JAPANADVANCED INSTITUTE OFSCIENCE AND TECHNOLOGY1990

COE NEWS

Newsletter of the Center for Strategic Development of Science andTechnology, Japan Advanced Institute of Science and Technology (JAIST)

Vol. 2 No. 3

Winter 2006

Guest Column 1:

Guest Column 2:

Events Driven Approach for Supporting
Real-Time Management of Complex
Systems

Knowledge-Based Management inUncertain Environment

Janusz Granat
National Institute of
Telecommunications,
Szachowa 1, 04-894 Warsaw
andFumiko Seo
Professor of Emeritus, Kyoto University (Kyoto
Institute of Economic Research)
fseo@kier.kyoto-u.ac.jp

Institute of Control and Computation Engineering,
Warsaw University of Technology, 00-665 Warsaw,
PolandJ.Granat@itl.waw.pl
Management and modeling of complex
systems is a challenging area of research. One of theapproaches to modeling such systems is event
driven modeling and management of a complex
system. The application of the concept of event tosystems modeling has been applied for modeling of
discrete systems, of stochastic systems, etc.
However, most of the existing modeling approaches
use only information about the type of event and thetime when an event occurs. Contemporary
information systems store much richer information
about events, in both structured and unstructured
forms. Structured information is stored in databasesin the form of tables. Unstructured information isstored in various forms of textual or even visual
information. Both types of information should beconsidered and used to analyze and predict events inmore advanced events driven modeling approaches.
Figure 1 shows the basic components of an event
driven modeling framework: the system that is
influenced by external and internal events, data andtextual information about the system and about
events, models, algorithms, event detection
algorithms, knowledge representation, description ofdecision maker behavior and actions.
In order to build models or algorithms wehave to store the data about the system and theevents.
Continued on page 2

Introduction
Changes of business environments ofcompanies have recently become very rapid, due toa great progress achieved in various technical fields
along with the development of information
technologies.
The environmental changes for the corporate
management have also been becoming great. The
consciousness on the consumer’s benefit isenhanced and varied. In addition, a new concept ofCorporative Social Responsibility (CSR) is
becoming more important as the measure for
evaluating the corporate values.
Continued on page 5

Inside this Issue
page 3:
Researcher Column: Creativity towards
Innovation
page 8:♦ COE Center NewsJAIST-COE News
Strategic Development of Science and Technology

creation and management. This includes, forexample, knowledge about the consequences of
events. The ability to track event causality andconsequences is an essential step toward on-line
decision support and constitutes an important
challenge for new algorithms for event mining. The
models, algorithms, and data provide the decision
maker with important knowledge about the system.
Then the decision maker can specify various actions
that will be applied in the system and reduce theinfluence of events on the system. The information
about actions should be also stored in acomputerized form; this will help later theevaluation of consequences of the chosen actions. In
some cases the results of the algorithms can bedirectly applied to the system (for example, in socalled event based control algorithms).
Recently, the focus is on real-time decision
support what requires a new class of the dataprocessing, new analytical algorithms as well as
modeling approaches. The actions have to be taken
immediately after an event occurred. The delay may
cause the fault of the system or significant economic
losses. It should be stressed that we can distinguisha broad spectrum of various types of events that willoften require dedicated algorithms and approaches.
However, the framework presented above will help
in the generalization of specific methods andalgorithms. Moreover, this framework may help in
integration of achievements in event based modelingin different scientific disciplines. At this time there
are separate developments in temporal data mining,stochastic systems, event based control etc. The
combination of these approaches might significantly
improve the design of new algorithms.
The approach presented here has various
applications in business monitoring, network
management, intrusion detection, fault detection etc.
Here we present selected examples of event driven
modeling: events monitoring, event processing
networks, events in environmental scanning, event
based control, temporal sequence associations forrare events, event mining and events in alerting
systems. There is also research on events monitoringin given environment. Diverse sensor networks areapplied for events monitoring. Sensor networks are
systems containing many sensing elements endowedwith computation, communication and motion thatcan work together in order to provide information
about events in an environment. In such a case we
obtain information about the type of event, the timeand location of events. Diverse control algorithms
are used for positioning mobile sensors in response
to a series of events. Many monitoring problems canbe also stated as the problem of detecting a change
in the parameters of a system called event detection.

