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Decision Support Systems 87.2 Building Decision Support Systems 1545

empirical evidence indicate that design and implemen-

tation issues vary for data-driven, document-driven,

model-driven, and knowledge-driven DSS. Multipar-

ticipant systems such as group and interorganizational

DSS also create complex implementation issues. For

instance, when implementing a data-driven DSS ade-

signer should be especially concerned about the user’s

interest in applying the DSS in unanticipated or novel

situations.

In creating an accounting or ﬁnancial simulation

model, a developer should attempt to verify that the ini-

tial input estimates for the model are thoughtful and

reasonable. In developing a representational or opti-

mization model, an analyst should be concerned about

possible misunderstandings of what the model means

and how it can or cannot be used [87.5]. Networking

issues create challenges for many types of DSS,butes-

pecially for communications-driven systems with many

participants, so-called multiparticipant systems. Today,

architecture and networking issues are increasingly im-

portant in building DSS.

DSS should be built or implemented using an

appropriate process. Many small, specialized model-

driven DSS are built quickly. Large, enterprise-wide

DSS are built using sophisticated tools and systematic

and structured systems analysis and development ap-

proaches. Communications-driven and group DSS are

usually purchased as off-the-shelf software and then

implemented in a company. Creating enterprise-wide

DSS environments remains an iterative and evolution-

ary task. An enterprise-wide DSS grows and inevitably

becomes a major part of the overall information systems

infrastructure of an organization. Despite the signiﬁ-

cant differences created by the speciﬁc task and scope

of a DSS,allDSS have similar technical components

and share a common purpose: supporting decision-

making.

A data-driven DSS database is often a collection of

current and historical structured data from a number of

sources that have been organized for easy access and

analysis. We are expanding the data component to in-

clude unstructured documents in document-driven DSS

and knowledge in the form of rules in knowledge-driven

DSS. Large databases of structured data in enterprise-

wide DSS are often called data warehouses or data

marts. DSS usually use data that has been extracted

from all relevant internal and external databases. Man-

aging information often means managing a database.

Supporting management decision-making means that

computerized tools are used to make sense of the struc-

tured data or documents in a database.

External data

• Dow Jones

• Reuters

Database component

• Knowledge

• Data

• Documents

Model component

• Inference engine

• Models

Communications component

• DSS architecture

• Network

• Web server

• Client/server

• Main frame

User interface component

• Dialog

• Maps

• Menus, icons

• Representations

• Charts, graphs

• Web browser

Users

Internal data

• Personnel

• Production

• Finance

• Marketing

Fig. 87.1 Traditional DSS components [87.1]

Mathematical and analytical models are the ma-

jor component of a model-driven DSS. DSS models

should be used and manipulated directly by managers

and staff specialists. Each model-driven DSS has a spe-

ciﬁc set of purposes and hence different models are

needed and used. Choosing appropriate models is a key

design issue. Also, the software used for creating spe-

ciﬁc models needs to manage needed data and the

user interface. In model-driven DSS the values of key

variables or parameters are changed, often repeatedly,

to reﬂect potential changes in supply, production, the

economy, sales, the marketplace, costs, and/or other en-

vironmental and internal factors. Information from the

models is then analyzed and evaluated by the decision-

maker. Knowledge-driven DSS use special models, an

inference engine, for processing rules or identifying re-

lationships in data.

The communications component refers tohowhard-

ware is organized, how software and data are distributed

in the system, and how components of the system

are integrated and connected. A major issue today is

whether decision support systems should be available

using a web browser on a company intranet and also

available on the global Internet. Managers and infor-

mation systems (IS) staff both need to develop an

understanding of the technical issues and the security

issues related to DSS architectures, networks, and the

Internet. Networking technology is the key driver of

communications-driven DSS.

