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Chapter XI
Using Grid for Data Sharingto Support Intelligence
in Decision Making
Nik BessisUniversity of Bedfordshire, UK
Tim French
University of Reading, UK
Marina Burakova-Lorgnier
University of Montesquieu Bordeaux IV, France
Wei Huang
University of Bedfordshire, UK
Copyright 2007, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
IntroductIon
This section provides grounding in intelligence
informed decision making technologies, their
application and integration within the modern
organisations.
Scott-Morton rst articulated the concepts of
decision support systems (DSS) in the early 1970s
AbstrAct
This chapter is about conceptualizing the applicability of grid related technologies for supporting in-
telligence in decision-making. It aims to discuss how the open grid service architecturedata, access
integration (OGSA-DAI) can facilitate the discovery of and controlled access to vast data-sets, to assist
intelligence in decision making. Trust is also identied as one of the main challenges for intelligence in
decision-making. On this basis, the implications and challenges of using grid technologies to serve this
purpose are also discussed. To further the explanation of the concepts and practices associated with the
process of intelligence in decision-making using grid technologies, a minicase is employed incorporat-
ing a scenario. That is to say, Synergy Financial Solutions Ltd is presented as the minicase, so as to
provide the reader with a central and continuous point of reference.
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Using Grid for Data Sharing to Support Intelligence in Decision Making
under the general term of management support
systems (MSS). Further works on bounded
rationality from Simon (1977) and classica-
tion types of DSS from Keen and Scott-Morton
(1978), Alter (1980), Holsapple and Whinston
(1996) have led us to understand that DSS is a
set of concepts associated with supporting the
decision making process via the use of appropri-
ate resources. These (resources) may include but
are not limited to users, data, models, software,
and hardware.
Computer-based developments over the last
four decades have facilitated decision makers
with numerous tools to support operational,
tactical and/or strategic level of enquiries withinthe environment of an organization. In relation
to intelligent decisions, the use of expert systems
(ES) and knowledge management systems (KMS)
have evolved over the years by developments in
computational science including data mining,
data visualization, intelligent agents, articial
intelligence, and neural networks. One of the
purposes of these technologies is to provide
managers (decision makers) with a holistic view
hence, the ability to analyze data derived from a
collection of multiple dispersed and potentiallyheterogeneous sources (Han, 2000).
One of the challenges for such facilitation is
the method of data integration, which aims to
provide seamless and exible access to informa -
tion from multiple autonomous, distributed and
heterogeneous data sources through a query in-
terface (Calvanese, Giacomo, & Lenzerini, 1998;
Levy, 2000; Ullman, 1997). In the context of DSS,
there are two broad classes of approaches to
data integration: Data Warehousing and Database
Federation (Reinoso Castillo, Silvescu, Caragea,Pathak, & Honavar, 2004). Practices in relation to
the data warehouse approach cover the acquisi-
tion, extraction, transformation, and loading of
the data into a centralized repository, which can
then be queried using a unied query interface.
The approach further allows interactive analysis
of multidimensional data of variable granularity
with multifunctionalities such as summarization,
consolidation, and aggregation (Nguyen, Min
Tjoa, & Mangisengi, 2003), as well as, the ability
to represent data in cube format (Nieto-Santiste-
ban, Gray, Szalay, Annis, Thakar, & OMullane,
2004). The key difference of the data federation
approach is, that decision makers can query di-
rectly the dispersed heterogeneous data sources
and hence, users are required to impose their own
ontologies in relation to the data requested.
The informational needs of a decision maker
are not limited to those prementioned and are very
seldom limited to data, but include other type of
resources, which may be required to be accessed
from multiple dispersed sources. The resourcesmay include but are not limited to databases,
software, hardware, or even instruments such as
satellites, seismographers, detectors and PDAs.
For example think of an emergency situation
caused by an earthquake. The emergency man-
agement team will be required to make real-time
intelligent decisions and act accordingly to save
lives, property, and the environment by assessing
multiple dispersed resources (Asimakopoulou,
Anumba, & Bouchlaghem, 2005). This particular
decision making process will require team work-ing and collaboration from a number of dispersed
decision makers whose decisions may be depended
on each others interactions. Resource integration
at that level will support decision makers since
it will allow them to view satellite images of the
affected area, observe seismic activity, forecast,
simulate and run what if scenarios, collaborate
with experts and the authorities. This will as-
sist decision makers to prioritize and ultimately
make decisions, which will be disseminated to
available rescue teams who will take then careof the operational tasks. This dissemination may
typically involve a server broadcasting decisions
to heterogeneous mobile devices such as personal
digital assistants (PDAs).
The volume of the data-sets is typically mea-
sured in terabytes and will soon reach petabytes
(Antonioletti et al., 2005). These data-sets are
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Using Grid for Data Sharing to Support Intelligence in Decision Making
variably geographically distributed and their
complexity is ever increasing. That is to say, that
the extraction of meaningful knowledge requires
more and more computing resources. The com-
munities of users that need to access and analyze
this data are often large and geographically dis-
tributed. The combination of large data-set size,
geographic distribution of users and resources,
and computationally intensive analysis results
in complex and stringent performance demands
that, until recently, have not been satised by any
existing computational and data management
infrastructure.
In tackling these problems, the latest studies
in relation to networking and resource integra-tion have resulted in the new concept of grid
technologies, a term originally coined by Foster
in 1995. Grid computing has been described as
the infrastructure and set of protocols that enable
the integrated, collaborative use of distributed
heterogeneous resources including high-end
computers, networks, databases, and scientic
instruments owned and managed by multiple
organizations, referred to as Virtual Organisa-
tions (Foster, 2002). Avirtual organization (VO)
is formed when different organizations cometogether to share resources and collaborate in
order to achieve a common goal (Foster, Kes-
selman, Nick, & Tuecke, 2002). Hence, the grid
concept as a paradigm has an increased focus on
the interconnection of resources both within and
across enterprises. In the rst phase, scientists
have almost exclusively used grid technologies
for their own research and development purposes.
Now however, the focus is shifting to more general
application domains that are closer to everyday
life, such as medical, business, and engineeringapplications (Bessis & Wells, 2005; ERCIM,
2001). It is anticipated that grid technologies will
facilitate intelligence informed decision making
in a way that managers and their teams will be
able to carry out tasks of increased complexity
more effectively and efciently in the form of
one or many interconnected, separable, or in-
separable VOs (Bessis & Wells, 2005; Brezany,
Hofer, Whrer, & Min Tjoa, 2003). Therefore,
in the context of this chapter, the primary goal
is to demonstrate how grid technologies and the
VO concept can serve as the vehicle to empower
intelligence in decision making.
To operate within a VO requires a decision
maker to interface a service or to act as an agent
of someone else in some capacity. Decision mak-
ers will necessarily be involved in delegacy. To
delegate is to entrust a representative to act on a
decision makers behalf. A key delegacy challenge
is the ability to interface with secure, reliable and
scalable VOs, which can operate in an open, dy-
namic, and competitive environment. To achievethis, a number of security mechanisms have to
be seamlessly integrated within the grid environ-
ment. Previous studies have proposed the use of
public key infrastructure (PKI) and X.509 digital
certicates (Foster, Kesselman & Tuecke, 2001;
Foster et al., 2002) while others have proposed
the use of IBC: Identity-based Cryptography (Lim
& Paterson, 2005).
In terms of social exchange theory, an inter-
action always contains an element of risk and
uncertainty due to the fact that an interactionpartner might not reciprocate or do so in an in-
sufcient manner (Stewart, 2003). A mediated
interaction as compared to a face-to-face inter-
action is characterised by a signicantly higher
level of uncertainty and risk (Lee & Turban, 2001;
Ratnasingam, 2005), which inevitably brings up
the question of the interrelation between risk and
control essential to an understanding of trust. The
perceived risk of an interaction is based on the
evaluation of its negative consequences, which are
difcult or impossible to control (Koller, 1988).The more negative are the consequences and the
less an individual can control them, the higher
is the perceived risk. The relationship between
trust and risk has a bilateral causal character that
offers large opportunities for building sustain-
able and auto-manageable systems. The greater
the risk of interaction, the more trust is desired
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Using Grid for Data Sharing to Support Intelligence in Decision Making
and the greater is the motivation to build trust
between oneself and ones interaction partner.
