Knowledge Engineering Management

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VOL 7 NO 52009

kNOwLEdgE

ENgINEErINg

aNd maNagEmENT


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SETLabs BriefingsAdvisory Board

Gaurav RastogiAssociate Vice President,Head - Learning Services

George Eby Mathew

Senior Principal,Infosys Australia

Kochikar V P PhDAssociate Vice President,

Education & Research Unit

Raj JoshiManaging Director,

Infosys Consulting Inc.

Rajiv Narvekar PhDManager,

R&D StrategySoftware Engineering &

Technology Labs

Ranganath MVice President &Chief Risk Officer

Subu GoparajuVice President & Head,Software Engineering &

Technology Labs

knolee PoeeIT SystemsIn the lst thee eces, infomtion technoloy hs evolve n mtue s

epenble online business tnsction pocessin (OLTP) technoloy. Some

tillions of business tnsctions e pocesse coss the ol pe y n it

is no supise tht millions of people hve conence to tust the inteity of

this technoloy. In ition, the lst one ece hs itnesse the vilbility

n iespe use of online nlyticl pocessin (OLaP) tools tht offe

multiimensionl insihts into the ltest entepise infomtion to the business

ecision-mes.

Concuent to the bove evolution, the el of aticil Intellience (aI)

hs one thouh seies of seious chllenes in binin nolee into

utomte esonin n ction. Hoeve, the ecent success stoies in

pplyin aI techniques to specic business poblems hol out pomises tht

this el hs beun to offe cceptble benets to the business community. a

pim shift in infomtion technoloy, teme s knolee Poee IT

(kPIT) systems is nticipte. These kPIT systems shoul enble business uses

- semi-utomticlly o in humn-ssiste ys - to extct, ene n e-use

ctionble entepise nolee. Fo exmple, the nolee of expeience

pofessionls ho cn inose n epi complex enineein tifcts ith

expet sills ho constitute smll pecente cn be me vilble to novices

ho constitute le pecente, in n ttempt to ise the pouctivity n/

o qulity of the novice oup. knolee Enineein is citicl spect of

kPIT systems. an this iscipline coves moels to epesent vious ins ofnolee n techniques to extct, ene n e-use such nolee, hee

n hen equie.

This issue ims to pesent lnscpe pictue of emein tens in business

pplictions of nolee enineein tht cn potentilly empoe entepises

to be smt. Be it the use of iveent teminoloy to efe to common

business concepts coss entepise IT systems o the use of omin-specic

nolee to utomticlly extct nncil t fom complex unstuctue

souces, the ultimte ol of nolee enineein is to enble entepises

move fom the titionl y of mnin entepises to tht of nolee-

oiente n nolee-poee mnement. all the ppes in this collection

eve oun vey potent theme nolee-poee systems fo shp

ecision min n efcient mnement.

we hope you enjoy ein this issue s much s e hve in puttin it toethe

fo you. Neeless to mention, you feebc ill help us in ou pusuits to

bin insihts into technoloy tens to you thouh specil issues of SETLbs

Biens such s this one. Plese o ite in to me ith you comments n

suestions.

rvi P gothi Phd

[email protected]

guest Eito


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SETLabs BriefingsVOL 7 NO 5

2009

Literature Review:Applications of Collaborative Multi-Agent Technology toBusiness: A Comprehensive SurveyBy Ravi Gorthi PhD, Niranjani S, Anjaneyulu Pasala PhD and Arun Sethuraman

Multi-agent systems have the potential to revolutionize the way businesses operate today. Theauthors discuss innovative ways to apply the intelligent systems in the most effective manner toease business communication bottlenecks and speed up decisions therein.

Research:Building Knowledge-Work Support Systems with InformationWarehousesBy Arijit Laha PhDKnowledge can be accessed and interpreted in countless ways. A knowledge managementsystem falls flat if the socio-cultural-behavioral aspects of knowledge workers and users arenot considered. A task-based knowledge management (TbKM) approach and an InformationWarehouse (IW) can open up a host of possibilities in the field of knowledge management,asserts the author.

Insight: Whats in a Name?

By Yogesh Dandawate and John KuriakoseBusiness knowledge is encapsulated in business ontology. Creating business ontologiesafresh is a gargantuan task. The authors draw from their vast experience and suggest thatorganizations that dig into IT artifacts from their existing IT portfolio can build suchontologies wi th ease.

Opinion:Knowledge Management for Virtual TeamsBy Manish Kurhekar and Joydip GhoshalVirtual teams are the order of the day and how one leverages KM tools to smudge the physicalboundaries is what becomes a key differentiating factor. The authors document a plethoraof ways to exchange knowledge in a virtual team setup and win over the challenges of virtualinteractions.

Viewpoint:Toward Disruptive Strategies in Knowledge Management

By Rajesh Elumalai and George AbrahamKM tools and technologies have become imperative to the survival of any organization today.The authors suggest that it is time to revisit the conventional methods that are prevalent todayand work around strategies to maintain a competitive edge in the market.

Perspective:Knowledge Engineering for Customer Service OrganizationsBy Rakesh Kapur and Venugopal SubbaraoCustomer service providers need to update themselves with every new invention andtechnology that hits the market. To stand out in the crowd of service advisors, a differentiatedservice can be accelerated and aided with the help of knowledge engineering solutions, feelthe authors.

Model: Support to Decision Making: An ApproachBy Sujatha R Upadhyaya PhD, Swaminathan Natarajan and Komal KachruTools empowered with a Bayesian inference engine can support decision making in uncertain

situations. The authors suggest that such automated support can effectively reduce analysistime and accommodate flexibility in business decision making.

Methodology: Automated Knowledge-Based Information Extraction fromFinancial ReportsBy Bintu G Vasudevan PhD, Anju G Parvathy, Abhishek Kumar and Rajesh BalakrishnanFinancial data is often stored in tabular form. Information stored in financial statements anddocuments can throw up brilliant analysis if extracted and mined properly. A methodologythat can mine such text residing in images and similar such unstructured data can affectinvestment decisions, avoid pitfalls and reap huge pecuniary benefits, claim the authors.