Part I 87.2

1546 Part I Home, Ofﬁce, and Enterprise Automation

Managers and DSS analysts both need to emphasize

the user interface component. In many ways the user in-

terface is the most important component. The tools for

building the user interface are sometimes termed DSS

generators, query and reporting tools, and front-end

development packages. Much of the DSS design and

development effort should focus on building the user

interface. It is important to remember that the screens

and displays in the user interface heavily inﬂuence how

a manager perceives a DSS, as what one seesis theDSS.

The technologies and software associated with de-

cision support systems continues to change rapidly,

and development tools are overlapping for some ap-

plications. In general, managers and IS staff need to

recognize that the overall technological and social con-

text of DSS and business management is changing.

87.3 DSS Architecture

Many academics discuss building decision support sys-

tems in terms of the four major components (Fig.87.2).

These components collectively comprise the overall

architecture of a DSS. This traditional view of DSS

components remains useful because it identiﬁes com-

monalties between different types of DSS, but it pro-

vides only an initial perspective for understanding the

complexity of DSS architectures.

As noted in the last section, a major component in

the architecture of a DSS is the user interface. The tools

for building the user interface are sometimes termed

DSS generators, query and reporting tools, and front-

end development packages. DSS user interfaces can be

distributed to clients in a thick-client architecture or de-

livered over a network using web pages or Java applets

in a thin-client architecture. A thin-client architecture

where a user interacts using a web browser has many

advantages, but until recently the sophistication of the

user interface was limited compared with a thick-client

architecture where a program resides on a DSS user’s

computer.

Table 87.3 Selected resources for DSS

Resource Type Exemplars

Organizations SIG DSS

http://sigs.aisnet.or

Websites www.dssresources.com

(contains links to most other DSS resources)

Journals Decision Support Systems, Management Science, Decision Sciences, and many others

Books S.B. Eom: The Development of Decision Support Systems Research: A Bibliometrical

Approach (Edwin Mellon, New York, 2007)

D.J. Power: Decision Support Systems Hyperbook, http://dssresources.com/dssbook/

(Fall, 2000)

Turban, Aronson, Liang, Sharda: Decision Support and Business Intelligence Systems,

8th ed. (Prentice Hall, Upper Saddle River 2007)

Model

management

server

Network

User

interface

Client

computer

Data

server

Fig. 87.2 DSS components (after [87.1])

A DSS database is a collection of data orga-

nized for easy access and analysis. Large databases

in enterprise-wide DSS are often called data ware-

houses or data marts. Document or unstructured data

is stored differently than structured data. Web servers

provide a powerful platform for unstructured data and

documents. The architecture for a data-driven DSS of-

ten involves databases on multiple servers, specialized

hardware, and in some cases both multidimensional and

Part I 87.3

Decision Support Systems 87.5 Further Reading 1547

relational database software. The extraction, transfor-

mation, loading, and indexing of structured DSS data

is sometimes very difﬁcult.

As noted before, quantitative models are an im-

portant part of many DSS, especially model-driven

DSS. Model management software can be centralized

on a server with a database, or speciﬁc models can

be distributed to client computers. Java applets and

JavaScript programs provide a powerful new means

to deliver models to users in a thin-client architec-

ture.

The DSS network component refers to how hard-

ware is organized, how software and data are distributed

in the system, and how components of the DSS are in-

tegrated and physically connected. An ongoing issue is

whether a speciﬁc DSS should only be available using

thin-client technology on a company intranet or avail-

able on the global Internet. This should depend on the

needs analysis and a feasibility study. Scalability is also

an important DSS issue. Scalability refers to the abil-

ity to scale hardware and software to support larger

or smaller volumes of data, and more or fewer users.

Scalability also refers to the possibility of increasing or

decreasing size or capability of a DSS in cost-effective

increments.

Table 87.3 provides selected additional readings

and other resources for exploring DSS applications and

technologies. These include web resources, text books,

and selected journal articles in addition to those in-

cluded in the references.

87.4 Conclusions

The DSS design and development environment is

changing as rapidly as the innovation in hardware and

software tools that are available to build them, and in

general the change is in a positive a direction. Web

and mobile technologies will facilitate improved DSS

tools.