Event Driven Approach… (con’t)
External events
Event
detection

Internal
eventsData & text

Actions
SYSTEM

Knowledge
representation

Algorithms
ModelsWinter, 2006

Decisionmaker

Figure 1. Basic components of the modelingframework
The existence and the proper quality of data are
crucial to any further steps. We can distinguishprimary data that are stored in relational databases
and preprocessed data that are prepared for specificmodeling tasks, as well as data stored in one central
database and data stored in distributed databases.
The designer of an information system can apply
event based system design approach; this leads towell structured databases containing information
about events. The importance of using textual
information about events is also growing. Recently,
also video sequences are becoming an importantsource of data for event discovery.
The models use mathematical formulas todescribe the behavior of the system. In the case of
the presented framework, the models describe
dependencies between events and observablevariables. Various models can be considered, such as
stochastic models, temporal relationships, temporalsequence associations, etc. The algorithms in Figure
1 are understood as algorithms that work withanalytical models as well as algorithms for event
mining or event processing. A key to understanding
events is the knowledge what might have caused
them and at which time the events can happen.
Event mining can be defined as a process of finding:frequent events, rare events, unknown events (iftheir occurrence can be deduced from an
observation of the system), correlation between
events, the consequences of an event and whatcaused the event. There is a special class ofalgorithms for event detection. We distinguish twoclasses of algorithms: event detection based on
numerical and categorical data analysis and event
detection by analysis of textual information. The
results of algorithms, data and textual information
go to the block called knowledge representation,
where a unifying representation of the results isprovided. However, the results constitute a rather
simple form of knowledge; here, there is a place for
introducing contextual knowledge and moreadvanced algorithms that support knowledge

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Strategic Development of Science and Technology

network faults etc. However, if we store the data
about the system in the database it is very difficult
to identify rare events. In this case the events arecharacterized by type of event and the time ofoccurrence of the event. Temporal sequence
associations for rare events can be applied to solve
this problem.
New opportunities result from the large
amount of data that is stored in various databases.
Event mining becomes a challenging area of
research. Here we focus on formulating the event
mining tasks that rely on observations of the systemand concern internal and external events. Figure 2
shows interrelations between events, observation of
the system that is given in form of time series andalarms. Sometimes, it is impossible to observe theevents directly. In such cases the data are stored indatabases in form of time series. This data
represents observations of the system in selected
points. The observations are analyzed by the system
and alarms are generated in case of abrupt changes
in the values of observations. In the next step
another algorithm finds the events that causedchanges in the system.
The following tasks of algorithms can beconsidered:

Another important concept are Event Processing
Networks (EPN) Such networks consist of Event
Processing Agents called event sources, event
processors and event viewers. EPN have been
applied for computer network monitoring. The
events sources were middleware sniffers. The
aggregated information about events has been
displayed by viewers and additionally has been used
for event mining.
This concept has been also applied forsolving business problems. Business organizations
are working on an improvement of the analysis of
the external environment and influences of thisenvironment on the performance of the organization.
Environmental scanning is a new term and it means
the acquisition and use of the information about
events, trends, and relationships in an external
environment. In this case the methods of dealing
with unstructured information about events areespecially important.
In event based control the sampling of data is
event-triggered
insteadoftime-triggered.
Contemporary control equipment includes eventbased PID controllers; such an approach reduces
CPU utilization. The event-triggered PID controller
is a nonlinear system of hybrid nature.
EventsWinter, 2006•

For significant change of observation find theevents that are the reasons of this change•

Predict future events by analyzing changes ofobservations•

Predict changes of observations after an event
occurst?

Observations

As we see, there are diverse applications ofevent based modeling as well as various methods ofand approaches to event analysis. The modelingframework presented here might help in developing
more advanced event driven modeling. We havestressed the importance of a new direction ofresearch called event mining.