Case Study: A Differentiated Approach to Business Process Automation usingKnowledge EngineeringBy Ashish Sureka PhD and Venugopal Subbarao

Manual processing of data, be it structured or unstructured, is bound to be cumbersome anderror prone. The authors suggest an application that is developed on a requirement that liesat the intersection of business process automation, knowledge engineering and text analyticsand promises to gather relevant data at a lightning speed.

The Last Word:Power Your Enterprise with Knowledge. Be Smart.By T Ravindra Babu PhD

Index


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Making business decisions under uncertain situationscan be a big pain. Thankfully, Bayesian networks

through their superior knowledge representation andinference methods come to the decision makers aid.

Sujatha Upadhyaya PhDResearcher, Information Management Group

SETLabs, Infosys Technologies Limited

In a world increasingly dictated by change, it is importantfor organizations to move away from people-dependentoperations to system-dependent ones. A sturdy knowledge

management system comes in handy in negotiating todaysall pervasive change.

Rakesh KapurPrincipal ConsultantConsulting Services, Infosys Technologies Limited


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2009

Applications of Collaborative Multi-Agent Technology to Business:

A Comprehensive Survey

By Ravi Gorthi PhD, Niranjani S, Anjaneyulu Pasala PhD and Arun Sethuraman

Intelligent, multi-agent technology offers a hostof new opportunities to businesses across industry

and business segments

The collaborative, intelligent, multi-agenttechnology has witnessed a considerableattention in recent years. This technology

promises to offer a host of new opportunities to

business communities in almost all the vertical

industry and horizontal business segments.

Applications built using these technologies

enable dynamic data and information acquisition,

aiding planning and decision making. In thispaper, an attempt has been made to present a

landscape of plausible applications, which could

be very useful to the current CxOs of enterprises

in planning future business strategies.

MULTI AGENT SYSTEMS (MAS) :

BACKGROUND

An intelligent agent is a distinct kind of

software program concept that has a goal,

has knowledge of one or more domains of

relevance, is autonomous (pro-active) in

achieving its goal, is reactive to the changes to

the environment in which it pursues its goal

and is capable of communicating with humans

and other agents [1, 2]. These agents possess

basic characteristics like (i) role to play, (ii) one

or more goals to achieve, (iii) capability to take

actions autonomously, (iv) capability to monitor

the environment periodically and pro-actively

and effect changes, if required, (v) capability tosense and react to the changes to environment,

and (vi) capability to communicate with

humans, and extended characteristics like (i)

specic knowledge in one or more subjects, (ii)

capability to communicate and collaborate/

compete with other agents, (iii) ability to be

mobile and move around in the environment,

if needed, to achieve the goals [3].

Henceforth in this paper, the term

agent refers to a software agent with the above

mentioned basic and/or extended characteristics.


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Simple problems can be solved byagents with basic characteristics whereas

complex problems require multiple intelligent

agents that collaborate and/or compete among

themselves. Systems built using the extended

characteristics are known as multi-agent

systems (MAS) [4].

Some popular applications of MAS are:

Multiple agents with different roles

collaborate among themselves to

continuously monitor road trafc and

effect changes to signal durations at

road intersections in order to improve

trafc ow [5]

A personal assistant agent (residing on a

mobile wireless connected device of its

owner) detects and interacts with other

similar agents of a local social-network

in a geography and offers a variety of

services of interest to its owner [6]

A set of agents with different roles

collaborate among themselves in a

supply-chain management environment

leading to enhanced productivity and

quality [7, 8].

PRIOR SURVEYSA survey on applicat ions of agents in

telecommunications describes how (i) agents

in Integrated in Service Provision help in

mediating all personal communication from

different media sources with specific user

needs, (ii) agents help in automating some

of the network management and supervision

tasks, and (iii) agents help distributed problem

solving [9].

Abbott and Siskovic discuss the

various ways in which agents can be used

in managing the network resources, inconfiguring software programs, in the

maintenance and repair of software programs,

in e-mail filtering, network monitoring and

protection, etc [2].

A survey on applications of agents

in medical science presents agents-based

Intelligent Decision Support Systems (IDSS)

in areas of clinical management and clinical

research [10]. The study also analyzes the

applications of agents-based IDSS in Neonatal

Intensive Care Unit (NICU).

Mladenic shares a survey on the

applications of agents in text analytics and

learning where machine learning approaches

viz., content-based approach and collaborative

approach, with various user interface agents

have been discussed [11].

A survey carried on Distributed

Articial Intelligence (DAI) illustrates how

multi-agents coordinate with each other in

accomplishing complex tasks and handling

conicting situations. It also discusses game

theory involving inter-agent cooperation [12].

Yet another survey by Kowalczyk et al., narrates

various short overviews on intelligent and

mobile agents in e-commerce [13].

Tveit describes an overview of agent

oriented software engineering [14]. Hoekstra

offers a survey on the usage of intelligent agentsin dynamic scripting, genetic algorithms and

neural networks for video games business [15].

However, given the possibility that

future ITES is likely to heavily depend on

and utilize the collaborative MAS technology,

the details offered by the above surveys

are found to be inadequate. CXOs require a

comprehensive and latest view on the landscape

of applications of MAS technology to various

vertical and horizontal business segments. Our

survey addresses this need.


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APPLICATIONS OF MULTI-AGENTTECHNOLOGY

There is evidence of fairly exhaustive surveys

on the applications of MAS technology to

various vertical industrial segments such as

banking and capital markets [16, 17, 18], travel

and tourism [19, 20, 21], telecommunications

[6], transportation and services [22] and

bioinformatics [23, 24, 25] and horizontal

business domains such as knowledge

management [26], supply chain management

[7] and software project management [27, 28,

29, 30]. The subsequent sub-sections present a

landscape view of these applications.

Application of MAS Technology in Vertical

Industry Segments

Banking and Capital Markets

The rapid growth of e-commerce in recent

years has given rise to concerns in the area

of tax evasion and mitigation. Wei et al., [16]

explore and exploit the power and features

of mobile, multi-agent technology to offer a

new solution to this taxation problem, even

while preserving the privacy of the purchaser.