Decision support systems are not a panacea for im-

proving business decisions. Most people acknowledge

that managers need good information to manage effec-

tively, but aDSS is not alwaysthe solutionfor providing

good information. A DSS can provide a competitive

advantage and a company may need computerized deci-

sion supportto remain competitive,but decision support

capabilities are limited by the data that can be obtained,

the cost of obtaining, processing, and storing the infor-

mation, the cost of retrieval and distribution, the value

of the information to the user, and the capability of

managers to accept and act on the information. Our ca-

pabilities to support decision-making have increased,

but we still have very real technical, social, interper-

sonal, and political problems that must be overcome

when we build a speciﬁc DSS.

Computerized DSS assume that managers need to

be kept in the decision-making loop but that managers

need and want help to improve their decision-making.

When it is possible and appropriate to automate

decision-making using software then we have chosen

to remove a human decision-maker from the decision-

making process. Managers choose to automate, create

computerized support or make unaided decisions.

87.5 Further Reading

•

A.E. Boyd, J.C. Bilegan: Revenue management

and e-Commerce, Manag. Sci. 49(10), 1363–1386

(2003)

•

T.A. Bresnick, D.M.Buede, A.A. Pisani,L.L. Smith,

B.B. Wood: Airborne and space-borne reconnais-

sance force mixes: A decision analysis approach,

Mil. Oper. Res. 3(4), 65–78 (1997)

•

G. DeSanctis, R.B. Gallupe: A foundation for the

study of group decision support systems, Manag.

Sci. 33(5), 589–609 (1987)

•

F.N. de Silva, R.W. Eglese: Integrating simulation

modeling and GIS: Spatial decision support systems

for evacuation planning, J. Oper. Res. Soc. 51(4),

423–430 (2000)

•

J.P. Shim, M. Warkentin, J.F. Courtney, D.J. Power,

R. Sharda, C. Carlsson: Past, present, and future of

decision support technology, Decis. Support Syst.

33(2), 111–126 (2002), special issue, DSS: Direc-

tions for the Next Decade

•

R.H. Sprague Jr.: A framework for the development

of decision support systems, Manag. Inform. Syst.

Q. 4(4), 1–26 (1980)

Part I 87.5

1548 Part I Home, Ofﬁce, and Enterprise Automation

References

87.1 D.J. Power: Decision Support Systems: Concepts

and Resources for Managers (Westport, Green-

wood/Quorum 2002)

87.2 D.J. Power: A Brief History of Decision Support Sys-

tems, DSSResources.COM, http://DSSResources.COM/

history/dsshistory.html,version4.0,March10

(2007)

87.3 M.S. Scott Morton: Management Decision Systems;

Computer-Based Support for Decision Making

(Harvard Univ. Press, Boston 1971)

87.4 J.D.C. Little: Brandaid, an online marketing mix

model, Part 2: Implementation, calibration and

case study, Oper. Res. 23(4), 656–673 (1975)

87.5 S.L. Alter: Decision Support Systems: Current Prac-

tice and Continuing Challenge (Addison-Wesley,

Reading 1980)

87.6 R.H. Sprague Jr., E.D. Carlson: Building Effective

Decision Support Systems (Prentice Hall, Englewood

Cliffs 1982)

87.7 R.H. Bonczek, C.W. Holsapple, A.B. Whinston:

Foundations of Decision Support Systems (Aca-

demic, New York 1981)

87.8 H.A. Simon: The New Science of Management Deci-

sion (Harper Row, New York 1960)

87.9 D.J. Power: Supporting decision-makers: An ex-

panded framework, e-Proc. Inf. Sci. Conf., ed. by

A. Harriger (Krakow 2001) pp. 431–436

87.10 D.J. Power: Specifying an expanded framework for

classifying and describing decision support sys-

tems, Commun. Assoc. Inf. Syst. 13, 158–166 (2004)