Alarm

Figure 2 Events and observations
In many cases we have to monitor andanalyze rare events such as credit card frauds,

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Strategic Development of Science and Technology

Winter, 2006

Motivation. Intrinsic task motivation is to becreated through: (1) meaning (value of work goal or
purpose), (2) competence (self-efficacy), (3) selfdetermination (autonomy in initiation andcontinuation of work, plus self-determined goals), (4)impact (influence on work outcomes. As the variety
of definitions for motivations show, individual
motivation is not static. Extrinsic motivation include:(1) productivity (efficiency), (2) serviceenhancement, (3) organizational control, and (4) risk
avoidance, (5) influenced by individual, job, work
environment, and external environments, (6)arbitrary rewards and goals. What motivates
someone in one personal state and one environment
will not be identical to what motivates them inanother, but individuals tend to have patterns ofmotivation–to be typically intrinsically or
extrinsically motivated.

Researcher Column:
Creativity towards Innovation
Totok Hari Wibowo
JAIST COE Center
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
totokw@gmail.com
Introduction
A different stream of study of innovationconsidered the patterns of innovation withinorganizations and attempted to link to the concept ofself-actualizing individual. Innovation in thisperspective is seen very much as a personal trait, aself-efficacy that allows individuals to remain incontrol, self-motivated, effective and innovative inmost situations.
Management science, on the other side, putemphasis more on leadership roles in changing theorganizational structure, organizational culture, andemployees’ motivations so that change andinnovation could be introduced more easily, with theview that there is one best way to run any
organization and to create innovation. These ideas
offer the potential to describe organizational
functioning in creating innovations in terms ofpatterns rather than merely as the product ofinnovation decisions that achieved pre-determinedoutcomes. Our research attempts to expand thenotion of innovation occurring in patterns by
developing some hypotheses and performing
analyses.

Organizational Culture. A bottom-up culture ischaracterized by: (1) Empowered relations, (2)Decentralization, (3) Organizational slack (excess
capacity), (4) Professional/people and task/businesscultures,(5)
Emphasisoninterpersonalcommunication patterns, (6) Some degree of
democratic control in the workplace. A top-down
culture is characterized by: (1) Hierarchical relations
and a focus on the control or authority structure (2)Centralization and formalization (3) Role and power
cultures (4) Emphasis on formal communication
patterns, (5) Emphasis on structure, (6) Provision of
direction to innovate from above.
Challenge. Challenge basically has two aspects, risk
and relative advantage, and can be distinguishedbetween a minor challenge and a major challenge.

Methodology
With the purpose to investigate the value ofinterrelating the individual and the collectivity, we
identify innovation patterns created in different
individual motivation, organizational culture andenvironments. This approach allows us to observe at
one time three major forces that influenceinnovations and to explore the nature of the patterns
formed. For this purpose, we defined the criteria forthe three dynamics based on the following
hypotheses, (1) Innovation occurs in patterns; (2)How people are motivated, the culture of anorganization and the magnitude of challenge are key
relationships in determining patterns of innovation;(3) Innovation patterns predict the creativity of theideas considered, the implementation environmentand challenges to be faced, and the fate and impact
of innovations.

The Patterns
The pattern of innovation incorporates the
dynamics of individuals, organizational culture andchallenge into patterns as can be seen in the table on
page 7.
Extrinsically motivated innovations are often
oriented to solving problems. The innovations areeither programmed ahead of time or introduced in
response to stress, distress, or any other stimulus
from environment. The crises and political factors
have been identified created extrinsic motivation.
When innovations of minor challenges are created ina top-down culture in combination with extrinsic
motivation, reactive innovation results.
The mixture of a top-down culture and major
challenge with extrinsic motivation forces
innovation on employees and produces imposed
innovation.