The authors propose the use of five types

of mobile agents, viz. purchaser agents,

seller agents, bank agents, tax agents and

certification agents, that interact with each

other in the creation and tracking of ElectronicInvoice (EI) and Electronic Tax Voucher (ETV)

leading to efficient and simplified e-commerce

taxation mechanism. The authors simulated

the proposed solution using IBMs Aglet

workbench.

The phenomenal growth in the area

of mobile wireless network users has led to

a great opportunity for the banking industry

to offer mobile banking services such as the

capability for the users to perform various

banking transactions viz., seek account balance,

get alerts on changes to bank accounts, performmoney transfers, pay utility bills, etc., through

their mobile devices. Adagunodo et al., present

an Interactive SMS Banking Agent based

innovative, incrementally scalable, mobile

banking solution [17]. In this solution, the real

time (24 hours a day and 7 days a week) SMS

banking agents run on a server (thus avoiding

the need for distribution and deployment) and

offer a range of banking transactions through

SMS facility on mobile devices.

There are many areas of social operations

such as visiting a restaurant, a hospital, an

entertainment park or a theater, where users

would like to know dynamically and easily

about the availability of resources. Muguda et

al., in their paper describe a very interesting

application of mobile agents [18]. They discuss

their experiments to characterize and model

the benets of planning in such environments,

where resources can be reserved and such

reservations can be traded in a market place.

Travel and Tourism

If people can dynamically get details on their

mobile wireless network enabled handheld

devices about a historical place, monument

or a piece of art work that they are currently

looking at, it can be of immense value to them.

Bombara et al., offer details of a multi-agentbased system called KORE that aims to address

the above need and provide a personal guide

to assist museum visitors through the visitors

wireless connected handheld device [19]. KORE

was developed as a prototype using Java Agent

Development Environment (JADE) to work on

Palm m505 PDA and consists of:

Main museum server, that has global

information database with details of all

the works of art in the museum


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Information service agents that provideaccess to main museum database

A set of zonal servers that have a

database with details on works of art

in that particular zone along with zone

information agents that are responsible

for managing the database

Beamer agents that drive the IR beamers

User mobile agents that use WAI (Work

of Art Identier) from IR beamers and

provide information based on Users

Preferences.

Tourists visiting a particular city would

like to dynamically receive details such as

places to visit, restaurants nearby, visiting

hours of a tourist spot, etc., on their mobile

devices.

Lopez and Bustos describe a multi-agent

system architecture that provides services like

obtaining up-to-date information on places of

visit and planning for a specic day in tourism

industry [20]. This MAS system architecture

consists of broker agents, sight agents, user

agents and a planning agent. Communication

among these agents is based on a common

ontology. Various services like search, reserve,

plan a specific day and register have been

provided to the users. When a user requires

details on places of visit, she uses variousservices provided by a user agent. Then the

user agent sends a REQUEST message to

a broker agent, that in turn processes and

forwards message to sight agents that match the

parameters and return a set of results that match

the users requirements containing relevant

information about each site. In order to reserve,

the user agent sends PROPOSE message with

required information to a sight agent, which

then reserves the bookings or sends refusal

if reservation is not possible. A plan agent

presents plans on receiving a REQUESTmessage from the user agent such that time

can be managed efciently throughout the day.

The authors have developed a prototype and

implemented it using JADE-LEAP platform on

Hewlett Packard iPAQ 5450 that has Bluetooth

and 802.11b onboard to run the user agents.

S i m i l a r l y , B a l a c h a n d r a n a n d

Enkhsaikhan present the use of MAS technology

in automating various services in travel industry

involving airline tickets, hotel accommodations,

taxi services, etc [21]. These agents communicate

with each other and negotiate the services to

provide an optimal solution to a customer. The

different types of agents used in creating this

MAS based application are:

Business agents such as travel agents,

ight agents, hotel agents, car agents

that are specialized assistant agents for

the customer using this system

Database agents that are responsible for

performing all database operations such

as queries and updates.

Transportation Services

Increasing population has led to increase in

traffic. There are limited parking spots in

busy commercial localities, malls, ofces andcolleges. People spend a lot of time nding a

parking space.

To address this problem of finding

parking space Ganchev et al., have demonstrated

a multi-agent based system solution wherein a

set of different types of agents collaborate to

automatically and dynamically locate a parking

space in a university campus. These ideas can

be extended to offer similar service in other

locations of public interest. The system can

inform the user through her mobile wireless


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connected device [22]. The authors presenteda detailed three tier architecture of this

solution consisting of: user mobile devices,

geographically dispersed InfoStations and a

central InfoStation center. Different types of

agents mounted on these devices or systems

collaborate through WPAN or Wi-Fi or Wi-Max

connections. In this solution, a user makes a

request for a parking slot through the mobile

device. This request will be forwarded to

the nearest InfoStation by the personal agent

residing on the users mobile device. If the

nearest InfoStation cannot conrm a parking slot

in its geography, it escalates the request to the

InfoStation center which then locates the near

optimal parking slot if available and informs

the personal agent of the user. This system

was implemented using JADE framework and

LEAP module that facilitate implementation of

agents on mobile devices. One can imagine that,

in future, it is possible to make such a personal

agent intelligent and proactive whereby (i) it

can examine the plans of its owner in advance

and proactively collaborate with appropriate

InfoStations and reserve parking slots, and

(ii) dynamically negotiate changes to these

reservations by sensing changes / delays to the

plans of its owner.

Roozemond and Rogier discuss the use

of intelligent agents to build traffic controlsystems that pro-actively bring changes in real

time to various trafc scenarios [5]. Information

agents collect information about weather, trafc

jams, public transport, route closures, best

routes and various parameters that control

trafc via a secure network and send it to the

user and the control stations. Signal durations

would hence be determined based on the

measured and predicted data. Trafc regulation

and tuning is done with coordination among

adjoining agents.

BioinformaticsThe adoption of multi-agent systems constitutes

an emerging area in bioinformatics [23]. In fact,

a working group on Agents in Bioinformatics

(BIOAGENTS) was founded during the rst

AgentLink III Technical Forum meeting held

in July 2004, with a purpose to explore agent

technology and develop new exible tools for

(a) analysis and management of data, and (b)

for modeling and simulation of computational

biology.