87.11 V. Dhar, R. Stein: Intelligent Decision Support

Methods: The Science of Knowledge (Prentice Hall,

Upper Saddle River 1997)

Part I 87

1549

Collaborative

88. Collaborative e-Work, e-Business, and e-Service

Juan D. Velásquez, Shimon Y. Nof

A major part of automation today is repre-

sented by collaborative e-Work, e-Business, and

e-Service. In this chapter the fundamental the-

ories and scope of collaborative e-Work and

collaborative control theory (CCT) are reviewed,

along with design principles for effectiveness in

the design and operation of their automation

solutions. The potential beneﬁts, opportuni-

ties, and sustainability of emerging electronic

activities, such as virtual manufacturing, e-

Healthcare, automated inspection, e-Supply,

e-Production, e-Collaboration, e-Logistics, and

other e-Activities, will not materialize without

the design of effective e-Work. For instance,

without automatic error prevention or re-

covery, e-Activities in complex systems will

collapse. The four-wheels of collaborative e-

Work, their respective 15 e-Dimensions, and

their role in e-Business and e-Service are ex-

plained and illustrated. Case studies of e-Work,

e-Manufacturing, e-Logistics, e-Business and

e-Service are also provided to enable readers

to get a glimpse into the depth and breadth

of ongoing efforts and potential inhibitors to

revolutionize such e-Systems. Challenges and

emerging solutions are discussed to stimulate

readers to push the boundaries of collaborative

e-Work.

88.1 Background and Deﬁnitions ..................1549

88.2 Theoretical Foundations of e-Work

and Collaborative Control Theory (CCT) ....1552

88.2.1 e-Work Theory and Models..........1553

88.2.2 Agents ......................................1553

88.2.3 Protocols...................................1553

88.2.4 Workﬂow ..................................1554

88.2.5 Integration................................1554

88.2.6 Computer-Integrated

Manufacturing ...........................1555

88.2.7 Human–Computer Interaction .....1555

88.2.8 Extended Enterprises ..................1556

88.2.9 Decision Models .........................1557

88.2.10 Distributed Control Systems .........1557

88.2.11 Collaborative Problem-Solving.....1559

88.2.12 Middleware ...............................1560

88.2.13 Distributed Knowledge Systems ...1560

88.2.14 Grid Computing..........................1560

88.2.15 Knowledge-Based Systems ..........1561

88.3 Design Principles for Collaborative

e-Work, e-Business, and e-Service ........1562

88.3.1 e-Work Design Principles ............1562

88.3.2 Emerging Collaborative e-Work,

e-Business, and e-Service Design

Principles ..................................1569

88.4 Conclusions and Challenges ...................1571

88.5 Further Reading ...................................1572

References ..................................................1573

88.1 Background and Deﬁnitions

The landscape in which everyday work and personal

activities are accomplished by humans, computers, ma-

chines, and organizations has evolved signiﬁcantly in

recent years. From a production point of view, specif-

ically, work has been reshaped by e-Work from the

smallest level systems, micro- and nanosystems, all the

way up to global enterprises. Just as work, business,

commerce, and service are different from each other,

so are e-Work, e-Business, e-Commerce, and e-Service.

They are highly related, yet they are not the same

(Fig.88.1), and therefore some deﬁnitions are needed.

e-Business is the integration of a company’s busi-

ness, including products, procedures, and services, over

the Internet [88.1]. A more comprehensive deﬁnition

Part I 88

1550 Part I Home, Ofﬁce, and Enterprise Automation

e-Work

e-Work features

and functions

• e-Operations

• Human-computer

interaction

• Human-robot

interaction

• Integration

• Collaboration

• Coordination

• Networking

• e-Learning

• e-Training

e-Work models

and tools

e-Business/

e-Service

Factories, plants

• e-Design

• e-Manufacturing

• ERP

• e-Logistics

• Robotic facilities

• Process

automation

• Team projects

• Telework

• CRM

• Scheduling

Offices, services

• Joint projects

• Outsourcing

• Joint marketing

• e-Supply

Alliances

Enterprise

Other e-Activities:

