=Paper=
{{Paper
|id=None
|storemode=property
|title=Making context explicit towards decision support for a flexible scientific workflow system
|pdfUrl=https://ceur-ws.org/Vol-696/paper1.pdf
|volume=Vol-696
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==Making context explicit towards decision support for a flexible scientific workflow system==
https://ceur-ws.org/Vol-696/paper1.pdf
CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
Making context explicit towards decision support
for a flexible scientific workflow system
Xiaoliang Fan (1, 2, xiaoliang.fan@gmail.com), Patrick Brézillon (1, patrick.brezillon@lip6.fr),
Ruisheng Zhang (2, zhangrs@lzu.edu.cn) and Lian Li (2, lil@lzu.edu.cn)
(1) LIP6, Box 169, Université Pierre et Marie Curie
4 Place Jussieu, Paris 75005 France
(2) School of Information Science and Engineering, Lanzhou University
222 South Tianshui Road, Lanzhou 730000 P.R.China
Abstract methods, change parameters, re-design the experiment) is
measured by a decision node in workflow design
Scientific workflow (SWF) system is a specific workflow accompanying with a numerical value (e.g. IF the variable
management system applied to science arena. For years, is greater than 5, THEN execute the activity A, ELSE
SWF systems are widely applied to many applications, execute activity B; WAIT for 2 minutes to execute activity
namely in physics, climate modeling, drug discovery C). However, scientific discovery is by nature a
process, etc. However, current SWF systems face the knowledge-intensive one (van der Aalst et al., 2005) that
challenge to adapt the flexibility and lack of decision scientists' decisions rely not only on data and information
support for scientist. We believe the major reason for the
available, but also on a learning process in which user’s
failure is due to do not make context explicit. We propose a
solution to introduce contextual graphs (CxG) in the four
preference, knowledge, and situation are captured to adapt
phases of the SWF lifecycle, each of which is expressed in the human-centered processes.
a standard format, including a case study in virtual Such challenges mentioned above become an obstacle
screening. Contextual graph allows to model scientists’ when scientists are making adaptive decisions to deliver
decision making processes as a uniform representation of new outcomes with fresh data and its context (Fan et al.,
knowledge, reasoning, and of contexts, so that scientists 2010). Brézillon and Pomerol (1999) define context as
are closely involved in each phase of SWF lifecycle to “what constrains the resolution of a problem without
maximize the decision support. Finally, we conclude and explicit intervention in it”. We believe that the main
highlight that using CxG is the key human-centered reason for this failure is largely due to the lack of context
process for SWF systems. management in an explicit way. In this paper we propose
four ways of making context explicit in scientific
Introduction workflow, by introducing contextual graph to in the four
Scientific workflow system liberates the computational phases of scientific workflow lifecycle. Representing and
scientists from burden of data-centric operations to making “context” explicit in SWF system would provide
concentration on their scientific problems (Altintas et al., sustainable decision supports for scientists by formalizing
2004; Goble et al., 2007). However, it is not yet satisfied, their research, strategies, and customization information,
considering that computational science (Roache, 1998) is where elements of knowledge, reasoning and contexts are
always reproduced in a flexible and exploratory pattern. represented in a uniform way.
Consider virtual screening (Chen & Shoichet, 2009) for Hereafter, the paper is organized in the following way.
example, the choice of one software over others depends Section 2 introduces the four phases of the scientific
much on contextual information that are highly specific of workflow lifecycle. Section 3 investigates the possibility
the situation at hand, and where, when, how and by whom of integrating contextual graphs to the four phases of
the scientific workflow is executed. Thus a strong and scientific workflow lifecycle through a case study in
sustainable decision support is urged for scientists to virtual screening. Section 4 discusses previous works on
transfer hypotheses to discovery. workflow flexibility in order to point out what is reusable
Workflow flexibility becomes a critical challenge to deal while problems remain to support decision-makings in a
with intermittently available resources, execution failures, flexible scientific workflow system. The general
and to support human-centric decision-makings. However, conclusion and future work in Section 5 closes the paper.
identifying how scientists make decisions to address
workflow flexibility is a very complicated issue. The ways Scientific Workflow Lifecycle
of scientists make their decision vary from one another: (1) Scientific workflow lifecycle is coming from workflow
based on their past experience considering successful or lifecycle (van de Aalst & van Dongen, 2003; Gil et al.,
failed ones; (2) inherited from the best practices within 2007; Deelman & Chervenak, 2008). It normally starts
science communities; (3) from the observed intermediate from the scientific hypotheses (Beaulah et al., 2008;
results; and (4) just follow their own distinguished way. Tadmor & Tidor, 2005; Claus & Johnson, 2008) to reach
Various approaches (Zhang et al., 2008; Courtney, 2001; a specific experimental goal, which includes four phases
Tabak et al., 1985) are proposed to get user involved to (see Figure 1):
describe their decision making processes. Normally in
such applications, a decision making (e.g., choose
3
CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
iterative manner. Furthermore, it must then be
facilitated to publish the workflow on a
repository, so that SWF could be archived for
re-use later.