GeneWeaver is a multi-agent system

comprising of a community of agents, having

ve distinct roles, that collaborate with each

other in order to automate the processes

involved in bioinformatics [24].

Armano G et al., describe a multi-

agent system for Protein Secondary Structure

Predict ion (PSSP) by a populat ion of

homogenous experts [25]. The authors discuss

how multi-agent technology is a very good t

to address the problems of PSSP.

Telecommunication

The advent of wireless connected mobile

devices has enabled human beings to be

connected with other humans and information

systems anytime, anywhere. Such a paradigm

shift in connectivity coupled with the MAS

technology is showing a phenomenal potentialfor a new set of social and business applications.

Bryl et al., have used multi-agent

technology and Bluetooth enabled mobile

devices to create and use ad-hoc social networks

[6]. These social networks can hence be used

to provide access to a variety of services that

allow users of a locality to interact and transact

in areas of mutual interest, such as buy and

sell books in a university campus. A generic

architecture of independent servers is presented

where multi-agent platforms can be installed


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and agents can act on behalf of their users.Each server is meant to offer one or more specic

services (e.g., buy and sell books) of interest to

the geographic area in which it is located (e.g.,

a university campus). The strength of this

architecture is that it is (a) domain independent

where each server can offer different services

relevant to its location, and (b) independent

of the MAS technology used (one can use

different MAS technologies such as JADE on

each server). A prototype with buy/sell books

service has been developed and implemented

using JADE and tested using Nokia 6260 and

PC/Server equipped with Tecom Bluetooth

adapter. Bluetooth communication has been

implemented using Blue Cove which is an open

source implementation of the JSR-82 Bluetooth

API for Java.

Application of MAS Technology in Horizontal

Business Segments

Knowledge Management

Knowledge Management (KM) is gaining

importance in large organizations owing to their

geographically distributed operations spread

across different time zones. Such organizations

are increasingly tapping into global markets on

the one hand and resources on the other. KM

systems attempt to offer the latest knowledge

of the enterprise knowledge extracted andcreated from structured and unstructured

sources - to the employees who need it.

Houari and Far offer a comprehensive

methodology to build such a sophisticated

KM system using the multi-agent systems

technology [26]. They discuss how a KM

system built using agents with distinct roles,

cooperation and communication capabilities,

intelligence, autonomy and shared ontologies

can be used to achieve better utilization of

knowledge in decision-making.

Supply Chain ManagementA typical supply-chain manager is responsible

for (a) managing the optimal arrival and

stocking of a range of input materials from

different sub-contractors, and (b) the integration

and processing of the input materials to

produce and deliver a variety of finished

products to the clients. The management of this

responsibility in todays world is still human

centric. The supply chain manager and her

team interact with the teams representing the

sub-contractors, enterprise production units

and the clients. These interactions are known

to involve many tedious tasks that are error

prone. In addition to that, the lack of latest

information from all these sources can impact

cost, productivity and quality of products

delivered to the clients.

To achieve better coordination in the

flow of information among the sub-services

of a supply chain management, Wang et

al., propose the use of a variety of software

agents [7]. The problem of coordination among

sub-services is modeled as a distributed

constraint satisfaction problem which is solved

collaboratively by the group of software

agents. The steps involved in this solution

methodology are as follows:

Decomposing customer requirementsinto a set of services represented by a

business process or plan. This can be

achieved by means of any workflow

representation or hierarchical task

network (HTN) wherein each task is

broken down into sub-tasks and uses

task-reduction rules to decompose

abstract goals into lower level tasks.

Find and coordinate the actors that

would be fullling these services.


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These steps are achieved by creatingmultiple service dispatcher agents, service

broker agents and service provider agents.

The requirements are initially analyzed by

the dispatcher agent, based on the customers

requirements and history of customer requests.

Following this, each service broker agent

forwards the request to service providers for

collecting bids or solutions to the request. Once

all the bids are received, the next step is to

lter the dominated solutions and then identify

compatible and promising solutions. The nal

step is to rene constraints for a global solution

by means of communications between the

various service broker agents, thereby achieving

coherence and coordination.

Kern et al., discuss on how intelligent

software agents can help humans in carrying

out different tasks involved in supply chain

management [8]. Their project, titled as

MobiSoft, proposes a new form of supply-chain

management. In their approach, a mobile device

based software agent that performs the role of

a personal assistant is provided to each of the

humans involved in the supply-chain process

ow. These personal assistant agents interact

and collaborate to reduce human errors and

provide latest information to their owners

anytime, anywhere thereby enabling the teams

to achieve higher levels of productivity andquality.

Software Project Management

An important characteristic of business

management is the use of dialogues by a

community of professionals to solve problems.

One of the serious problems confronting the

business managers, in their problem solving

and decision making endeavor, is the high

degree of dependency on human interactions

and the high degree of manual interpretation

of dialogues by humans. Such a dependencyon manual intervention lends itself to problems

that can result from (i) the unpredictable,

inconsistent egoistic behavior of humans that

one witnesses time to time, (ii) the drop in

efciency of humans under stressful situations,

or (iii) the use of less experienced/qualied

humans for managing tasks due to lack of

sufcient number of adequately skilled human

resources.

Sethuraman et al . , i l lustrate the

application of MAS technology to one such

business management task, viz., software

project management (SPM) [27]. The authors

discuss the various sub-tasks of SPM that can

get benetted from the use of MAS technology.

They use the task of Quality Review which is

initiated and completed at the end of each phase

of the software lifecycle and demonstrate how

a set of personal assistant agents assigned to

each (a) software engineer, (b) quality assurance

reviewer, (c) quality assurance manager and (d)

software project manager, collaborate among

themselves and manage the Quality Review

task efciently. The agents manage many steps

of the process of Quality Review but do not

perform the sub-task of actually reviewing the

artifacts. The main advantages brought out by

this research are (i) productivity improvement,

as the agents perform many mundane tasks thatotherwise consume the time of experienced

software professionals, and (ii) consistency of

ensuring that the task of Quality Review is

initiated and completed at the right time and

any incompleteness is recorded and escalated

in time.