e-Commerce

Customers,

services

• Online orders

• Online sales

• Sales force

automation

• e-Procurement

• Design

customization

• Financial

transactions

• Online

purchasing

• Supply chain

management

• Funds transfer

Suppliers,

services

• Agents

• Protocols

• Workflow

• Middleware

• Parallelism

• Teamwork

• Groupware

• Decision support

systems

Examples: Telemedicine, e-Charities,

e-Explorations

Fig. 88.1 e-Work as the foundation for e-Business, e-Service, e-

Commerce, and other e-Activities (after [88.3])

provided by Browne et al. [88.2] states that e-Business

is about conducting business both internally and/or

externally by electronic means, e.g., over the Inter-

net, intranet or extranet, and that it involves not only

buying and selling but also operating automated, ef-

ﬁcient internal business processes servicing customers

and collaborating with suppliers and business partners

(Fig.88.2).

e-Commerce covers the range of online business

activities for products and services, both business-to-

business and business-to-consumer, through the Inter-

net [88.1].

e-Service is the provision of services over electronic

networks such as the Internet, intranets or extranets

without its scope being limited to service organiza-

tions but ratherencompassing all enterprises, even those

that manufacture goods and which require the devel-

opment and implementation of sound service practices

over electronic networks.

e-Work are the e-Operations, e-Functions, and e-

Support that enable the e-Business, e-Commerce and

e-Service activities deﬁned above. Its formal deﬁ-

nition describes it as any collaborative, computer-

supported and communication-enabled activities in

highly distributed networks of humans and/or robots

and autonomous systems [88.3]. Among the activities

comprised by e-Work are e-Business, e-Commerce, e-

Manufacturing, e-Healthcare, e-Training, e-Logistics,

virtual (v-)Factories, v-Enterprises, and many more

(Fig.88.3). The activities mentioned above, as well as

all others inﬂuenced by e-Work, rely on the use of com-

puter support and communication technologies and at

the same time require a certain degree of collaboration

to support the interactions between humans, computers,

and machines.

The transformative inﬂuence of e-Work can be sum-

marized in this quote [88.3]:

As power ﬁelds, such as magnetic ﬁelds and grav-

itation, inﬂuence bodies to organize and stabilize,

so does the sphere of computing and information

technologies. It envelops us and inﬂuences us to

organize our work systems in a different way, and

purposefully, to stabilize work while effectively pro-

ducing the desired outcomes.

Often the terms collaboration and coordination

are used interchangeably, and even though they are

closely related, they do exhibit signiﬁcant differ-

ences. Camarinha-Matosand Afsarmanesh [88.4,5]and

Nof [88.6] provide a number of deﬁnitions that help

clarify the scope and breadth of each term by placing

them in a broader context, which also includes closely

related terms such as networking and cooperation. It is

important to highlight the differences between all these

terms, as their implications on the design of e-Work are

crucial (Fig.88.4).

Coordination involves the use of communication

and information exchange to reach mutual beneﬁts

among parties by working harmoniously.

Cooperation involves all aspects of coordination

but also incorporates a resource-sharing dimension to

support goal achievement. Commonly, cooperation will

exhibit a component of division of labor among all par-

ticipants and therefore the aggregated value is the result

of adding the individual parts.