Figure 1 shows the relationship among each phases
of scientific workflow lifecycle: hypotheses arrive as
keywords to search pre-existing scientific workflow in
SWF repository; then scientist begin to design the
workflow model and maintain the mapping from an
abstract workflow to a concrete one; workflow execution
phase enacts the workflow model on available resources
according to data and control dependencies; if a change is
encountered, there is an iterative process to re-design the
workflow model as well as re-execute the workflow
instance; if executed successful, scientist will publish the
workflow in the SWF repository for the sake of
Figure 1: SWF lifecycle
reproduction in the research communities.
Current studies (van de Aalst & van Dongen, 2003;
Workflow Searching: before initiating a brand
Deelman & Chervenak, 2008) on SWF lifecycle
new workflow designing, scientists get used to
generally result in the weakness to manage the workflow
firstly consult a public SWF repository for
changes and exceptions. We believe that the major failure
searching previously published workflows
is due to do not make context explicit in the SWF systems.
(Wroe et al., 2007). Once found, it would be
easy to reproduce the pre-existing workflow to
constitute a new one. Workflow searching Make Context Explicit in SWF Lifecycle
results of sharing SWF considered with its Representing and making context explicit in SWF system
context of use. The more shared SWFs are taken is a challenge that could promote a SWF system more
place in the SWF repository, the more accurate flexible and enhance its intelligence to facilitate effective
the searching result would be. decision-makings. In this section, we discuss managing
Workflow Designing is then initiated for contexts explicit throughout the four phases of the SWF
constructing a workflow model (Ludascher et al., lifecycle, each of which is described using a standard
2009). An abstract workflow model will firstly format including: motivation, realization approach,
be designed, in which scientific tasks and their example, and discussion.
execution orders, as well as data and its The example is represented in the Contextual graphs
dependencies will be described. Secondly, the formalism (Brézillon, 2005) through a case study entitled
phase involves the mapping from abstract “Virtual screening research on avian influenza H5N1
workflow to concrete/executable workflow virus”, which aims to find dozens of drug candidates for
where the required resources are selected. By H5N1 virus (He et al., 2008), by docking 7.7 million
mapping the workflow instance onto the small molecules separately on H5N1 protein (Chen &
available execution resources, an executable Shoichet, 2009). Figure 2 shows a docking example,
workflow is created for the next phase. which binds a molecule (ZINC12050767) to a virus
Workflow Execution is the enactment of protein (H5N1 PAC Polymerase, known as Bird flu)
executable workflow by a workflow engine through the Dock 6.2 software. Virtual screening could be
(Deelman & Chervenak, 2008), in which input considered as millions of docking procedures on the PAC
data is consumed and output data is produced protein.
(Tan et al., 2010). Workflow engine follows the
order of tasks and their dependencies defined in
the workflow model. It is common to re-execute
the workflow iteratively, considering the
evolutionary changes of workflow model (e.g.,
in workflow design, adding or skipping tasks,
and altering task dependencies) or momentary
changes of a running workflow instance (e.g.,
making local decisions in response to a special
situation, alter decision after analysing observed
intermediate result, reporting exceptional cases). Figure 2: Docking example
Workflow Publishing is a post-execution phase
for scientists to interpret workflow results (Tan The application is not only a time-consuming workflow
et al., 2010; Ludascher et al., 2009) and to application in which intensive computing is expected to
publish the SWF in its context of use (Wroe et be performed by docking software, but also a very flexible
al., 2007; Deelman & Gil, 2006). Depending on one that there is no unique solution for each computing
the workflow outcomes and analysis results, the because they vary from each other on selecting docking
original hypotheses or experimental goals may software. For example, scientists should identify the
be revised or refined, giving rise to another context in which the experiment is organized as a
round of workflow design/execution in an scientific workflow. According to the current focus and
4
CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
context, they link a specific resource (e.g., software, Example: In Figure 4 (Left), CE1 is a contextual element
database, and instrument) with the workflow to realize a (blue circle with number 1). The instantiation of the CE1
specific task. The concept of human-centered process is (Is the protein rigid or flexible?) leads to the generation
particularly relevant in such domains. of two scientific workflow instances in Figure 4 (Right):
Figure 3 provides the definition of the elements in a one is SWF_1 (i.e. value of CE2= “Rigid”), and the other
contextual graph (actions, contextual elements, sub-graphs, is SWF_2 (i.e. value of CE2=“Flexible”). In the
activities and temporal branching). A more complete application, if scientists want to do a rigid virtual
presentation of this formalism and its implementation can screening, “rigid” will become a keyword when
be found in (Brézillon, 2005). performing the searching. Thus, SWF_1 will be selected.
