sm+1
and pn 6= sm+1
Figure 3 illustrates an example of a simple workflow for patient visiting a health
clinic in a clinical trial. The reachability graph shows all the possible states of the sys-
tem and transitions that transform these states. Note that the states associated with the
Fig. 3: Synchronisation working example
PatientID Task End-time
1 Triage/ Do get Drugs 12:00
1 Get Drugs 12:10
2 Triage/Do see Dr 12:11
3 Triage/Do see 12:30
1 Get Drugs 12:10
2 See Doctor 13:00
2 Do Lab 13:10
2 CD4 13:15
Table 1: event log
hidden tasks are painted black because in actual sense they do not exist. Assume that
part of this process exists on the server and a client implementing it as a workflet. The
client keeps an event log indicating the tasks executed and the end-time of execution.
Suppose the event log is as illustrated in the table below:
We now propose an algorithm to determine the reached markings algorithm.
Throughout the algorithm a set of vertices, which corresponds to state of work for each
case, denoted by P is maintained. Initially, each node is unmarked. A node Pi is marked
means that a task si has been executed and node Pi in P has been reached. Initially,
only P0 is marked. Using a sequential set of event logs for process A; for each node,
check for the log to determine the next state. While a task exists and corresponds to an
edge from the current state, then mark the next node and remove task from log. The
last marked node corresponds to the current state of work. If the state is not the end
node, the outgoing edge from that state corresponds to next task to be undertaken. If the
node is an end node Pn , then the case is complete. The following algorithm provides
the pseudo code for determining the reached markings:
int count = 0
set α = (a1 , a2 .......am )
node initialN ode = pn
node f inalN ode = pn
reachability (graph P , node pi , edge si )
mark p0
while α 6= ∅ do
for all ai do
for all pi do
if match (si , ai ) then
mark pi
end if
end for
end for
reachedN ode = pi
end while
if reachedN ode 6= pn then
Return pi+1
else
Return pn
end if
4 Conclusion
We have presented in this paper ongoing research about workflow support for mobile
data collection. The use of worklets to enable execution of multiple tasks on a mobile
device reduces the need for many connections thereby allowing for more flexibility in
a disconnected environment. Moreover, it would be neccessary to have these worklets
dynamically generated in order impose less restrictions on the system. We proposed a
model checking technique based on model unfolding and genetic algorithms to gener-
ate these worklets. In order to enable synchronisation between the workflow server and
worklet client, we propose event-log based synchronisation. The use of this approach
greatly reduces the memory usage and application footprint - a key requirement for the
limited resource mobile environment. We thus provide clear progress towards achieving
the objectives of the research as highlighted by the practical ideas and theory presented.
The mobile data collection tool will be enhanced to allow for offline behaviour using
these ideas. It is anticipated that all methods and frameworks proposed will be empiri-
cally tested in MHealth projects to validate the research.
References
1. M. Weske. Business process management: concepts, languages, architectures. Springer-
Verlag New York Inc, 2007.
2. US Food. Drug Administration. Guidance for industry, E6 good clinical practice: consoli-
dated guidance. Federal Register, 10:691–709, 1997.
3. K.K. Moon. Techniques for Designing Case Report Forms in Clinical Trials. ScianNews
Volume, DOI, 2006.
4. R.S.H. Istepanian and C.S. Pattichis. M-health: Emerging mobile health systems. Springer-
Verlag New York Inc, 2006.
5. J. Klungsoyr, T. Tylleskar, B. Macleod, P. Bagyenda, W. Chen, and P. Wakholi. OMEVAC–
Open Mobile Electronic Vaccine Trials, an interdisciplinary project to improve quality of
vaccine trials in low-resource settings. M4D 2008, General Tracks.
6. W.M.P. Van Der Aalst and A.H.M. Ter Hofstede. YAWL: yet another workflow language.
Information Systems, 30(4):245–275, 2005.
7. Y. Anokwa, C. Hartung, A. Lerer, B. DeRenzi, and G. Borriello. A new generation of open
source data collection tools. In Information and Communication Technologies and Develop-
ment (ICTD), 2009 International Conference on, page 493. IEEE, 2010.
8. J. Klungsoyr, P. Wakholi, B. Macleod, A. Escudero-Pascual, and N. Lesh. OpenROSA,
JavaROSA, GloballyMobile–Collaborations around Open Standards for Mobile Applica-
tions. M4D 2008, General Tracks.
9. M. Adams, A. ter Hofstede, D. Edmond, and W. van der Aalst. Worklets: A service-oriented
implementation of dynamic flexibility in workflows. On the Move to Meaningful Internet
Systems 2006: CoopIS, DOA, GADA, and ODBASE, pages 291–308, 2006.
10. J. Esparza and K. Heljanko. Unfoldings: a partial-order approach to model checking.
Springer-Verlag New York Inc, 2008.
11. J. Esparza and K. Heljanko. Implementing LTL model checking with net unfoldings. Model
Checking Software, pages 37–56, 2001.
12. D. Hollingsworth. Workflow Management Coalition-Terminology & Glossary,[online],
1999, verf
”ugbar im World Wide Web: http://www. wfmc. org/standards/docs. TC-
1011 term glossary v3. pdf.