Petr ie et a l . i l lustrate how MAS

technology can be utilized for propagation

of dynamic knowledge, such as designs and

plans that are changed according to the status

of project execution, between the project


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designers and planners, such that the effectsof changes are communicated properly [28].

The effort is concentrated on provision of

support for complex projects, where it is crucial

to communicate changes to necessary actors

on time, also termed as Distributed Integrated

Process Coordination. The authors characterize

their coordination model as a logical set of

dependencies among the project elements

that can be used to determine the effects of

changes within the project. To implement

this, Redux dependencies model was used,

which tracks validity within the dependency

model and notifies designers as and when

changes occur. They exhibit this by means of

a central facilitating event which uses these

Redux dependencies for decision making and

propagation. They conduct a case study by

means of a building construction example.

Nienaber et al., talk of a comprehensive

black-box model of a generic agent framework

that could be used in different phases of

software project management [29]. The paper

discusses the creation of personal assistant

(PA) agents, messaging agents, task agents,

monitoring agents and team manager agents.

A multi-agent system comprised of these

types of agents is used in the framework

to support various aspects of SPM, such

as scope management, time management,cost management, quality management,

human resource management, communication

management and risk management. The paper

discusses the prototype design of the system

and proposes the development of the same

using JADE framework.

Pitt et al., describe the design and

implementation of a CEC GOAL project, that

aims at development of generic software tools

to support distributed project management,

which is collaborative, decentralized and inter-

organizational [30]. The authors propose theuse of autonomous software agents to provide

for normalization of inter-organizational

terminology and f low of information,

structuring of inter-organizational interactions

with respect to contracts and working practices

and also to enable each organization to provide

or use services required or offered by other

organizations. It uses a distributed review

process as an example to exhibit the application

of an agent system with behavior specified

by means of decision logic. They consider a

quality control scenario, where the deliverables

are largely technical papers. The project ofce

aims to assure quality of the papers by means

of getting them reviewed by at least three

reviewers. Hence a call for participation for

performing this review is sent out, followed by

an announce and negotiate way of aiding this

review process.

CONCLUSION

MAS is found to be increasingly adapted by

various industry verticals and horizontal

business segments. There is an important

need to present a comprehensive survey of

the current and potential future applications

of MAS technology, which is undertaken

in this paper. The landscape of case studies

discussed in this survey points to a host of newopportunities to various business communities.

REFERENCES

1. M Wooldridge, An Introduction to

Multi-Agent Systems, John Wiley &

Sons, 2002

2. L Abbott and H Siskovic, Intelligent

Agents in Computer and Network

Management. Available at http://

teachnet .edb.utexas.edu/~lynda_

abbott/webpage.html#intagt3


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3. G Weiss, Multi Agent Systems: A ModernApproach to Distributed Artificial

Intelligence; The MIT Press, 2001

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A Survey of Agent-Based Intelligent

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2009

Building Knowledge-WorkSupport Systems with Information

WarehousesBy Arijit Laha PhD

Efficient access to relevant information canenable your knowledge worker perform complex

tasks with seamless ease

Knowledge Management (KM) today can beseen as a key focus area within all leadingknowledge driven organizations. Several

initiatives are taken up within organizations

to manage knowledge. However, despite

all the money spent and the efforts put in

by organizations to achieve effective KM

capabilities, the results till date are far from

stellar. The overall situation led Maier to

observe the solution is still not there andmany businesses trying to implement these

technologies have been frustrated by the fact

that the technologies certainly could not live up

to the overly high expectations [1].

Much of the problems stem from the

multi-faceted nature of knowledge management

systems (KMS) that not only involve information

technology, but the social, cultural and

behavioral aspects of the organization as a

whole, as well as of various user communities

within the organization [2]. Usually there exists

signicant diversity of the above factors across

organizational subunits within an organization

[3]. This tends to render the universalistic

approach of organizational KM less effective [4].

In recent years, a host of extensive

ethnographic studies, where the researcher

becomes part of the environment and culture

under study and makes rst-hand observations

over an extended period of time, have been

published to demonstrate the effectiveness ofKM practices in various organizations [4, 5]. All

these studies, among other interesting ndings,

emphasize the necessity of building KMS to

cater to the needs of knowledge workers such

that it directly affects the way they perform a

task. Such task-oriented approach of knowledge

management is called the task-based knowledge

management (TbKM) [5, 6].

Burstein and Linger, based on their

extensive field works, positioned TbKM as a

robust framework suitable for studying and


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analyzing the characteristics of knowledge-intensive tasks [6]. They define a task as a

substantially invariant activity with outcomes,

including tangible outputs. A task is performed

by socially situated actors. Burstein and Linger

use the term knowledge work referring to the

collection of activities that constitute a task. They

have also outlined a conceptual architecture

of KMS supporting the workers in performing

tasks. This clearly makes a huge paradigm shift,

changing the focus to task instead of organization.

As a consequence, the designer of a KMS, instead

of studying the KM requirements of an entire

organization in all its diversity, can study the

requirements of individual workers or community

of workers involved in performing specic classes

of tasks and design system supports adapted to

the task-specic characteristics.

In TbKM, a KMS designed for supportinga targeted task or task-type is called a knowledge

work support system ( KWSS) [6]. TbKM can be

viewed as a framework for developing KWSS,

each supporting a single targeted task or task

type. Typical examples of such task-types are

survey design, dictionary construction, weather

forecasting, etc. [6]. In large organizations, there

is a bulk of knowledge-intensive tasks involving

planning, decision-making and many other

creative and reflective requirements where

suitable KWSS can be of great value. However,

due to the scale and complexity of such tasks,for building effective KWSS we need to consider

several additional aspects of KWSS that have not

received adequate consideration under TbKM.

EFFICIENCY AND RELEVANCE: THE

CONTEXT

This paper looks into the problem of providing

the knowledge workers efcient access to relevant

information. In the process, the discussion will

also delve on developing a novel approach for

building a very advanced information archive

called the information warehouse (IW) to

meet the requirements of knowledge workers

performing complex tasks using KWSSes.

As indicated above, the two operative

terms vis--vis information access here are

efcientand relevant. In conventional information

management systems the unit of creation, archivaland retrieval of information is whole document.