The greater the trust among all members of a
particular group, the greater the risk manage-
ment abilities of that group (McLain & Hack-
man, 1999). Tan and Thoen (2003) afrm, in the
context of uncertainty, trust permits one to feel
condent that current action will have a favour-
able outcome. Seen in this light, trust arises in
spite of high risk and uncertainty conditions as
a compensatory mechanism that permits one
party (e.g., the grid service consumer) to engage
in interaction with another party [either a partner
(individual or collectively) or a system (e.g., the
grid service providers)]. Thus, any analysis oftrust formation between grid entities should ide-
ally take into consideration the specicity of the
grid system, the particular network congurations
and the virtual character of the collaboration. At
the same time, it is important to stress that trust
develops not between organizations as such, but
rather as between the individual human actors
or proxy agents who represent them (Hoecht &
Trott, 1999).
The development of a VO partnership within
a grid community can be viewed generically asa model of dyadic interaction between trustor
(the grid service consumer) and trustee (the grid
service provider). A trustor (the grid service con-
sumer) inevitably takes a risk while depending on
the performance of a trustee (his/her grid partner).
This step is predicated upon the necessity to rely
upon another party in order to achieve ones own
interests, and hence, the interdependence between
grid partners. Not only trusting behavior, but
trusting intentions as well involve a high level
of risk. In the situation of high insecurity, trustbuilding is based on cognitive mechanisms, the
wary suspicious side, to assess the situation and
its consequences, thus potentially reducing the
importance of affective regulation (McKnight,
Kacmar, & Choudhury, 2004). However, the
cognitive nature of these mechanisms does not
of itself equate merely to the control of an inter-
action partner by means of security measures
alone. Trust is more likely to develop under in-
security, when an individual does not know how
the partner will behave (Molm et al., 2000). In
negotiated exchange, an outcome is predictable
thanks to agreement terms that minimize risk of
free-riding behavior, except if the agreement is
not completely binding. Negotiation has the re-
versed effect on trust building: it minimizes risk
and, thus, decreases trust and increases distrust.
Furthermore, there are certain regularities of trust
formation in a computer-mediated interaction that
are different from the situation of the face-to-face
communication.
Within this chapter, our main goal is tohighlight that resource integration within grid
environments in general and for assisting intel-
ligence in decision-making in particular have
been frequently limited to technical merits alone.
We hereby elaborate and articulate our ideas at
greater length and propose ways in which trust
issues as a soft, socially related concept can be
better articulated both with reference to the lit-
erature and to a novel semiotic paradigm. Hence,
the chapters main goals are twofold. Firstly, to
discuss how grid technologies, VOs and opengrid service architecturedata access integra-
tion (OGSA-DAI) can assist intelligence in deci-
sion making. We do this, by discussing Simons
(1977) well-known decision-making phases model
intelligence-design-choice alongside with the
concept of bounded rationality. Secondly, to
stimulate conceptual thinking towards a better
understanding of the novelty of this technology
and the need for a relevant soft trust model to
support its emergence. We do this, by discuss-
ing the role of soft trust issues at two distinctintangible and ambiguous levels of abstraction:
at the VO level of abstraction and the Grid (data)
service level of abstraction through the use of the
semiotic paradigm. To further the explanation of
the concepts and practices associated with using
grid technologies to support intelligence in deci-
sion-making, a minicase is employed incorporat-
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Using Grid for Data Sharing to Support Intelligence in Decision Making
ing scenarios. We conclude by discussing the
implications of using grid technologies to assist
intelligence in decision-making.
thE grId concEpt And Its
coMMErcIAl ExploItAtIon
The concept of grid computing has emerged as
an important research area differentiated from
open systems, clusters, and distributed comput-
ing. That is to say, open systems such as Unix,
Windows, or Linux servers, remove dependencies
on proprietary hardware and operating systems,
but in most instances are used in isolation. Each
deployed application has its own set of servers
purchased for a particular purpose within the
enterprise. Multiple applications rarely share
common servers, resulting in silos of statically
linked applications and servers. This conguration
results in poor server utilization. In contrast, the
grid builds upon open source architectures and
addresses the removal of silos within a connected
enterprise (Xu, Hu, Long, & Liu, 2004). It might
also prot by providing available internal resource
to other internal and/or external customers.Unlike conventional distributed systems,
which are focused on communication between
devices and resources, grid computing takes
advantage of computers connected to a network
making it possible to compute and to share data
resources. Unlike clusters, which have a single
administration and are generally geographically
localized, grids have multiple administrators and
are usually dispersed over a wide area. But most
importantly, clusters have a static architecture,
while grids are uid and dynamic with resourcesentering and leaving.
The added value that grid computing pro-
vides as compared to conventional distributed
systems lies in the inherent ability of the grid to
dynamically orchestrate large scale distributed
computational resources across VOs, so as to le-
verage maximal computational power towards the
solution of a particular problem. More specically,
the grid can allocate and reschedule resources
dynamically in real-time according to the avail-
ability or nonavailability of optimal solution paths
and computational resources. Should a resource
become compromised, untrustworthy or simply
prove to be unreliable, then dynamic rerouting and
rescheduling capabilities can be used to ensure
that the quality of service is not compromised.
Prior agreements, including service delivery
and recovery aspects can be pre-arranged at the
VO level of abstraction before and during run-
time execution at the service level of granularity
across the computational nodes that a particular
VO owns. These advanced features that areintegral to grid computing are rarely to be found
in large scale conventional distributed networks,
particularly those that need to cooperate and co-
ordinate dynamically across organizational and
geographical boundaries. Hence, it is the ability
of grid communities to orchestrate their activities
at the VO level and the service level dynamically
(without the need to consider platform dependant
features) that characterizes grid solutions as dis-
tinct from large-scale conventional distributed
computer networks.The grid is a computational network of tools
and protocols for coordinated resource sharing and
problem solving among pooled assets. These can
be distributed across the globe and are heteroge-
neous in character. Specically, grid computing
is widely seen to represent the next wave of
computing and as such has become the subject of
worldwide focus amongst the research community.
It is specically characterized by ad hoc col-
laborations (sharing of computing resources) as
between geographically distributed institutionsand organizations. The grid is a type of a parallel
and distributed system that enables the sharing,
selection, and aggregation of resources distributed
across multiple administrative domains based on
their availability, capability, performance, cost,
and users quality of service requirements (Goyal,
2005). Grid computing uses many computers con-
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nected via a network simultaneously to solve a
single scientic or business related problem.
Whereas global grid initiatives initially tended
to focus on the needs of the UK scientic com-
munity (Fox & Walker, 2003) in an initiative col-
lectively known as E-Science, in the future, the
business community is expected to increasingly
benet too: grid computing is expected to become
a mainstream business-enterprise topology dur-
ing the rest of the current decade (Castrol-Leon
& Munter, 2005). The type of application most
likely to benet from the blurring of the binding
as between application and host is one that usually
requires substantial amounts of computer power
and/or produces or accesses large amounts ofdata. That is to say, execution of an application
in parallel across multiple host machines distrib-
uted within or between enterprises can increase
performance substantially and also make use of
the spare capacity of existing nodes (PC, servers,
etc.) too. Grid applications are often typically
involved with large volumes of data produced
by data-intensive simulations and experiments
(ERCIM, 2004). In order to guarantee seamless
automation and interoperability of the distributed
data, the need for adequate descriptions such assemantic-based data descriptions, models, ser-
vices, and systems becomes crucial.