Collaboration is the most demanding of the three

processes, where all involved parties share informa-

tion, resources, and responsibilities to jointly plan,

Part I 88.1

Collaborative e-Work, e-Business, and e-Service 88.1 Background and Deﬁnitions 1551

Customers, distributors,

resellers

Customer relationship management

Enterprise requirement planning

Knowledge management

Supply management

Partners,

suppliers

Employees

Enterprise application integration

Stakeholders

• Campaign management

• Catalog management

• Promotion management

• Price

“e-Marketing”

• Sales force automation

• Sales contact, lead

management

• Quote, proposal generation

• Commission management

“e-Sales management”

• Field service

• Call center

• Returns

“e-Customer service”

• Order entry

• Order management

• Configuration

• Available to promise

• Data warehouse

• Decision support systems

• Acknowledgement

• Invoicing

• Payment

• Credit management

“e-Sell”

• Human resources

• Payroll

• Finance

• Maintenance, repair and

operations (MRO)

“e-Manage”

• Demand

planning

• Supply planning

• Forecasting

• Replenishment

• Production plan

“e-Plan”

• Procurement

• Inventory

management

• Strategic sourcing

• Supplier payment

“e-Buy”

• Build to order

• Production

• Manage factory

“e-Make”

• Distribution

management

• Logistics

• Shipping

• Receiving

“e-Move”

Fig. 88.2 e-Business application framework (after [88.7] with permission from Taylor and Francis)

e-Business e-Commerce e-Logistics e-Factory e-Design . . .e-Manufacturing

e-Work

Fig. 88.3 Scope of collaborative e-Work as the common foundation of e-Activities (after [88.8])

implement, and assess the set of activities required to

achieve a common goal, thus jointly creating added

value.

Over the years, computers and advances in commu-

nication technologies and related areas have enabled us

to signiﬁcantly improve the quality of service and prod-

ucts, as well as the level of customization. Speciﬁcally,

collaborative e-Work is revolutionizing the capabilities

of e-Business, e-Production, and e-Service. By lever-

aging existing and well-established work, business and

service models, theories, and solutions with the new

electronic world, it is possible to augment companies’

abilities to signiﬁcantly better meet customers’ needs.

In the next section, the four knowledge clusters of e-

Work and their related 15 dimensions are discussed in

detail.

Part I 88.1

1552 Part I Home, Ofﬁce, and Enterprise Automation

Coordination Cooperation

• Communication

• Information

exchange

• Complementary

goals

• Alignment

of activities

• Compatible goals

• Individual identities

• Ability to work apart

Collaboration

• Joint goals, identities

and responsibilities

• Work together

Fig. 88.4 Coordination, cooperation, and collaboration levels of interaction

88.2 Theoretical Foundations of e-Work

and Collaborative Control Theory (CCT)

Collaborative control theory (CCT) has been developed

over the last decade to support the effective design of

collaborative e-Work, e-Business, and e-Service sys-

tems, and includes several design principles [88.9–

11]. Many researchers have contributed to CCT (e.g.,

see survey in [88.3]) and have addressed issues of

collaborative performance. For instance, collaborative

intelligence has been deﬁned as a measure of the col-

laborative ability of an interacting, distributed group or

Active

middleware

e-Work

Distributed

decision

support

Integration,

coordination,

collaboration

e-Work theory

and models

Integration,

coordination and

collaboration

theory and models

Decision

models

Distributed

control

systems

Collaborative

problem

solving

Computer

integrated

manufacturing

Human-computer

interaction (HCl)

Extended

enterprise

Middleware

technology

Grid

computing

Knowledge-

based systems

Distributed

knowledge systems

Agents Workflow Protocols

Fig. 88.5 Collaborative e-Work: foundations, dimensions, and

scope (after [88.3])

organization, whether of humans and/or agents, robots,

sensors, etc. The major tradeoff that CCT addresses is

that with a larger number of collaborating participants

in a more complex, large-scale system; the knowledge

and performance generated from collaborative efforts

can increase proportionally to the reach of the network,

but conversely the number of conﬂicts, delays, and er-

rors increases too, up to ineffectiveness, reduction in

value, and collapse. To support the collaboration effort,

e-Support is essential so that participants can interact in

real time or asynchronously even though they are not

located within the same physical location. Technolo-

gies used to enhance distributed collaboration and to

facilitate group problem-solving and decision-making

include messaging software, coordination protocols,

task administration protocols, and other e-Support tech-

niques and tools:

1. Group moderation and facilitation

2. Agreement to adhere to a set of fundamental rules

relating to participant interactions

3. Frequent sharing of feedback

4. Frequent service reviews and agreements on

progress and plans

5. Maintenance of a collaborative memory as an orga-

nizational knowledge base

6. Service-oriented architectures for collaborative au-

tomation.