Similarly, SWF_2 is chosen when searching for a
“flexible” screening. As a result, CxGs act as an interface
to make decisions to choose SWF from the SWF
repository.
Discussion: It is normal to expect nothing from the
repository, scientist could move to the next phase to start
workflow design from scratch.
Workflow Designing
Figure 3: Elements in Contextual graph Motivation: During workflow design, a certain degree of
freedom is given to the user to execute a workflow by
Workflow Searching offering multiple alternative execution paths. Classical
Motivation: Before the workflow design, context workflow systems reduce the degree of flexibility by
behaves as an interface to determine which SWF should offering powerful design constructs (e.g., start, if/else,
be chosen from a library of SWFs, or a SWF repository. repeat until, parallel execution, end), in which decision-
In this case, a scientist plays a role as a context provider making is always measured by a decision node
to guide the choice of the right SWF model according to accompanying with a numerical value. However, human
current focus and context at hand, so as to largely match decision is so complex that a numerical decision is less
what the scientific hypotheses indicate. descriptive than a simple question. As a result, we
describe execution paths of workflow in contextual graphs
Realization approach: (CxGs) which model contextualized information (CEs)
Scientist firstly searches a SWF from a SWF and their dependencies. In a contextual graph, the most
repository, using keywords which could best appropriate execution path could be selected from those
describe their hypotheses and are coherent with the encoded during the execution time to address the context
context at hand. at hand.
If the pre-existing SWF is exactly what they want,
the scientist could skip workflow design phase and Realization approach:
just replace with their own parameters for workflow Firstly, it is necessary to know all the current
execution directly. instances of the CEs at the moment of the
Otherwise if it is similar to their needs, slight application of the workflow. An instantiation is the
modifications will be carried out shortly in the value that a contextual element can take for a
workflow design. specific instantiation of the focus at hand.
Then, a group of contextualized information is
generalized as a set of CEs.
CEs are then formalized in a contextual graph by
their dependencies. The contextual graph is ready
for the workflow execution, when a SWF instance
corresponds to a specific execution path under the
instantiation of context. In CxG, the execution path
is a sequence of actions, connected by the
instantiation of the selected contextual elements.
Example: In Figure 5, a scientist designs the workflow of
Context graph: virtual screening on protein PAc protein preparation as a contextual graph with a set of
1: Is the protein rigid or flexible? contextual elements (CE1 and CE4) and their execution
Rigid 2: Activity: perform first rigid screening dependencies. The possible execution paths are controlled
Flexible 3: Activity: perform second flexible screening by the value of each contextual element. For example, the
4: analyze the result instantiation of CE1 (i.e., value of CE1= “Yes”) and CE4
(i.e., value of CE4= “Yes”) leads to the execution path of
Figure 4: (Left) Contextual graph of virtual screening on “1→2→4→11→5→6→9”.
H5N1 protein; (Right) Choosing one SWF from two
SWFs (SWF_1 and SWF_2)
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CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
optimize the protein) is invoked as a new SWF
component. Furthermore, the contextual graph is updated
along with the change of CEs, and it is necessary to record
such update in a knowledge base for the sake of workflow
sharing, which will be discussed in the next section.
Contextual graph: protein preparation (old)
1: Can you find the protein by yourself?
Yes 2: download it from "Protein Data Bank"
No 3: ask for help until you get the protein
4: Do you need to do "protein preparation"?
Yes 11: enter parameters during "protein preparation"
5: Activity: remove unrelated molecules
6: Activity: add hydrogen and charge
9: store the protein prepared in the database Figure 6: Contextual graph: protein preparation (new)
No
Discussion: It would be a risk of incoherence between the
Figure 5: Contextual graph: protein preparation (old) running workflow instance and results. For example,
when you made a decision two minutes ago and the
Discussion: Describing a completely set of all possible contextual graph chooses an execution path for the
execution paths during workflow design might be either workflow. But later, right before the workflow execution,
undesirable or impossible (Schonenberg et al., 2008). For a new context arrives to urge the adaptation of a new
example, a certain number of possible execution paths are contextual graph.
unknown before execution. As a result, late-modelling
(Han et al., 1998) could enable to make sub-model Workflow Publishing
dynamically defined during execution. Motivation: If executed successfully, the scientist then try
to analyse the results generalized by workflow execution.