Perhaps the most important function that has
emerged from the grid concept is the notion of
VOs. Grid computing provides a means by which
an open distributed and large scale network of
computational resources owned by VOs can en-
gage in the cooperative processing of typically
large data-sets, using the spare capacity of existing
computers owned by real organizations. There-
fore, a VO is formed when different organizationscome together to share resources and collaborate
in order to achieve a common goal. A VO denes
the resources available for the participants and
the rules for accessing and using the resources.
Resources here are not just computing, storage, or
network resources, but they may also be software,
scientic instruments or business data. Thus, by
engaging in a grid partnership both large and
small organizations can potentially leverage the
vast pooled assets of other partner organizations
without the need to purchase or physically own
these expensive resources. A VO mandates the
existence of a common middleware platform that
provides secure and transparent access to com-
mon resources. In practical terms, a VO may
be created using mechanisms such as certicate
authorities (CAs) and trust chains for security,
replica management systems for data organization
and retrieval and centralised scheduling mecha-
nisms for resource management (Venugopal,
Buyya & Ramamohanarao, 2005). Typical initial
application areas have included E-Science data-grids in which Universitys share their resources
across a grid so as to process vast quantities of
data involved in areas such as molecular model-
ing, climate change modeling, and nancial and
economic modeling.
In terms of standards, grids share the same
protocols with Web services (XML, WSDL,
SOAP, UDDI). This often serves to confuse as
to exactly what the differences between the two
actually are. The aim of Web services (WS) is to
provide a service-oriented approach to distributedcomputing issues, whereas grid arises from an
object-oriented approach. The idea of service-
orientation is not new. Distributed application
developers have long deployed services as part
of their infrastructure. CORBA is an example
of the efforts to standardise on a number of
services that provide the functionality needed to
support loosely-coupled, distributed object-based
applications. Further developments in the area
led to the emergence of WS (Atkinson et al.,
2005). However, WS typically provide stateless,persistent services whereas grids provide stateful,
transient instances of objects. In fact, the most
important standard that has emerged recently
is the open grid services architecture (OGSA),
which was developed by the Global Grid Forum
(GGF). OGSA is an informational specication
that aims to dene a common, standard, and
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open architecture for grid-based applications.
The goal of OGSA is to standardize almost all
the services that a grid application may use, for
example job and resource management services,
communications, and security. OGSA species a
service-oriented architecture (SOA) for the grid
that realizes a model of a computing system as
a set of distributed computing patterns realized
using WS as the underlying technology. An im-
portant merit of this model is that all components
of the environment can be virtualized. It is the
virtualization of grid services that underpins the
ability to map common service semantic behavior
seamlessly on to native platform facilities. These
particular characteristics extend the functional-ity offered by WS and other conventional open
systems. In turn, the OGSA standard denes
service interfaces and identies the protocols
for invoking these services. The potential range
of OGSA services are vast and currently include
data and information services, resource and
service management, and core services such as
name resolution and discovery, service domains,
security, policy, messaging, queuing, logging,
events, metering, and accounting. OGSA-DAI
(data, access and integration) provides a meansfor users to grid-enable their data resources.
OGSA-DAI is a middleware that allows data
resources to be accessed via Web services. How-
ever, newer developments in the area have led to
more sophisticated data integration capabilities
using distributed query processing (DQP). DQP
works as a layer on top of OGSA-DAI, which al-
lows queries to be applied to various XML and
relational data resources as though they were a
single logical resource. This can be done through
an additional set of grid services that extend thescope of OGSA-DAI: one of these services acts
as the point of contact for a client and orchestrates
other services behind the scenes, including ser-
vices that evaluate queries on each data resource.
Data integration scenarios can be managed at
either the client or service end; DQP illustrates
an extension to OGSA-DAI at the service end,
enabling data integration (Antonioletti et al.,
2005).
Eay Ai f e gi y
be-ci bai Iy
(1999-)
The grid is being utilized internally and externally
by business organizations to aid their nancial
decision making and modeling. A number of
major Banks in the UK in the U.S. and Europe
have been early adopters (1999-2006) of inter-
nal and external grid computing models so as to
better utilize underused computational nodes in
the context of nancial services modeling and
decision making. As the chairman of the inu-
ential Landesbank Baden Wurtenburg (LBBW)
has recently concisely expressed, the grid and
nancial service industry are a marriage made
in heaven: The banking and nance industry is
predestined from Grid computing solutions. Our
business processes can be parallelized and thus
made faster and more efcient than ever before
(Platform, 2005). That is to say, by seeking to
use underused resources as part of a grid (wherethe VOs are typically comprise different internal
departments), these organizations hope to create
and run advanced simulations and otherwise
distribute increasingly data-intensive computa-
tional tasks across their existing computational
nodes without the need to purchase additional or
dedicated resources. Many of these grid projects
are of a highly commercially sensitive charac-
ter and therefore the details are often withheld
from the public domain. The interested reader
is however, refereed to two reports (Davidson,2002; Carbonnier, 2005) in which grid projects
within JP Morgan and Chase Manhatten Banks
respectively are described in some detail and
which may be viewed as being fairly typical in
illustrating the rationale behind early adoption of
grid applications within international banking. In
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Using Grid for Data Sharing to Support Intelligence in Decision Making
essence the commercial rationale behind many of
these projects is to leverage extra value from exist-
ing computational resources by using the spare
capacity of a vast network of computational
nodes to support data-intensive operations. The
nancial imperative for wider commercial use of
the grid is now undeniable and has recently been
articulated as follows:
Grid computing is not just about an asset change
in enterprise environments; it is about support-
ing a new business model, since there is no
killer application for grids. The key question for
Finance Directors and CFOs is how to break
out of the cycle of asset acquisition and into acapacity service provision model in order to
save money against a new budget system. The
benets of grid computing are about helping to
bring CAPEX (capital expenditurei.e., the cost
of the network, infrastructure and terminals) and
OPEX (operating expenditurei.e., the cost of
keeping the network running) down to acceptable
levels. The grid-based pay-per-use/utility model
is attractive because it can transfer cost from a
CAPEX to an OPEX model, but we dont believe
it will ever be an all or nothing situation forusers. (Fellows, 2005)
gi Ai y sME (sma a
Meim Eeie):
te ne Wae?
In the next wave of commercial adoption of
the grid within the nancial services industry
(2005-onwards), small and medium enterprises
(SMEs) are also now seeking to engage in external
grid partnerships, so as to gain access to vastlyincreased computational power at minimal cost.
In the most common case, the type of application
most likely to benet from the blurring of the
binding as between application and host is one that
usually requires substantial amounts of computer
power and/or produce or access large amounts of
data. That is to say, execution of an application in
parallel across multiple host machines distributed
within or between enterprises can increase per-
formance substantially and also make use of the
spare capacity of existing nodes (PC, servers, etc.)
too. In order to guarantee seamless automation
and interoperation of distributed data, the need
for adequate descriptions such as semantic-based
data descriptions, models, services and systems
becomes crucial.
EnAblIng IntEllIgEncE In
dEcIsIon MAkIng usIng grId
tEchnologIEs
The objective of this section is to discuss and
exemplify the potential of how grid technologies,
VOs and open grid service architecturedata
access integration (OGSA-DAI) within a dynami-
cally changing environment can assist intelligence
in decision-making. We do this, by discussing
Simons (1977) well known decision making
phases intelligence-design-choice alongside
with the concept of bounded rationality.