On the other hand, according to TbKM, a task is

a system of activities consisting of structure and

processes. Structure refers to the composition

of the task in terms of smaller activities and the

processes encapsulate the interrelation of the

activities. It is easy to see that a knowledge worker

engaged in performing a task, at any given time, is

actually engaged in performing an activity as part

of the task. Consequently, his/her information

needs are governed by the current task.

A regular search for information often throws up multiple

documents for the knowledge worker to read and decipherrelevant knowledge


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However, the worker, while seekinginformation, receives a set of whole documents,

some of which, depending on the precision of

the IR system, typically contain information

useful to the worker, embedded in relatively

small portions. Nevertheless, to access the

information, the worker needs to read a number

of (ideally all) whole documents retrieved,

which puts enormous demand on the cognitive

ability of the worker as well as on the available

time to her. Further, in todays environment,

where availability of multiple IT-enabled

archives of enormous volumes is common in

organizations, the worker faces serious threat

of information overload.

The most crucial support an information

system can provide to a knowledge worker

is the access to information that the worker is

likely to nd relevant and thus useful. However,

judgment of relevanceis a complex issue involving

situational, topical, cognitive, even social aspects,

many of them being beyond the scope of

information systems [7]. Nevertheless, the context

dened as any information that can be used to

characterize the situation of an entity forms an

important basis of judging the relevance [8].

From the perspective of a knowledge worker, her

current task and the current activity as part of the

task, dictate the most important components of

context. Sufce it to say:The information needed by a knowledge

worker, to make use in the context of her current

task-activity instance, is produced by other

knowledge workers as part of different instances

of task (may be of different types altogether)

performance, within their own contexts.

Thus, to properly use the information,

the current worker, i.e., the consumer, needs

to understand and/or compare the current

context with those of the other workers, i.e., the

producers of information.

While describing different aspects ofIW, this discussion will draw upon various

examples from a patient-care KWSS, under

development, for clearer explanations.

INFORMATION WAREHOUSE (IW)

IW supports archival of contextualized

information and is organized into two layers

the contextualization support and the

task instance (TI) archive that put together

provide the required functionalities [Fig.1].

The contextualization support layer consists of

artifacts that include structural and semantic

denitions of the supported tasks/task-types

and the domain vocabulary. They provide

means to category-based annotations of the

informational elements and are developed as

part of building individual KWSS. On the other

hand, the TI archive contains the information

produced/reproduced by performance of

instances or episodes of supported tasks.

The Contextualization Support: Knowledge

work is defined as the production and

The Information Warehouse

Contextualization Support

Generic

Informational Elements

Domain Semantics/

Vocabulary Theasurus/

Ontology, etc.

Mapping:

Task Elements to Generic

Info Elements

Mapping: Task

Elements to Domain

Semantics

Target Task(s)Definition(s): Structural

and Semantic

The Task Instance (TI)

Archive: TI Information are

contained in Generic

Informational Elements:

Granular, Linked and

Provenanced

Figure 1: The Information Warehouse (IW) ArchitectureSource: Infosys Research


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reproduction of information and knowledge[9]. The denition simultaneously captures two

aspects of knowledge work:

1. C o n s u m p t i o n o f I n f o r m a t i o n :

Consumption of information allows a

human worker to gain new knowledge

and/or update existing knowledge.

2. Production of Information: When the

worker articulates the knowledge, in

some symbolic form, production of

information occurs.

Information, unlike knowledge, is

an entity amenable to capture in persistent

media, sharing and archival. Information can

be in the form of end products (reports, plans,

strategies, procedures, lessons learned, etc., or

as intermediate products (memos, suggestions,

arguments, minutes of meeting, etc).

Ready for Discharge

Ready for the Physician

Patient Registered

Physician with KWSS

Ready for Treatment

Plans Modified

Commun

ica

te

EOT: End of Treatment

Follow-up Planned

Treatment Planned

Admin System In the Patient-care Facility (Hospital Ward)

Patient Discharged

Treatment Completed

Receive Instructions

EOT

Communicate(Receive Plans )

Patient under Treatment

Monitor

Condition

Condition Recorded

End Treatment

Apply

Treatment

Send Plans to

Patient-care

Facility

ins

truc

tE

OT

(Periodic/

Scheduled)

(Register Patient)(Register Patient)

(Discharge Patient)

(Wait for finish and

provide admin support

when required)

(Assign Physician)

(Receive Patient)

(Determine Treatment)

(Follow-up treatment)

(Send to

Physician)

(Complete) Review

Admit f or Treatment

Patient Admitted

Figure 2(a):The Activity Interdependency Model Source: Infosys Research

The contextualization support layerof IW consists of artifacts describing domain

vocabulary, task-structure definitions and

mappings between elements of task-structure

definitions and various types of container

objects used by the system to archive the

informational elements. There could be many

perspectives from which the structure of a

knowledge work can be analyzed [10]. This

discussion builds up on the activity-based

approach proposed by Dustdar [11]. Here task-

structure denitions comprise of two kinds of

artifacts namely the activity dependency model

and the information usage model. Activity

dependency model describes the various

interdependencies among the activities within

a task whereas the information usage model

describes the supportive interrelationships

among the informational elements associated

with the activities. Graphical representations of

the top level denitions for a patient-care KWSS


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are shown in Figures 2(a) and 2(b). The elements

of these artifacts are used to mark, annotate

and provide semantics to the instance-specic

or episodic informational elements archived in

TI archive.

Methodologies for analyzing the task-

structures and processes, and developing the

definition artifacts are beyond the scope ofthe current discussion. However, for the sake

of clarity we present the result of a high level

analysis of the patient-care task [Fig. 3].