Workflow Execution Type of result analysis includes: 1) evaluate data quality
Motivation: Scientists frequently re-execute the scientific (e.g., does this result make sense?), 2) examine execution
workflow by adding or ignoring portions of workflow traces and data dependencies (e.g., which results were
realized at design time. Context should support the “tainted” by this input dataset?), 3) debug runs (e.g., why
assembling of SWF components, which must be did this step fail?), or 4) simply analyse performance (e.g.,
recompiled each time when a new context arrives (i.e., a which steps took the longest time?). After the result
contextual element takes a new instance). As a result, a analysis process, it is possible to re-design and re-execute
new execution path, or even a new contextual graph will the workflow iteratively until the new context is addressed.
be inserted or removed when SWF evolves along with its Incremental knowledge acquisition should be proceeded
context. to make contextual graph growing to be more efficient.
Furthermore, one of the motivations what scientists are
Realization approach: counting on SWF is the sharing, reproduction,
Each time a new instantiation of a CE occurs, the transformation, and evolution of the “old” SWF to be a
contextual graph is re-executed, and the SWF is brand “new” one. It is expected to enable sharing of SWFs
recompiled for generating a new SWF instance for according to their contexts of use. In this circumstance,
execution. the context defines the status of the knowledge and also
If the scientist wants to re-design the workflow by maintains the relationship between different kinds of
adding or ignoring portion of SWF, they first stop knowledge.
the current workflow execution.
Then, a new group of contextualized information, Realization approach:
including the information representing the workflow A SWF repository is build up to document
changes, should be generalized as a new set of workflows with their contexts of use.
contextual elements. When workflow is re-executed, the contextual graph
If a CE with the following activities/actions is added is adapted incrementally to trace the workflow
or ignored, a new contextual graph is produced to flexibility. Once a new contextual graph is
address the new focus. generated, add it as a new scenario to SWF
repository.
Example: Figure 6 is inherited from Figure 5. During the Conscientious users might partition the workflow
execution phase, the scientist finds something wrong with into coherent fragments and publish them.
the intermediate result, because he doesn't take into
account whether the protein is flexible or rigid. So he Example: Once a contextual element is modified, a new
decides to stop the current execution and re-design the CxG is created to address the new focus and its context.
experiment. As a result, a new contextual element CE7 (Is Drawn from Figure 6, Figure 7 shows a new contextual
it a rigid or flexible screening?) is added. When the value graph to be added in a SWF repository for future sharing
of CE7 is “flexible screening”, Activity13 (Activity: with other scientists.
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CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
Context has been considered as a key element to support
decision making in human centered processes for a long
time (Brézillon, 2003; Brézillon, 2010). To address a
coherent formalism of context, Sowa (1984) proposes
conceptual graphs with their mechanisms of aggregation
and expansion. Then, Sowa (2000) introduces a way to
manage the context in conceptual graphs. Brézillon (2005)
presents a simpler formalism of Contextual Graphs (CxGs)
Contextual graph: protein preparation (new)
for representing context. Compared with other approaches,
1: Can you find the protein by yourself?
Yes 2: Download it from "Protein Data Bank"
CxGs formalism is good at describing decision making in
which context influences the line of reasoning.
No 3: Ask for help until you get the protein
In the implementation level, a number of applications
4: Do you need to do "protein preparation"?
exist for preparing formal representation of context.
Yes 11: Enter parameters during "protein preparation"
McCarthy (1993) formalizes contexts as formal objects,
5: Activity: remove unrelated molecules
and the basic relation is ist(c,p). It asserts that the
6: Activity: add hydrogen and charge
7: Is it a rigid or flexible screening?
proposition p is true in the context c, where c is meant to
capture all that is not explicit in p that is required to make
Rigid
p a meaningful statement representing what it is intended
Flexible 13: Activity: optimize the protein
to state. Formulas ist(c,p) are always asserted within a
9: store the protein prepared in the database
context, i.e., something like ist(c', ist(c,p)): c': ist (c, p).