With this in mind we go on to describe a typi-
cal SME nancial services application in which actitious organization (Synergy Ltd) seeks to
engage in a VO partnership with several universi-
ties so as to seek to leverage the computational
power of the grid for competitive advantage. The
scenario serves as an integrative element within
this chapter, since the remaining sections make
explicit reference to it.
sME seai: syey Fiae
si l
Synergy Finance Solutions Ltd. is a (ctitious)
small and medium enterprise (SME) that develops
and sells advanced computer share trading pack-
ages to both private and corporate investors. These
packages are designed to support individual and
corporate investors wishing to track and predict
future equity (share) price movements across
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Using Grid for Data Sharing to Support Intelligence in Decision Making
global equity markets. Their current package is
designed to meet the needs of individual inves-
tors and is called PrivateInvestor. The license
to use the package is sold to private investors
and the package is typically installed on their
local PC workstation. PrivateInvestor uses
advanced fractal modeling techniques to track
real time Global share price changes on a daily
basis so as to establish patterns. These patterns
are then used together with 12-month historical
price data-sets and advanced fractal modeling
techniques to guide each private investor as to
exactly when best to trade shares, so as to gain
maximum prot at minimal nancial risk. The
package adapts itself to the risk prole of eachindividual investor as it learns more about their
real-time share-trading activities. Synergy makes
their data-set of historical share price patterns
for each share traded available for downloading
into the PrivateInvestor package, on demand,
to each investors workstation.
The managing director of Synergy is Mark
who is a rational manager (Keen & Scott-Morton,
1978) and very familiar with Simons (1977) three-
phase systematic decision-making process. He has
applied it successfully many times in the past.Mark thinks that it is the time to apply it again
for the benet of Synergy. Mark starts with the
rst phase that is the intelligent phase. His goal
is to clearly dene the problem by identifying
symptoms and examining the reality. The rst
phase begins with the identication of his orga-
nizational goal and objective that is to provide an
accurate service to his PrivateInvestor package
customers. Mark thinks that his company does
well in this respect and therefore, he feels that to a
certain extend his organizational goal can be met.However, Mark feels also dissatised. He identi-
es a difference between what he desires/expects,
and what is occurring. This is due to the fact that
a number of PrivateInvestor package customers
have not invested in the best possible way. Mark
made an attempt to determine whether a problem
exists. During his investigation, the sales depart-
ment informs him that Synergy has lost some
customers in the last year. The sales department
conrmed that the scale of loss is not signicant.
For some managers, losing a few customers is not
a major concern but for Mark this is considered
to be a symptom of an underlying problem. Mark
decided to revisit the kind of service that Synergy
offers to PrivateInvestor package customers.
Mark meets with Synergys executive team that
consists of the marketing, nancial advisor, po-
litical analyst and sales managers. He also meets
with Synergys three data analysts who analyse the
12-month data-set. Outcomes from the meeting
have led them to appreciate that the 12-month data-
set limits the accuracy of their advanced fractalnancial models; customers who have left and
gone to competitors who use a 10-year data-set;
competitors use more data analysts; competitors
invest more money in buying additional hardware
resources; and nally, competitors have access to
more modeling tools to choose from; On this basis,
Synergy realizes the need to make an intelligence
informed decision that will keep it abreast of its
competitors. Synergy fully understands that they
need somehow to provide a more accurate service
to its customers. This should be a good enoughsolution to retain existing PrivateInvestor pack-
age customers and maybe even, to increase the
number of its customers.
With this in mind, Synergy moves to the design
phase that is, the second phase of Simons (1977)
systematic decision-making process. This phase
involves nding or developing and analyzing
possible courses of action towards the identica-
tion of possible solutions against the identied
problem space. Synergy operates also under
the process-oriented decision-making thinking(Keen & Scott-Morton, 1978) and fully appreci-
ates Simons (1977) bounded rationality theory.
Synergy appreciates that despite the attractiveness
of optimization as a decision-making strategy, its
practical application is problematic. This is due to
the fact that it is not feasible to attempt to search
for every possible alternative for a given decision.
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Using Grid for Data Sharing to Support Intelligence in Decision Making
Simon exemplied this by dening the term of
problem space. A problem space represents a
boundary of an identied problem and contains
all possible solutions to that problem: optimal,
excellent, very good, acceptable, bad solutions,
and so on. The rational model of decision-making
suggests that the decision maker would seek out
and test each of the solutions found in the domain
of the problem space until all solutions are tested
and compared. At that point, the best solution will
be known and identied. However, what really
happens is that the decision maker actually simpli-
es reality since reality is too large to be handled
by human cognitive limitations. This narrows the
problem space and clearly leads decision-makerto attempt to search within the actual problem
space that is far smaller than the reality.
In the context of this chapter, the attempted
problem space is incomplete and refers to the
actual problem search space. Thus, the decision
maker will most likely not choose the optimal
solution because the narrowed search makes it
improbable that the best solution will ever be
encountered. The approach will lead the decision
maker to settle for a satisfactory solution rather
than searching for the best possible solution.
Similarly, Synergys 12-month data-set make
it impossible for data analysts to identify and
produce the most accurate packages for Priva-
teInvestor customers. On the same basis, data
analysts use a limited number of advanced fractal
nancial models as compared all those that are
theoretically possible available.
At this stage, Synergy has decided to identify
the course of action, which will lead in improv-
ing their existing solution without seeking the
optimum solution. Using this rationale, Synergy
feels that providing access to its own vast 50-year
data collection of historic share-prices that iscurrently unusable can be used to produce more
accurate packages for PrivateInvestor custom-
ers. It is believed that this will increase the actual
problem search space. Thus, the data analysts,
PrivateInvestor package customers and the
decision makers will most likely choose a better
solution because the extended search of the actual
problem search space increases the possibility
Figure 1. VO Grid partners extended search space (Extended version of Simons bounded rationality
theory, 1977)
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Using Grid for Data Sharing to Support Intelligence in Decision Making
that a better solution will be encountered. Figure
1 (reproduced over-page) illustrates Synergys
intelligence and choice decisions.
However, because of the capacity and process-
ing power limitations of the servers at Synergy,
only the last 12 months share-price patterns
have been available for download. Admittedly,
the move to allow access to this 50-year historic
data-sets, adds some complications. For example,
the amount of data required to be analyzed and
charted is potentially vast: patterns relating to
each share are analyzed in real time, daily, weekly,
monthly, yearly, and so forth. Synergys managing
director invites his IT manager to the meeting.
He conrms that buying additional hardwareresources required for these modeling processes
would be a very expensive and risky business.
Synergy decides that it might be a good idea to
extend its search space to look for more alterna-
tive solutions to choose from. Synergy invites
its IT manager to collaborate with two external
academics that are highly regarded in the area of
data management and decision-making modeling.
The outcome of this discussion leads Synergy to
believe that grid technologies may prove viable
as an alternative solution.Synergy moves to the choice that is the third
and last phase of Simons (1977) systematic de-
cision-making process. At this stage, Synergy
needs to make a decision based on the alterna-
tives derived from the previous phase. Synergy
has three options to choose from:
Take the risk and do nothing Buy additional hardware resources, even
consider to invest in more data analysts and
in the deployment of additional cutting-edgefractal nancial models
Enter into a grid partnershipSynergy decides that it is better to enter into
a grid partnership with several universities by
purchasing the computing spare time of their
computational nodes. This is because this will
allow data analysts and PrivateInvestor package
customers to apply their advanced fractal nancial
models to a wider search area (a 50-year data-set
as compared to 12 months). It might then still be
possible not identify the best possible solution
but it is more likely that a better solution will
be identied because the extended search of the
actual problem search space will increase the op-
portunities for a better solution to be encountered.
This in turn, will provide more opportunities to
allow investors to consider where to invest, what
are the possible advantages, disadvantages, risks
and ultimately, decide when to invest.