Examples of these developments are discussed

in manufacturing and supply [88.12], in shared un-

Part I 88.2

Collaborative e-Work, e-Business, and e-Service 88.2 Theoretical Foundations of e-Work and Collaborative Control Theory (CCT) 1553

derstanding for collaborative control [88.13], and in

multirobot remote activation with collaborative con-

trol [88.14].

The “e” in collaborative e-Work is supported by

the continuous creation of knowledge in four areas and

their overlaps: (1) e-Work, (2) integration, coordina-

tion and collaboration, (3) distributed decision support

systems, and (4) active middleware, all of which in

turn are rooted in 15 e-Dimensions as described below

(Fig.88.5).

The ﬁrst theoretical foundation area is e-Work itself,

and it comprises four e-Dimensions: (1) e-Work theory

and models, (2) agents, (3) protocols, and (4) workﬂow,

as described below.

88.2.1 e-Work Theory and Models

Theory and models provide the foundation for the de-

velopment of models to (1) augment human abilities

and capabilities at work, (2) augment organizational

abilities to accomplish their goals, and (3) augment the

abilities of all organizational agents (e.g., human, com-

puters, robots, etc.) for collaboration. Below, several

principles that serve as guidelines and foundation for

effective e-Work are discussed in more detail.

88.2.2 Agents

Agents are independently operating and executing

programs capable of responding autonomously to ex-

pected or unexpected events [88.16]. Agents enable

the automation and integration of ﬂexible, adaptive,

and heterogeneous activities, components, and tasks in

a network. For instance, intelligent information agents

are being used to (1) be proactive in the identiﬁca-

tion of enterprise resources, (2) provide value-added

information for ongoing e-Activities, and (3) reduce the

number of conﬂicts and errors in the information pro-

vided to and by all members of networked enterprises

(Fig.88.6). The applicability of agents expands a sig-

niﬁcant number of domains, including: autonomous

mobile or stationary robots, electronic information sys-

tems, data mining and data clusters, decision-support

systems, agent-based simulation, and intelligent de-

sign and manufacturing systems. An example of agents

working in a collaborative environment includes the

University of Michigan digital library (UMDL) where

the library’s electronic information and services are

offered in a distributed environment by building on

commerce and communication protocols for agent in-

teraction.

Communication

skills

• I/O Processing

• Human affect signal

sensoring

User

Knowledge

skills

• Ontological

knowledge

• Metadata, profiles

• Formats

• Learning: Genetic,

neural nets,

case/dialog/

memory-based

Self, environmental

knowledge

Acquired A priori

• ACL

Agent

User

Agent

Task level skills

Collaborative skills

• Wrapping

• Access via Web, API,

middleware (TCP/IP,

HTTP, IIOP)

ACL = Agent Communication Language; API = Application Programming Interface;

TCP/IP = Transmission Control Protocol/Internet Protocol; HTTP = Hypertext Transfer Protocol;

IIOP = Internet Inter-ORB Protocol; HAI = Human Agent Interaction; AC = Affecting Computing

Sources

• Retrieval

• Filtering

• Integration

• Visualization

• Purchasing

• Data mining

• Querying

• Conversation

• Negotiation

• Brokering

• Matchmaking

• Social

filtering

• HAI

• AC

Information

Fig. 88.6 Agent-mediated trading: intelligent agents and e-Busi-

ness (after [88.15])