The Task Instance (TI) Archive: The inner

layer of IW, called the task instance (TI) archive

contains the information produced through

knowledge articulation. The knowledge

reproduced is often from external sources and

is found relevant while performing the instances

or episodes of supported tasks/knowledge

Patient Consultsthe Doctor andInitiation of the

Case (in medicalsense) Occurs

DiagnosticProcedure

Diagnosis andTreatment Plan

TreatmentFollow-up

Plan

PatientCondition

Follow-upReport

Review

Making aDiagnosis

(next page)

Doctor

Makes

LeadstoMedication

and/or

Surgery

Therapy

Educationand

Instructions

Included in

Or

Imple

ment Implement

Report

Evalu

ate

Medical Care Case

Figure 2(b):The Information Usage ModelSource: Infosys Research

works. The challenge lies in organizing theinformation in TI archive of IW to leverage the

contextualization support that will provide

the workers with an improved platform

for accessing processing and producing

information. To achieve this, we strive to

impart three essential attributes to the archived

information: (i) proper granularity level, (ii)

linkage, and (iii) provenance of information.

Granularity Level: Knowledge-intensive tasks

are never monolithic or atomic in nature.

They consist of a set of interrelated activities,

often fairly diverse in nature. Knowledge

workers, whether as producers or consumers

of information, at any given point in time,

work on one activity. Thus, instead of large

body of information, as typically contained

in whole documents, it will be much easier

for the workers to work with information as

produced and consumed at activity levels.

In IW, the TI information is archived at

the level of informational elements (IE).

IE is commensurate with the activity and

informational granularity levels specified by

the task definitions. The task definitions are

available for the respective task classes in the

contextualization support layer. Further, TI-

specific IEs are persistently linked with their

corresponding definition elements.Given such an organization of archived

information, it can be easily seen that the

knowledge worker, as a consumer, can enjoy

great advantage in establishing the context

of the accessed informational elements and

utilize them with much more ease. Further,

the knowledge worker as a producer can also

articulate and produce information at the

activity level that can be easily contextualized

by the artifact supporting the task-type she is

engaged in.


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Linkage: With the maintenance of proper

granularity level of the informational elements,

creation and maintenance of proper linkage

among the informational elements can improve

the impact of IW manifold. This follows

from the well-known fact that the value or

usefulness of a piece of information increases

enormously when it can be associated with

other related pieces of information and studied

together.

For example, consider a patient-care

system. Consider a scenario where a doctor,

treating a patient, is trying to make a diagnosis

based on observed physical and clinical

ndings. Undoubtedly, mere information on the

fact that there were x number of patients who

Activity

Patient Consults the

Doctor and Initiation

of the Case

(in medical sense)

Occurs

DiagnosticProcedure

Diagnosis and

Treatment Plan

Follow-up Plan

Follow-up Report Patent Condition

Treatment

Report

Evaluate

Review

Therapy

Me

dica

tion

Anc

hor

Surgery

Educa

tion

an

d

Ins

truc

tions

Actors

Registers Creates

UsingPerforms

Makes

Informational

Elements

Medical record/file

Anchor

Creates

The Case for the

Patient's Treatment

Task

Processing

Making a

Diagnosis

On Diagnosis

The Diagnosis

Decision Treatment

Plan Workplan: List

Medication

Workplan List:

Activity, Schedule

Therapy

Workplan List:

Activity, Schedule

Instructions

Workplan List:

Activity, Schedule

Expectations/ Prognosis

Follow-up Plan List:

Expectation, Schedule

Follow-up

Report

Analysis

Store Analyze

Consulting

Treats Patient

Processes

The Case

Record

Cond

itions

ApplythePlans

Starts

Leadsto

Implement

Implem

ent

Conventional

Healthcare

Database

Review

Review

Conventional

Healthcare

Database

Patient

Receptionist

Doctor

Nurse

Nurse

Patient

Figure 3: Developed Contextualization Support Artifactsfor Patient-care KWSS

Source: Infosys Research


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had shown similar conditions, will not sufce.The doctor would want to know about the

diagnoses, prescribed treatments, success rate,

pattern in the prognosis and many more such

related pieces of information. Such a capability

needs rich navigable links among the IEs.

In IW, the links between various

informational elements within a task instance

as well as across task instances is maintained

based on several criteria so that given a piece

of information one can easily nd other related

IEs. This can help various services in the layer at

the top of IW and can serve establishing broader

context, providing support/evidence, allowing

follow-up of the usage as well as consequence of

usage of the information and many other aspects

of the information and knowledge works.

Provenance: Provenance of a piece of

information encompasses related information

on how, where, what, when, why, which and by

whom the information has come into existence.

This relates directly to the requirement of

reliability and authenticity of information that

are, as observed by Schultze, as among the most

important aspects of information other than the

content itself [13]. Further, it forms the basis of

validation of the information that is of crucial

importance in various task types. In the scenario

of collaborative works, where the information,containing some crucial arguments, prepared by

a co-worker can be examined much rigorously

using the provenance and linkage facilities

that go beyond the upfront information.

Contributions of the workers is thus likely to

be much more effective due to exchange of

information and collaboration. Apart from its

enormous importance to knowledge workers

by its own virtue, in many tasks, maintenance

of detailed provenance information is required

by various regulatory frameworks.

Creation and maintenance of provenancecan be easily achieved in IW. Due to activity

level granularity of the information and the

existence of links among the IEs as well as

between IEs and elements of task-denitions,

if the identity of the worker is consistently

maintained among the contents of IEs, one can

readily establish the provenance of an IE from

multiple perspectives. Also, IE level provenance

can be easily utilized to compute provenance at

various levels within the tasks.

Figure 4 depicts an example of TI

information as they are archived annotated by

contextualization elements.

DESIGNING THE TI ARCHIVE IN IW

At the technical design level one can envisage the

TI archive as a network of information elements

(InfoEl). The InfoEls are objects, specialized

to serve as containers of various types. The

InfoEl objects also contain specialized methods

that can assist computations commensurate

with the corresponding IE types. InfoEls are

interconnected through two types of links,

the creational links and the reference links,

and we call the organization as the creational

and referential network or CaRN view of

information [Fig. 5]. The CaRN view has the

following properties:

The CaRN view consists of task instances

(TI). In other words, TIs are the macro

units of the CaRN view.

A TI contains all information developed

during performance of a task or

knowledge-work instance and denes the

sub-network of InfoEls corresponding to

that particular TI.