The idea is to utilize the spare-capacity of uni-
versity computers in real-time, on an on-demandbasis. Their grid partners will then orchestrate the
optimal workow (scalability) needed between
themselves, making best use of any spare capacity
available, so as to process and analyse this 50-year
historic data-set for each individual share. There
are a number of middleware solutions supporting
the coordination and allocation of jobs to be done
in a dispersed environment including Condor-G,
Globus Toolkit, and Unicore. These historic pat-
terns are then to be fully integrated with real-time
minute-by-minute share trading patterns so asto generate a prediction (typically buy, sell, hold,
etc.) back to Synergy. Thus, it is more likely for
their data analysts to select and produce a more
accurate prediction that is clearly caused by intelli-
gence data sharing. Synergy intends to make these
(more accurate) predictions available to existing
private investors who have previously purchased
the PrivateInvestor package at additional cost
that is as an optional premium Gold service
option on an on-demand basis. Historical data is
initially held centrally at Synergy but it can bedistributed via the grid partnership agreement
across any virtual organisation (VO) partners
as necessary that is made available to any grid
partner or partners on demand. Each University
partner may choose to delegate the data-analysis
and processing of this data to another partner in
real-time, depending on the availability of their
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Using Grid for Data Sharing to Support Intelligence in Decision Making
real-time processing capacity. If required, a uni-
versity partner may decide to move data to the
distributed environment as required to meet time
related constraints. The concept is rather similar
in principle to the UK National Grid, whereby
electricity is generated and distributed across
many providers and consumers according to real-
time demand. In this case, Synergy is deemed to
be the consumer and their university partners are
deemed to be their suppliers. Not electricity of
course, but of share pattern analytics derived from
both historical and real timeshare data.
By entering into a grid partnership, Synergy
will be provided with even more opportunities to
make intelligence informed decisions and producemore accurate predictions. Using Simons (1977)
three-phase systematic decision-making theory
(intelligence-design-choice), Synergys data
analysts will have access to a wider selection of
available nancial strategies including more data
mining tools and models available through the grid
partnership. For example, university academic,
research, and technical members of staff will
provide such support and share their expertise
with Synergy. On the other hand, Synergy could
make available a number of incomplete and ob-
solete data-sets that can be used by the university
partners for educational and research purposes.
That is to say, tutors could demonstrate to students
how to apply advanced fractal nancial modeling
using real world data-sets. Similarly, researchers
could undertake experimental research to further
advance nancial models for the benet of Synergy
and the wider community.
Overall, the VO approach will extend the op-
portunities to see things from a multiperspective
point of view that will ultimately advance the in-
volved partners. It is anticipated that the intended
approach will expand available opportunities by
extending the actual search space and by facilitat-ing methods required to deliver a better quality
of service. The ability to share and compute a
vast data-set alongside with the incorporation
of advanced modelling tools and utilisation of
expertise across the grid application environ-
ment will support Synergys managers and data
analysts. PrivateInvestor package customers
and grid partners will make intelligence informed
decisions. For Synergy, this will result in a no
cost solution that will provide a higher quality
Figure 2. The climate between the VO partners
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Using Grid for Data Sharing to Support Intelligence in Decision Making
of service as compared to their competitors. The
move to make historical data available to distrib-
uted partners can be a risky and challenging one.
In particular, data access, integration, analysis,
and charting using a variety of dispersed sources
including legacy systems can cause resource allo-
cation and recovery complications. However, there
are a number of paradigms whereby data access
and integration (DAI) can be implemented within
a grid environment. The concept is rather similar
in principle to E-Science, whereby dispersed data
owners make their heterogeneous data sources
available to other researchers in a VO via the use
of OGSA-DAI (a method for data replication and
virtualization). In addition, sophisticated dataintegration capabilities using DQP, as a layer
on top of OGSA-DAI will allow grid partners
to query, data and/or text mine to the dispersed
resources as though they were a single logical
resource. Figure 2 illustrates the potential of the
grid within a dynamically changing environment
via the use of a rich picture.
Finally, another issue of concern is quality of
service (QoS) including the aspects of multilevel
access, user-friendly interface, security, and reli-
ability. Synergy clearly needs to select and formeffective and evolving partnerships with trusted
university grid service providers. Within these
providers, Synergy seeks to orchestrate the pro-
vision of services in an optimally trustworthy
manner. To achieve this Synergy may need to
check not only that their VO partner local security
and access controls are adequate but also seek to
check and examine wider QoS, and reliability is-
sues too. Indeed, Synergy needs to check on the
organizational reputation of their VO providers
before entering into a grid partnership with anyparticular University potential partner.
The following section seeks to exemplify how
the OGSA-DAI can facilitate the discovery of and
controlled access to distributed sources in general
and Synergys 50-year vast data-set in particular,
to assist intelligence in decision making amongst
the VO partners.
ui ogsA-dAI Faiiae Ae
syey va daa-e
Analysis of the 50-year data-sets requires a
complex series of processing steps in which each
generates intermediate data products of a size com-
parable to the input data-sets. These intermediate
data products need to be stored, either temporarily
or permanently, and made available for discovery
and use by other analysis processes. OGSA-DAI
is the standard infrastructure to support effective
manipulation, processing and use of this vast,
distributed data resource. This will allow shared
data, networking, advanced fractal nancial
models, and compute resources to be delivered to
Synergys data analysts in an integrated, exible
manner. The method will enable Synergys data
analysts to make intelligence informed decisions
and to produce more accurate predictions for the
benet of Synergys customers.
The aim of the OGSA-DAI middleware is to
assist with the access and integration of dispersed
data sources available on the grid. OGSA-DAI
is compliant with Web services inter-operability
(WS-I) and the Web services resource framework
(WSRF). OGSA-DAI is a middleware, whichsupports the integration and virtualization of data
resources, such as relational, XML databases, le
systems or indexed les. Various interfaces are
provided and many popular database management
systems are supported including MySQL, Oracle,
DB2, XML. Data within each of these resource
types can be queried, updated, transformed,
compressed, and/or decompressed. Data can be
also delivered to clients or other OGSA-DAI Web
services, URLs, FTP servers, GridFTP servers, or
les. On the OGSA-DAIs Web site there are fullinstructions of how to download and install the
middleware. Set-up prerequisite software includes
JDK 1.4, Tomcat, Apache Ant and some additional
libraries such as JDBC drivers, etc.
According to the latest specications, OGSA-
DAI provides the following types of services:
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Using Grid for Data Sharing to Support Intelligence in Decision Making
Data access and integration service group
registry (DAISGR): The service allows
data resources that are represented by ser-
vices to be registered and discovered.
Grid data service factory (GDSF): The
service acts as a persistent access point
to a data resource and contains additional
related metadata that may not be available
in the DAISGR.
Grid data service (GDS): The service
acts as a transient access point to a data
resource.
On this basis, we can now proceed to describe
a scenario to introduce various issues of relevanceto Synergys data-sets access and integration
services. These include data collection, advanced
fractal nancial modelling, data generation, and
data analysis. Figure 3 demonstrates these OGSA-
DAI related service interactions between the
different VO grid partners. Figure 3 also notates
these services so as to provide the reader with a
central and continuous point of reference.
Let us assume that the environment comprises
ve simple hosting environments: one that runs
the Synergys data analyst user application (A1);three that encapsulates computing and storage
resources (B, C, D); all three also encapsulate
data-set services; in which one of them (B)
encapsulate Synergys fractal nancial models
and other partners data mining tools; and nally,
a different one (E) that remains idle but could
take over compute related tasks and/or host data
moved from another partner environment. To
complicate the scenario, we assume also that the
latter hosting environment (E) runs a partners
user application (A2) to assist in applying advancednancial modelling tools on a demand basis. It
also encapsulates advanced nancial models.
Firstly, we expect that each data-set is stored in
a different VO grid partner (service provider) and
it is registered with the Grid Data Services Fac-
tory (GDSF) so that they can be found. Similarly,
it is anticipated that Synergys advanced fractal
nancial models and any other data mining tools
have been registered as a service so they can be
found too. Let us assume that Synergys data
analyst as a service requestor needs to obtain
X information on share prices of a particular
stock over the period of ten years. At this stage,
it is important to note that Synergys data analyst
does not need to know which data-set(s) are able
to provide this information and where these are
located. It might be the case that information is
stored in more that one data-set (DS).
The following lists the steps required for a
service requestor to interact with appropriate
data services:
Action 1: Synergys data analyst as a ser-
vice requestor will need to request the data
access and integration service grid register
(DAISGR) for source of data about X.
Action 2: Register will return a handle to
the service requestor.
Action 3: Register will send a request to
the factory (GDSF) to access the relevant
data-sets that are registered with it.