Criteria for the Three Dynamics
Criteria
forIndividual
Motivation,
Organizational Culture, and Challenge are given
below:

Continued on page 7

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Strategic Development of Science and Technology

Winter, 2006

at the top level of organizational decision hierarchyin the corporation.
On the other hand, the final goal of thecorporate management is becoming also more
complex. First, in the rapidly changing businessenvironments, the time effects are introduced into
the profit-seeking activity. The concept of the netpresent value discounted with the time factor has
been advocated in the context of the Discounted
Cash-Flow (DCF) or Value Creation Management(VCM). More recently, however, the enhancementof the quality of corporate management is attracting
increasingly much concern for the stakeholders andpublics. The components of the corporate values are
becoming more complex and multivariate; this leads
to multi-objective decision problems. According to
the increase of consumers’ knowledge and selfawareness, the CS for the corporate activity in thetechnology development is composed of diverse
elements, such as its environmental effects, safety
for human living, convenience for users, benefits forstakeholders, disclosure to public, accountability
and compliance of the corporation, etc. The
requirements of consumers concerning the products
development are becoming much more rigorous. In
result, the corporate management cannot be
considered separately from the technology
management.
The concept of the knowledge-based
management should be constructed on the bothphases, technical and corporate, with theirintegration. The concept of advanced corporate
management will be constructed as the strategic
decision making while facing the changing businessenvironments.

Knowledge-Based Management… (cont’d)These environmental changes are bringing
much uncertainty into the business management,
which is confronting more complex decision
problems. The knowledge-based management isintroduced as a method for coping with thissituation; in particular, much progress is expected in
the area of the management of technology (MOT).
In relation to MOT, the theory of theKnowledge-Creating Company has been presented
by Nonaka and Takeuchi, which is based on theconcept of the tacit knowing of Polanyi and includes
a knowledge creation process called the SECI spiral.
An extension of this concept to the model called
Creative Space was also given in this area
(Wierzbicki and Nakamori 2004). This paper
discusses the knowledge-based management inbusiness firms from the points of view of corporate
management along with technology management in
uncertain business environments and examines anadvanced method for coping with the multicriteria
aspects of the corporate management in thechanging environments.
Knowledge-Based Management
The concept of knowledge in the corporate
management is composed of two aspects: theknowledge on technology and the knowledge on
corporate management. On this foundation, theconcept of knowledge-based management also has
two aspects: technical and managerial.
Recently, much attention is paid tomanagerial aspects of the technical development incompanies. The standardization in the design for theparts of a product has evolved to the group
technology (GT) in working sites. More recent
development is the concept of modular approach asa design rule and its consolidation, which has been
evolved to the construction of the modular-cluster
type architecture in industrial markets. One of thepowers of the modularity is its optional property: inthe construction of module architecture, diverse
options can be easily considered. A special type ofmodular-cluster architecture, in which the needs andrequests of customers should be taken in account, iscalled ORMDA (Option Rich Module Design
Architecture). This new trend in the productdevelopment corresponds to the changing
environment for marketing, which is switching
increasingly from the product-oriented to thecustomer-oriented or society-oriented on thefoundation of CSR. In this sense, the technology
management cannot be separated from the corporate
management. Decision making for treating thiscomplex managerial problem should be performed

Contents of MOT concept
MOT can be constructed, from the point ofview of CS, on several factors:• Technology assessment for new products. The
priority in the choice of the technology is puton the user-oriented consideration, which
includestheenvironment-oriented
considerations. Technology development for
the new products is thus combined with the“social-in” as well as “market-in” marketingand should avoid “marketing myopia,” or“product myopia” coming from the classicalproduct-oriented way of thinking. This leads tothe concept of the Social Responsible
Investment.
• Market positioning of new products withparticular benefits. The quality of products has
been enhanced largely due to the generalpermeation of “de facto standard” in the5

JAIST-COE News
Strategic Development of Science and Technology
advanced industrial markets. This situation
leads to severe market competition for thesubsidiary or fringe benefits in the new
products, such as service systems for customers,
convenience in use, quick response to users’
requests or questions, reliability of thecompany, brand, etc. Physical quality of the
products is only one of elements to be secured.
Market positioning of a particular benefit of anew product leads to the strategic decision
problems with multi-objective aspects.