No
Sharma (1995) gives a list of desirable properties for
Figure 7: Contextual graph: protein preparation (new) contexts in a formal language and distinguishes four
approaches for formalizing contexts: (1) incrementing
arity; (2) variation on implication; (3) modal operator
Discussion: Encourage sharing of scientific workflow
forms; and (4) syntactic treatment. Based on McCarthy's
with its context, would make it as a complementary of
work on context logic, Farquhar et al. (1995) present an
paper-based publications. In such a case, scientific
approach to integrating disparate heterogeneous
workflow would be archived along with paper-based
publications. However, the quality of sharing data and information sources.
In Table 1, we compare various approaches to model
workflow becomes a new question.
decision making in workflow, as implementation of
“Exclusive Choice workflow pattern” (van de Aalst &
Summary
Hofstede, 2003).
Contextual graphs are a formalism of representation
allowing the description of decision making in which Table 1: Comparison of various implementations of
context influences the line of reasoning (e.g. choice of a “Exclusive Choice workflow pattern”
method for accomplishing a task). The advantage of
contextual graphs relies on that: (i) CxGs provide
naturally learning and explanation capabilities in the Approach Decision Decision Decision
system; and (ii) CxGs allow a learning process for Element Value Type
integrating new situations by assimilation and BPEL , Condition Numerical
accommodation. In short, the notion of context is made (Zhang et al., value
explicit during the four phases of scientific workflow 2008)
lifecycle by contextual graphs. Contextual Graphs
formalism has been already used in different domains CxG Contextual Value of Any value
such as medicine, incident management on a subway line, (Brézillon, 2005) Element CE
road sign interpretation by a driver, computer security, UML Decision Condition Numerical
psychology, cognitive ergonomics, etc. (Courtney, 2001) Node value
Related Works Petri-net Exclusive Arc Numerical
(Tabak et al., choice expression value
Various approaches, such as BPEL (Zhang et al., 2008),
1985)
UML (Courtney, 2001), Petri-net (Tabak et al., 1985), are
proposed to address the issue of workflow flexibility by
getting user involved in representing decision-making. By comparison, Contextual Graphs plays an equivalent
Applications (Yu et al., 2005; Hey et al., 2009) have role to other approaches for representing decision making.
proven the significance of current systems to handle Furthermore, the advantage of contextual graphs embraces:
numerical decision-making as control-flow functions, (1) multiple representations of decision making, not only
such as “wait 30 second, and then proceed the next task”, with a numerical value, but also with any kind of answers
“if the value is greater than 5 then execute the task_A, else to questions to get scientists involved in a local decision-
execute the task_B”. However, it becomes an obstacle to making process; (2) it is directly readable (e.g. generally
manage the common but important decisions, such as “are something as “If the contextual element C has the value
you satisfied with the result?” and “do you need to do the V1, thus use method M1, and with the value V2 use
protein preparation again”, which is more comprehensive method M2”); and (3) it is very easy to have an
for scientists. incremental growth of a contextual graph by addition of
contextual elements and branches for representing
7
CEUR Proceedings 4th Workshop HCP Human Centered Processes, February 10-11, 2011
practices developed by users and not yet known by the Context: 5th International and Interdisciplinary
system. Conference (pp. 55-68 ). Berlin :Springer Verlag.
Brézillon, P. and Pomerol, J.-Ch. (2010). Framing
Conclusion decision making at two levels. In Respicio, A., F. Adam,
The human-centered processes must be considered at a G. Phillips-Wren, C. Teixeira & J. Telhada (Eds.),
global level to deal with the user, the task at hand, and the Bridging the Socio-Technica Gap in Decision Support
context in which the task is accomplished. Take a flexible Systems- Challenges for the next Decade. Amsterdam:
scientific workflow for example, scientists could not IOS Press.
handle the transferring from hypotheses to discovery in Chen, Y., & Shoichet, BK. (2009). Molecular docking and
the SWF system without taking into account the context. ligand specificity in fragment-based inhibitor discovery,
We propose a solution to introduce contextual graphs in
Nature Chemical Biology, 5 (5), 358-364.
the four phases of SWF lifecycle, each of which is Claus, B., and Johnson, S. (2008). Grid computing in
expressed in a standard format, including a concrete large pharmaceutical molecular modeling, Drug
example in the area of virtual screening. In our application discovery today, 13(13-14), 578-583.
on virtual screening, we use contextual graphs to model Courtney, J.F. (2001). Decision making and knowledge
the decision making processes of scientists as a uniform management in inquiring organizations: toward a new
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This work is supported by grants from National Natural Farquhar, A, Dappert, J, Fikes, R and Pratt, W. (1995).
Science Foundation of China (90912003, 60773108, Integrating information sources using context logic
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