Action 4: Factory will create a grid data
service (GDS) to manage access to relevantdata-sets.
Action 5: Factory will return a handle of
the GDS to Synergys data analyst.
Action 6a: Synergys data analyst as a ser-
vice requestor will perform the query to the
respective GDS using a database language
such as SQL.
Action 7: The GDS will interact with the
data-set(s).
Action 8a: The GDS will return querys
results in a XML format to the servicerequestor.
In the event that GDSF has identied more than
one of the data-sets (DS1, DS2, DS3) that contain
the relevant information, Synergys data analyst
will either select a particular GDS (for example,
GDS1) based on the analysts preference(s) or
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Using Grid for Data Sharing to Support Intelligence in Decision Making
request for data to be integrated into a sink GDS
(6b). That is to say, a sink GDS will handle the
communications (6c) between data analyst and the
multiple GDSs (GDS1, GDS2, GDS3), which will
further interact (7) with their respective data-sets
(DS1, DS2, DS3) so as to return querys results in
a XML format (8b) to Synergys data analyst.
Similarly, a service requestor can submit a
request for a particular nance model that is either
a service of Synergy or registered with another
VO grid partner. A service requestor can be either
a Synergy data analyst (A1) or a partners advisor
(A2) who is available to offer advice or to assist
Synergys data analyst in applying a special type
of nancial modeling tool on an on-demand basis.Once data and models have been collected via the
GDS, Synergys data analyst or a partners advisor
could then for example run their simulation tests.
In the event that a service will or communication
fails another registered resource (service provider)
will take over of the outstanding task(s). For ex-
ample, if during compute perform, one resource
(D) from the grid partners becomes unavailable,
another idle registered resource (E) from the same
or different partner will carry on the computation.
This is due to the fault tolerance grid service that
allows a task to carry over to a different registered
and available resource.
The approach as a whole allows the discovery
of resources and allocation of tasks on a reliable
and exible manner. Using available computing
power, grid partners will minimize time related
constraints when Synergys data analysts run
their prediction tests, which ultimately will en-
able them to make more informed decisions.
The availability of equity enhances computing
power alongside accessing a larger selection of
data-sets, that can be data-mined using additional
data mining tools and advice from experts on an
on-demand basis will likely assist Synergy to
produce more accurate predictions. It is also amethod for the other participated VO grid partners.
Thus, Synergy could make available a number
of incomplete and obsolete data-sets that can be
used by the university partners for educational
and research purposes. That is to say, researchers
(A3) could undertake experimental research to
further advance nancial models for the benet
of Synergy and the wider community. Similarly,
tutors could demonstrate to students how to ap-
ply advanced fractal nancial modeling in real
world data-sets (A4).
Figure 3. OGSA-DAI interactions between VO grid partners
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Using Grid for Data Sharing to Support Intelligence in Decision Making
However, despite the fact that the primary aim
of OGSA-DAI is to make data more accessible,
it must also provide controls over data access
to ensure that the condentiality of the data is
maintained, and to prevent users who do not
have the necessary privileges to change or even
to view data content. Some related trust issues
are discussed next.
te re f sf t Ie
i Ieiee Ifme
deii Mai
Trust is a very complex and nonhomogenous phe-
nomenon that covers many elds of social knowl-
edge and enquiry. The concept has previously
been variously been identied with: a general
disposition; a rational decision about cooperative
behaviour; an affect-based evaluation about an-
other person; a characteristic of social systems
(Rousseau, Sitkin, Burt, & Camerer, 1998), and as
a clan organising principle (McEvily, Perrone,
& Zaheer, 2003). Trust relates to a willingness to
rely on others, and to the condent and positive
expectations about the intentions or behaviour of
another, also, to the willingness to be vulnerableand to acquire risk (Mayer, Davis, & Schoorman,
1995; Rousseau et al., 1998). In spite of the fact
that trust can be analyzed in relation to risk-taking
intentions and/or behaviours, the theoretical link
between trust and risk often remains somewhat ill
dened. The interdependence between trust and
risk is interpreted in many different ways. First,
risk is considered to be an essential condition of
trust emergence (Coleman, 1990), when none or
almost none of the assurance mechanisms are
available to build an interaction between partners.Secondly, trust entails a willingness to take risks
based on the sense of condence that others will
respond as expected and will act in mutually sup-
portive ways, or at least, that others do not actually
intend to do harm (McKnight et al., 2004). The
assumption that trust and risk are closely related
phenomena is not solely a theoretical model, but
has been supported through empirical evidence.
Thus, Koller (1988) found that the degree of risk
affects the degree of trust toward an interaction
partner and stressed that both phenomena relate
to the domain of social perception. An individual
concludes that the individual trusts the interaction
partner, if the individual nds that interaction
with the partner in a risky situation. Indeed, trust
appears to be situated somewhere between com-
plete control and uncertainty. Indeed, trust may
well begin only when mere condence ends. In
many ways trust is seen as being intimately de-
pendant on an information gap as between trustor
and trustee. An individual aware of all relevant
facts does not need to trust, while an individualnot knowing anything about the issue in question
is unable to trust, but only to hope or believe
(Clapses, Bachman, & Wehner, 2003). It has also
been demonstrated (McLain & Hackman, 1999)
that in the context of a lack of information about
the interaction partner, trust emerges in a high-
risk insecure environment, and at the same time,
plays the role of a risk-reducing mechanism.
On this basis, an important element of this
chapter is to highlight that a VO within a grid
environment in general and decision making inparticular is frequently not limited by technical
consideration only. We prementioned that to oper-
ate within a VO, a decision maker is involved in
delegacy. To delegate is to entrust a representative
to act on decision makers behalf. The interac-
tion between individual delegates (as members
of the grid community) to build mutual trust is
central to the analysis itself. We share the view
that, individual elements may offer solutions to
problems but are at best limited as a whole. In
other words, a VO includes, but does not equateto the level of interactions (people-to-people)
and the level of grid services alone. It is also
enriched by a number of phenomena related to
organizational behavior. It might inherit concerns
related to risk and (in)security and might require
further the exploration of trust into the domain
of human cognition and behavior. Hence, a VO
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Using Grid for Data Sharing to Support Intelligence in Decision Making
can be viewed analyzed as a special kind of a
social network and in this respect, with particu-
lar references to its structure, cognitive aspects,
and relations. Thus, it seems important to revisit
trust related issues within the application of grid
environments.
There is little previous research that compre-
hensively accounts for and models the holistic
nature of trust-building processes and regularities
within a grid application environment. Hence, to
safeguard interests and alleviate inconsistencies
caused within a VO as a distributed environment,
we hereby propose a two-level model of abstrac-
tion, a kind of multidisciplinary deconstruction,
that seeks to identify the grid community andsingles out the technological and social mecha-
nisms of trust formation with grid services.
soFt trust At tWo lEvEls oF
AbstrActIon
The purpose of this section is to stimulate con-
ceptual thinking towards a better understanding
of the novelty of this technology and the need for
a relevant soft trust model to support its emer-gence. We do this, by elaborating and articulating
our ideas in relation to the role of soft trust issues
at two distinct intangible and ambiguous levels
of abstraction: at the VO level of abstraction and
the grid (data) service level of abstraction through
the use of the semiotic paradigm.
Emeee f via oaizai
(vo) lee f Aai
A grid service provider needs to ensure that un-authorized access to services and data does not
take place. Additionally, a providers reputation
is clearly at stake and there is a need to maintain
quality, timeliness, reliability, and integrity of the
service according to whatever kind of agreement
has been entered into with consumers and other
providers in an orchestrated manner. There is an
obligation for a service provider to ensure quality
and continuity of service under a wide variety of
conditions. Legal and economic factors may be
relevant too. Intrusion detection is an important
area of responsibility, particularly so in grid
contexts where an unauthorized user may be
potentially able to gain access not only to services
but also to the underlying data-sets themselves.