88.2.3 Protocols

Protocols provide an efﬁcient and effective platform

for autonomous entities (such as agents) to inter-

act, be productive, and achieve their goals. Many

different types of coordination protocols have been de-

signed to achieve effective coordination among agents,

such as (1) dependence-based coordination between

tasks [88.17, 18] and (2) decision- and time-based

protocols to better meet requirement changes and

complexscenarios that requiredecisions beyond coordi-

nation [88.19–21]. As an example, Ko and Nof [88.21]

compare a task administration protocol (TAP)which

is a coordination and interruption–continuation pro-

tocol (CICP), with a non-TAP coordination protocol

(CP) to examine which protocol enables better assign-

ment of tasks in a human–robot team under emergency

pressures. They conclude that the performance of

a TAP-based coordination protocol outperforms the

non-TAP coordination protocol according to the ra-

Part I 88.2

1554 Part I Home, Ofﬁce, and Enterprise Automation

12345678910

TAR

CICP

1.2

0.8

0.6

0.4

0.2

Fig. 88.7 Task allocation ratio (TAR) for emergency

tasks [88.21]. The task administration protocol (CICP)al-

ways performs better because it is typically at a higher

level of decision intelligence relative to the coordination

protocol (CP)

tio of total unallocated tasks to total number of

allocated tasks (TAR)(Fig.88.7). The protocols of

interest in e-Work are those that function at the ap-

plication level and which determine workﬂow control.

Protocols at this level enable effective collaboration

by coordinating information exchange and decisions

such as resource allocation among production tasks

Constraint-based broker server

UDDI-enabled registry

UDDI = Universal Description Discovery and Integration

e-Service adapter

e-Services

e-Service adapter

e-Services

e-Service adapter

e-Services

Cost-benefit

evaluation

server

Constraint

satisfaction

processor

Registration

coordinator

Inquiry

coordinator

Dynamic WfMS

Workflow server

ETR server Event server

Workflow

engines

Process

definition

tool

Internet

Fig. 88.8 Dynamic workﬂow management system (WfMS)archi-

tecture (after [88.22])

to achieve a common goal and jointly generate better

value. The unique advantage of TAPsisinsmarte-

Support and efﬁcient, optimized streamlining of human

collaboration.

88.2.4 Workﬂow

Workﬂow captures the systematic assignment and

processing of activities in dynamic, distributed environ-

ments by enabling process scalability, availability, and

performance reliability. Workﬂow technology has re-

ceived a lot of attention in recent years since it enables

businesses to better manage information and opera-

tions. One recent effort has focused on the design and

implementation of a dynamic workﬂow management

system to support e-Businesses. The system devel-

oped allows for interenterprise workﬂow management

using e-Services for a network of collaborating enter-

prises [88.22]. Due to the dynamics of the Internet

and the organizations using it, an organization can join,

leave or remain in acollaborative network withoutmuch

difﬁculty; however, regardless of the decision of a given

enterprise to join/leave or remain in a collaborative net-

work of organizations, all scenariosrequire that existing

data resources, processes, and services be integrated or

interfaced via workﬂow models. Their model was the

ﬁrst attempt to develop a dynamic architecture to better

account for the nature of services and their availability

at different instances in time (Fig.88.8).

The scope of the second theoretical foundation

area includes integration, coordination, and collab-

oration and comprises four e-Dimensions: (1) inte-

gration, coordination, and collaboration theory and

models, (2) computer-integrated manufacturing (CIM),

(3) human–computer interaction (HCI), and (4) ex-

tended enterprises, as described below.

88.2.5 Integration

Integration, coordination, and collaboration theory and

models provide the backbone for (1) identifying exist-

ing and new connectionsbetween informationresources

and processing entities/agents and (2) developing mod-

els and methodologies to enhance the collaborative

activities between participating entities/agents and their

use of information resources. Various approaches have

been used to address the challenges of integration, co-

ordination, and collaboration in the e-World. One of

the best measures to assess the effects of a higher

level of integration, collaboration, and cooperation is

the number of areas being inﬂuenced, among them:

Part I 88.2