Winter, 2006

Methodological Discussions
Decision analysis can treat consistently themulti-objective properties of decision problems in
uncertain environments. Traditionally decision
analysis is based on the expected utility hypothesis
which includes the evaluation techniques of theutility functions and probability distributions. The
utility function is constructed for measuring thepreference of decision consequences with a common
scaling unit. The probability distribution isconstructed for measuring the chance of occurrenceof an event. Both of the concepts are constructed on
the probability measure with the cardinal scale.
Finally the expected utility function is evaluated andused for the numerical comparison of decision
alternatives. Decision analysis is used for decision
support not as normative, nor descriptive, but asprescriptive approach.
The theoretical extension to multi-objective
decision analysis is called multiattribute utility
theory (MAUT). MAUT presents a method for theheuristic construction of the multiattribute utility
function (MUF), which includes the devices for ahierarchical configuration of multiple objectives, thevalue trade-off experiments between attributes,
sequential nesting procedures into MUF, andsensitivity analysis.
Recent development is with embodying
uncertainty in MAUT, resulting in the multiobjective decision analysis under uncertainty whichconstructs the expected multiattribute utility
function (EMUF). New computer softwaredeveloped for this purpose is called MIDASS(Multi-objective Interactive Decision Analysis
Support Systems), (see Seo, Nishizaki and
Hamamoto 2004). MIDASS finally constructs andevaluates EMUF as a measure for evaluating
gambles with multiple objectives. The main
characteristics of MIDASS includes:(1) Methodological integration of multi-objective
decision analysis with the probability
evaluation and final derivation of EMUF.
(2) Methodological assistance with consistency
checks in the evaluation processes.
(3) Functional independence. Parts of MIDASS
can be operated independently of each other.
(4) Data independence. Database of MIDASS isconstructedindependentlyofprogramoperations and is preserved separately for eachjob.
(5) Preservation of evaluation results. MIDASS
preserves all the results of evaluation if they are
not deleted.

• Finding the core competence of a corporation.
Product positioning in an industrial market iscombined with the establishment of the corecompetence of the company. The concept of
the core competence is not only related totechnological aspects, but also to managerial
aspects. Therefore, corporate management
cannot pursue simply the financial indices,such as price earning ratio (PER), price bookvalue ratio (PBR), etc. The ISO 9000 series
propose the concept of Quality Management
System (QMS) for corporations in coordination
with the ISO 14000 series, where the point ofview of the customers’ satisfaction has beenaddressed. QMS is composed of the eight rules:
1) customer focus, 2) leadership by the top
management, 3) involvement of people(employers), 4) process approach, notprocedural,
5)
systems
approachtomanagement, 6) continual improvement, 7)
factual approach to decision making, 8)
mutually beneficial supplier relationship. The
requirements of ISO 9001:2000 and ISO
9004:2000 for QMS are (i) quality management
system, (ii) manager’s responsibility, (iii)
operational management of resources, (iv)product realization, (v) measurement, analysis
and improvement.
• Introducing the time elements under
uncertainty. The time element in the corporate
management, which involves uncertainty, is
included as an intrinsic factor in the strategic
decision making. The role of the enterprise risk
management (ERM) is also increased.
On the above considerations, the concept of
the corporate value in MOT has multi-objective
characteristic involving uncertainty, which isdifferent from multivariate problems. For copingwith such decision problems, appropriate analytical
tools should be developed in an operational way.

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JAIST-COE News
Strategic Development of Science and Technology(6) “Fool proof” property. Messages for alarming
and confirming user’s operation appear
frequently in every stage of the evaluation.

Winter, 2006

References
Wierzbicki, A. P. and Nakamori, Y. (2006) Creative
Space: Models of Creative Processes for the
Knowledge Civilization Age, Springer.

For an empirical use of this method, diverse
application fields will be examined in more detail,such as in real investment decisions, which will beleft still to be reconstructed from the points of viewof the multi-objective decision problems under
uncertainty.