Corporate governance policies and orientation,
trusted accountancy practices, all serve to dene
a providers relationships to its suppliers, custom-
ers, and business partners (Will, 2003). Trust or
mistrust of a VO at an organizational, depart-
mental and workgroup level may well inuence
whether or not a VO is suitable as a grid partner.Furthermore, a VO is clearly embedded within a
society and culture. A provider needs to consider
how their virtual identity may be veried, and
trusted by potential consumers of grid services.
In particular managing user expectations and
soft requirements poorly can lead to consumer
frustration and indeed even result in frustration
and a degree of mistrust (Tiong, 2005).
In order for Synergy to select and form an
effective and evolving partnership with trusted
providers and orchestrate the provision of servicesin an optimally trustworthy manner it is necessary
to look beyond mere agent-to-agent level of trust
formation and technological mediators to wider
concerns. The value of this approach is intended
to help Synergy to select and verify a suitable
university partner or set of partners to orches-
trate their activities (grid workows) in such a
manner to maximize trust while minimizing risk
of various that is to optimally match candidate
partners against sets of relevant trust, reputation
and reliability criteria.For example Synergy might wish to check the
status (VO reputation) of their potential univer-
sity grid service partners in terms of any of the
above mentioned dimensions: nancial viability,
research reputation, ranking in university league
tables, implementation of local security policies,
and so forth. Equally, a university might wish to
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Using Grid for Data Sharing to Support Intelligence in Decision Making
check whether Synergy meets their own internal
ethical and corporate governance standards by
referring to suitable public domain sources. By
using a semiotic trust ladder, it should be possible
for both Synergy and candidate or actual university
partners to more systematically check and verify
trust dimensions at the social, pragmatic, and
syntactic levels of abstraction. For example it will
be possible for both Synergy and their partners
to look beyond the ne-grained issued of which
XML based standard to select and to address more
fundamental issues that encompass risk, quality
of service and trust issues. Essentially the idea
is for the grid partners to quantify and manage
hidden or implicit trust expectations, to assessthe potential commercial and reputational risks
of their engagement as well as of course selecting
the most appropriate technological trust mediators
to support grid workow activities.
It should also be possible for both Synergy (the
grid service consumer) and University partners
(grid service providers) to dynamically assess and
re-assess their relationships in the light of new and
changing evidence or wider trust domains so as
to generate for example a crude trust/risk rating
for a grid service before, during invocation andafter service invocation. However, to really add
value to existing trust management in the context
of agent to agent (autonomous) trust brokerage
and negotiation a much more ne-grained means
of enabling an agent with these wider contexts
is needed. Organizational reputation and orga-
nizational cultures change and evolve over time.
Local contexts, methods and ways of working
also evolve continually. Ideally therefore, as a
grid service is invoked an agent should be able
to reverify at least some elements of an e-serviceproviders wider trust domain (or just in time)
during run time execution.
gi (daa) seie lee f
Aai viewe t
e semii le
Human trust is a far more elusive and subtle
concept than is articulated in frameworks such
as Web services-trust, as it generally involves
the reference not merely to local contexts but
also wider organizational and social settings
within which e-service transactions of all kinds
typically take place. Existing approaches to the
trusted grid services, which emphasise the value
of establishing secure communications between
autonomic entities do not appear to attempt to
explicitly seek to verify local events, credentials
against wider social, cultural, and organizational
dimensions. Indeed, Liu (2003, 2006) has called
for a wider examination of so-called soft issues
of grid computing and more specically identi-
es the semiotic paradigm as being a potentially
useful conceptual probe within which to address
these wider concerns. Without seeking to enable
agents with wider organizational trust contexts
(what we herein choose to call a trust domains)
we cannot say that these agent based approaches
truly simulate real human trust, but rather, only alimited subset of the characteristics of human trust
that are necessary but not sufcient to claim that
a particular grid service is in fact trustworthy.
Based on Lius (2003, 2006) more general
approach to soft issues of the grid, this work
maps these concerns to the well known classic
semiotic ladder (Stamper, 1973) so as to instan-
tiate a new variant, namely the semiotic trust
ladder shown within Table 1 below, to illustrate
the value of the semiotic paradigm in helping
stakeholders to better conceptualise trust issueswithin virtual organizational settings. Essentially
the novel semiotic trust ladder offers a way of
conceptualizing and modelling trust meaning
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Using Grid for Data Sharing to Support Intelligence in Decision Making
making at a variety of levels of abstraction by
identifying actors, signs, and articulating ways
in which norms and metanorms mediate all acts
of communication.
In Table 1, for each layer of the trust ladder,
some exemplar trust issues are identied and
aligned to the grid service lifecycle. By extend-
ing this approach it is possible to develop a fully
comprehensive account of trust issues during the
entire grid service lifecycle. Indeed, by attempting
to identify and map trust issues to the trust lad-
der, it is hoped that previously implicit or poorly
understood or articulated trust issues may be more
clearly revealed to VO partners at an earlier stage
in the grid service lifecycle than hitherto.
IMplIcAtIons And chAllEngEs
oF usIng grId tEchnologIEs
to support IntEllIgEncE In
dEcIsIon MAkIng
One of the major implications in using grid
technologies as a vehicle to assist intelligence
in decision-making is the ability to enlarge the
actual search space boundaries within the term
of problem space as described by Simon (1977).
Problem space represents a boundary of an identi-
ed problem and contains all possible solutions
to that problem: optimal, excellent, very good,
acceptable, bad solutions, and so on. By searching
in a narrow space, the decision maker will most
likely not choose an optimal solution because the
narrowed search of the actual problem search
space makes it improbable that the best solution
will ever be encountered.
Clearly the grid potentially vastly increases the
size and complexity of the problem spaces that can
realistically be addressed not only by SMEs, but
by all types of organization. Problems that havehitherto been regarded as being intractable either
because of the size of the data-sets needed, their
distributed nature or the sheer complexity of the
multidimensional analysis required can now be
re-examined. Within E-Science these problem
spaces encompass traditional scientic domains
such as nuclear physics but now also typically
include areas such as climate change, where
vast quantities of data and simulations requiring
multidimensional analysis are needed.
Table 1. Macro-dimensions of VOs via a semiotic trust ladder
Exemplar Grid Service
Trust Issues
Semiotic
Trust Ladder
Applicability
(VO Grid Lifecycle)Signs
To what extent does the Service
conform to the desired VO
cultural/cross-cultural norms?
Are there any legal safeguards?
Social world trust: Beliefs and
expectations
Planning stage Cultural/Social trust
Policy signs
Reputation of Grid service
provider/consumer?Any ethical conicts?
Pragmatics: Goals, intentions,
trusted negotiations, trustedcommunications
Planning, build, run time Reputation signs
How reliable, valid are the
services and will they meet
quality norms?
Semantics: Meanings, truth/
falsehood, validity
Build and run time Authentication/validity signs
Secure agents: How trusted are
they?
Syntactics: Formalisms, trusted
access to data, les, software
Build and run time Trusted access signs
Intrusion detection/prevention
adequate?
Empirics: Entropy, channel
capacity
Run time Messaging/trafc management
signs
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Using Grid for Data Sharing to Support Intelligence in Decision Making
Within the business community large Banks
have been amongst the rst to exploit the enhanced
power of the grid to leverage extra value from
vast legacy systems. Now as has been shown
through our illustrative case study, SMEs are
able to address previously intractable problems
and to leverage competitive advantage from grid
computing. This is only the beginningdecision
makers will soon be able to address or re-address
complex multidimensional problems within their
businesses using grid solutions as their standard
or normative preferred tool. Thus, the grid should
not be seen as being merely a tool of scientists or
academicians but rather as a new and powerful
business decision support tool, having real cut-ting edge potential to solve business problems
and enhance competitive advantage. However,
for the power of the grid to be fully realized by
business decision makers, a risk assessment is
needed. For as has been shown in this chapter,
trust issues remain one of many risk factors that
need to be considered before grid computing is
adopted. Since the grid by denition involves the
creation of virtual partnerships between VOs, like
any partnership there are risks as well as rewards.
In the future, grid computing will only be seen toserve and support decision makers if these risks
are properly assessed and accommodated. Like
all enabling technologies, investment needs to
be made in properly harnessing the power of the
grid without exposing the business to undue risk.
This is one of the challenges that still remain to
be solved if grid computing is indeed to become
a normative tool of the business community, not
just a play-thing of academia and scientic stake -
holders. Indeed, there is a greater need now for
the business community to assume a more activerole in the development and commercialization of
the grid. While scientists have hitherto dominated
the grid community, this dominance may soon
increasingly be challenged.
conclusIon
This chapter has endorsed the logic that the con-
cepts and practices associated with grid related
technologies can assist managers in making
intelligence informed decisions within a virtual
organisation (VO). This approach will extend the
opportunities to see things from a multi-perspec-
tive point of view that will ultimately challenge,
mature and advance the involved partners. It is an-
ticipated that the decision to use grid technologies
will unfold new opportunities as it will enlarge the
actual search space boundaries within the term of
problem space as described by Simon (1977). By
default, a problem space represents the boundary
of an identied problem and contains all possible
solutions to that problem. It might then still be
possible not identify the optimal solution but it
is more likely to increase the opportunities for a
better solution to be encountered. Overall, it will
facilitate methods towards normative thinking as
required for a better quality of service.
In the context of this chapter, we have referred
to a VO as the ability to share and exploit com-
modities within a dynamic distributed environ-
ment via networks. Commodities as services areshared and exploited via the use of policies and
may include but are not limited to computational
nodes, stored data, expertise, and other resources.
We have referred to them as transient, uid serv-
ices since they enter and leave based on their
availability and a number of policies.
A core element of this chapter has been to
highlight those VOs within grid environments
that are frequently not limited by technical con-
sideration alone. We took the holistic view that
VOs are also a kind of a social network. Therefore,trust was examined as a soft issue with respect to
its structure, cognitive aspects, and relations. In
particular, we discussed the role of soft trust issues
at two distinct intangible and ambiguous levels of
abstraction: at the VO level of abstraction and the
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Using Grid for Data Sharing to Support Intelligence in Decision Making
grid (data) service level of abstraction through the
use of the semiotic paradigm. We concluded that
trust remains a subtle and elusive concept, yet it
is vital that decision makers attempt to concep-
tualize trust issues explicitly, particularly when
considering implementing complex distributed
systems, such as the grid. Furthermore, semiotics
may well provide a useful paradigmatic vantage
point within which to conceptualize about these
vital trust issues at the empiric, pragmatic, and
organizational levels.
rEFErEncEs
Alter, S. L. (1980). Decision support systems:
Current practices and continuing challenges.
Reading, MA: Addison-Wesley.
Antonioletti, M., Atkinson, M. P., Baxter, R.,
Borley, A., Chue Hong, N. P., Collins, B., et al.
(2005). The design and implementation of grid
database services in OGSA-DAI. Concurrency
and Computation: Practice and Experience,
7(2-4), 357-376.
Asimakopoulou, E., Anumba, C. J. & Bouchla-ghem, D. (2005). Studies of emergency manage-
ment procedures in Greece, Italy and the UK. In
Proceedings of the3rdInternational Conference
on Construction in the 21st century, Advancing
Engineering, Management & Technology , Athens,
Greece (pp.15-17).
Atkinson, M., DcRoure, D., Dunlop, A., Fox, G.,
Henderson, P., Hey, T., et al. (2005). Web service
grids: An evolutionary approach. Retrieved Janu-
ary 5, 2007, from http://www.nesc.ac.uk/techni-
cal_papers/UkeS-2004-05.pdf
Bessis, N. & Wells, J. (2005, March 22-24). Grid
technologies revive the basic IT infrastructure.
Information Systems Unplugged: Developing
Relevant Research. InProceedings of the UKAIS
2005:10th International Conference in Informa-
tion Systems, Newcastle, UK.
Brezany, P., Hofer, J., Whrer, A. & Min Tjoa, A.
(2003). Towards an open service architecture for
data mining on the grid. In Proceedings of the
DEXA Workshops 2003 (pp. 524-528).
Calvanese, D., Giacomo, G. & Lenzerini, M.
(1998). Information integration: Conceptual mod-
elling and reasoning support. In Proceedingsof
the CoopIS98.
Carbonnier, J. P. (2005).BNP paribas bolsters grid
project. Retrieved January 5, 2007, from http://
www.datasynapse.com/pdf/DWT_BNP_Pari-
bas_Bolsters_Grid_ Project _05_3005.pdf
Castro-Leon, E. & Munter, J. (2005) Grid com- puting: Looking forward part 1: Technology
overview. Intel White Paper. Retrieved January
5, 2007, from http://www.intel.com/cd/ids/devel-
oper/asmona /eng/202676.htm
Clapses, C., Bachman, R. & Wehner, T. (2003).
Studying trust in virtual organisations.Interna-
tional Studies of Management and Organisation,
33(3), 7-27.
Coleman, J. S. (1990). Foundations of social
theory. Cambridge: Harvard University Press.
Davidson, C. (2002, October).JP Morgan unveils
compute backbone project. Waters Financial
Intelligence. Retrieved January 5, 2007, from
http://www.platform.com/Company/Case.Studies
/CS.JPMorgan.06.08.2005.htm
ERCIM News. (2004, October). Special theme:
Grids: The next generation (No. 59), pp. 43-44.
ERCIM News. (2001, April). GRIDS: e-science
to e-business.
Fellows, W. (2005, March 21 ). Enterprise grid
computing to shape IT business models. Finance
Director Europe. Retrieved January 5, 2007,
from http://www.financedirectoreurope.com
/articles/451group.htm
Foster, I., Kesselman, C. & Tuecke, S. (2001).
The anatomy of the Grid: Enabling scalable
8/4/2019 Using Grid for Data Sharing to Support Intelligence in Decision Making
22/23
00
Using Grid for Data Sharing to Support Intelligence in Decision Making
virtual organisations. International Journal of
Supercomputer Applications, 15(3).
Foster, I. (2002, July 22). What is the grid? Athree point checklist. Grid Today, 1(6). Re-
trieved January, from http://www.gridtoday.com/
02/0722/100136.html
Foster, I., Kesselman, C., Nick, J. M. & Tuecke,
S. (2002, June 22). The physiology of the grid:
An open grid services architecture for distributed
systems integration. Retrieved January 5, 2007,
from http://www.globus.org/alliance/publications
/papers/ogsa.pdf
Fox, G. & Walker, D. (2003).E-science gap analy-sis.Cardiff University. Retrieved January 5, 2007,
from http://grids.ucs.indiana.edu/ptliupages/pub-
lications/Gap Analysis30June03v2.pdf
Goyal, V. (2005, April 4). The promise of grid
computing. Message posted to gridcomputing@
yahoogroups.com
Han, J. (2000).Data mining: Concepts and tech-
niques: Morgan Kaufmann.
Hoecht, A. & Trott, P. (1999). Trust, risk and con-
trol in the management of collaborative technologydevelopment.International Journal of Innovation
Management, 3(3), 257-270.
Holsapple, C. W. & Whinston, A. B. (1996).
Decision support systems: A knowledge-based
approach. St. Paul, MN: West Publishing.
Keen, P. G. W. & Scott-Morton, M. S. (1978).
Decision support systems: An organisational
perspective. MA: Addison-Wesley.
Koller, M. (1988). Risk as a determinant of trust.
Basic and Applied Social Psychology, 9(4), 265-
276.
Lee, M. K. O. & Turban, E. (2001). A trust model
for consumer internet shopping. International
Journal of Electronic Commerce, 6(1), 76-91.
Levy, A. (2000). Logic-based techniques in data
integration. In Jack Minker (Ed.), Logic based
articial intelligence. Kluwer Publishers.
Lim, H. W. & Paterson, K. G. (2005). I
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