Seo, F., Nishizaki, I. and Hamamoto, H. (2004)
Development of Multi-objective Interactive
Decision Analysis Support Systems for
Strategic Use, Presented at the INFORMS
Annual Meeting, Denver. October 27.

Creativity towards Innovation (cont’d)

Table of Innovation Patterns Based on Motivation, Culture and Challenge Dynamics
Innovation Pattern
Reactive
ActiveNecessary
ImposedProactive
Continuous
Buy-inTransformational

Motivation
Extrinsic
Extrinsic
Extrinsic
Extrinsic
Intrinsic
Intrinsic
Intrinsic
Intrinsic

CultureTop-down
Bottom-up
Bottom-up
Top-down
Bottom-up
Bottom-up
Top-down
Top-down

Challenge
Minor
Minor
Major
Major
Minor
Major
Minor
Major

problem interests them. A combination of intrinsic
motivation with a bottom-up culture and minor
challenge produces proactive innovation. Proactive
innovation can also be seen as problem focused, but
the creation of solutions before agreement to solve
the problem has been achieved within theorganization places it in a less-convergent, active,
problem-solving category.
The combination of intrinsic motivation with a
top-down culture and minor challenge creates buy-in
innovation. In an environment where individuals are
intrinsically motivated but there is a top-down
culture and major challenge, transformationalinnovation is created. Intrinsic motivation combinedwith a bottom-up culture and major challenge
creates continuous innovation. In continuous
innovation major change is created both throughcumulative minor changes and through periodic
major changes.

Extrinsic motivation can also occur in bottomup cultures, though one of the objectives of such
cultures is often to induce and facilitate intrinsic
motivation. This combination could occur, forexample, when exterior forces such as budget
deficits affect on organizational units. Although insuch a situation staffs are not intrinsically motivated,
they can organize to deal with the challenge in abottom-up fashion. This unusual combination of
extrinsic motivation with a bottom-up culture
produces active innovation when combined withminor challenge. Extrinsic motivation combinedwith a bottom-up culture and major challenge
produces necessary innovation.
Intrinsic motivation produces different kindsof behavior: There is more problem- seeking andmore problem-solving at the local level than whenpeople are extrinsically motivated. Intrinsicallymotivated innovations oriented toward problem
finding often grow out of looseness in theorganization. They result from personal initiative,
when individuals have or create the time to focus on
something besides their immediate work: In suchcases, the individual takes steps to deal withorganizational problems either because the problem
interests them or because the process to solve theConclusion
An idea is not an innovation - an innovation
does not exist until it has been successfully
implemented. Long-term survival of an innovation
depends on its becoming internalized andinstitutionalized, and is bound up with the political

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Winter, 2006

pattern and to address whether the patterns aredifferent in their outcomes.

climate. The impact and fate of these patterns would
be an appropriate next issue for consideration.
The advantage of a model that integrates
motivation, environment and magnitude ofchallenge is that it points to where an organization
may have problems, and in which of these threedomains it may need to act in order to encourage
innovation.
Future research should focus on analyzing
additional cases to confirm the existence of each

References[1] Boden, Margaret, A. (Ed.). (1996).
Dimensions of creativity. Cambridge,
Massachusetts: MIT Press[2] Rickards, Tudor. (1999). Creativity and the
management of change. Oxford: Blackwell.

COE Center News♦ Dr. Homei Miyashita joined the Center as postdoctoral researcher as of April 1, 2005.

JAIST-COE News is published by the Center for Strategic Development of Science and Technology, Japan
Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
Questions, comments, article proposals and for more information about the JAIST-COE News, contact Totok
Hari Wibowo at totoku@jaist.ac.jp, tel. +81-761-51-1790 or go to the link: www.jaist.ac.jp/coe/index.htm
Articles in this newsletter are based on a variety of sources. Signed articles reflect the views of the author.
Unsigned articles are prepared by the editorial staff, which is solely responsible for their content.

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