A creational link joins two IEs when the

information content of one is created to

satisfy the need of creating the content


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Initial FindingsAnalysis

TestObservations

Test 2Observation

Test 1Observation

Test 3Observation

Test 2Observation

Test 1

Observation

Diagnosis (Differential)Decision Treatment

Plan Workplan(s)

MedicationWorkplan List:

Activity, Schedule

TherapyWorkplan List:

Activity Schedule

InstructionsWorkplan List:

Activity, Schedule

Expectations/Prognosis

Follow-up plan List:Expectations,

Schedule

Follow-up ReportAnalysis

Aspect 2Analysis

TestObservation

Diabetes

Blood SugarLevels

Fasting BS

Post-meal BS

MicroAlbumin Level

Test 1

Test 2Observation

Aspect 1Analysis

MetabolicCondition

Possibility 1 ActionPossibility 2 Action

Aspect 1Analysis

Weight Loss

Increase in Thirstand Appetite: Palpitation

and Weakness

Diabetes orHeart-disease or Both

Heart Disease

CardiacPerformance

Treadmill Test

ECG

BloodChemistry

Uric Acid

Condition: Diabetes,Initial Stage,

Kidney not Affected

NerveConditions

Glyco-Haemoglobin

Verify/Confirm Diagnosisand/or Record Patient

Status Analysis

Find

PossibilitiesChec

kFor

Chec

k

Inves

tiga

te

Inves

tiga

te

InvestigateInvestigate

Norma

l

Norma

l

Normal

Measure

Measure

Measure

Measure

Measure

Measurements

Measurements

Measurements

Measurements

Measurements

All withinNormal Ranges

Feedback onSuccess ofTreatment

Modify TreatmentBased on Feedback

Sing

leCon

dition

Processing the CaseMaking a Diganosis

HistoryObservation

SymptomsObservation

While processing any of the IntelliObjects theuser can search the archive for relevant pieces ofknowledge (other IntelliObjects) and navigatearound using context and relevance links to studythem. Those found useful in solving the problem(s) will be marked relevant and new relevancelinks may be created and maintained.

SignsObservation

Phys ExamObservation

Lipid Profile

Diabetic

Retinopathy

Comparison ofReported BS Levels

With Expected Values

Expected BS LevelLowering Under Treatment

Diet Control, RegularChecking of BS Level

Report to DoctorEvery Fortnight

Regular PhysicalExercise

Min. 30 Min/day

Oral Medicinesand/or Insulin Inj.

No Major Illnessin Recent Past

Aspect 2Analysis

False True

Figure 4: An Example of Task Instance Information asArchived

Source: Infosys Research


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of the other. Creational links also reect

the relationships between pieces of

information developed by activities

performed as part of the same task

instance, i.e., the creational links are

allowed to form between IEs that belong

to the same TI.

A reference link exists between two IEs

when the content of one is used, but not

explicitly created to cater to the needsof the other. Thus, the reference links

are free to cross the TI boundaries to

associate IEs belonging to different TIs.

Implementations of the archival of

information in CaRN view can be achieved

with relational databases (we have adopted this

option in our current prototype), XML databases,

object databases, content management systems

or some hybrid of them. However, irrespective

of implementation technology adopted, it can be

TI TI TI Special TI

(Policy)

Special TI

(Legal

Constraints)

Special TI

(Resource

Constraints)

A Task Instance (TI)Task

Subtask 1

Subtask 2

Soln. alt. 1Soln. alt. 2

Solution

aspect 1

Solution

aspect 2

Verification 1 Verification 2

Finding 1 Finding 2

Observation 1 Observation 2

Subtask 3

Selected

Solution Reasons

Action Plan Impact

Watch Plan

Expected

ImpactsActual

Impacts

Casual Analysis

of Deviations

Modified

Action Plan Information

Elements (InfEIs)

Creational Relationship Reference Relationship

Figure 5: The CaRN View of Information in TI Archive Source: Infosys Research

easily perceived that in this scheme, access to the

content of InfoEls and their interconnections can

provide the user with information with higher

relevance along with a capability of navigation

to other contextually related information using

the edges of the InfoEl network.

CONCLUSION

The IW embodies a powerful idea for

information archival for supporting knowledge-intensive tasks in several ways signicantly

ahead of the conventional practices. The

core facility provided by the IW for archival

of richly contextualized information opens

up enormous possibilities of building next

generation information systems for knowledge

management. At the one end of the spectrum we

can envisage building customized applications

leveraging the information in IW to support

individual tasks such as patient-care, legal

research, etc.


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At the other end of the spectrum liesa very exciting possibility. One can think of

building a technology platform consisting of the

IW and a host of interesting information access

and processing services, such as exploration,

collaboration, argumentation, recommendation,

contextualized articulation, transcription, etc.,

and most importantly a KWSS development

service for building and maintenance of KWSS,

running at the top of the platform. Given that

the platform is installed in an organization,

development service can be used for building

the denitions for new tasks and deploying

them to realize new KWSS on top of the same

platform. Further, it can enable verication and

update/improvement of performance of existing

KWSSes, resulting in overall organizational

learning. Suffice it to say, that a platform

consisting of IW can be used for building a

number of very sophisticated, inter-operating

KWSS to address the knowledge management

needs of an organization, to a large extent.

REFERENCES

1. R Maier, Modeling Knowledge Work for

the Design of Knowledge Infrastructures,

J University Computer Science, Vol 11,

No 4, 2005

2. M Alavi and D E Leidner, Knowledge

Management Systems: Issues, Challenges,and Benets, Comm. AIS, Vol 1, Article

7, 1999

3. G P Pisano, Knowledge, Integration,

and the Locus of Learning: An Empirical

Analysis of Process Development,

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2009

Whats in a Name?By Yogesh Dandawate and John Kuriakose

Recover business ontology from existing enterprise

IT assets, incrementally

Business ontology is a formal and preciserepresentation of business knowledge interms of concepts, relations and rules. Software

engineering within the enterprise involves

a translation of knowledge about the world

from abstract mental models to executable

code. Business domain, technology design and

implementation language are three distinct

knowledge domains involved in software

engineering.

The current software engineering

scenario is characterized by geographically

dispersed teams and work transition between

these teams. It is vital that the entire team has a

shared understanding of the system in terms

of the business domain, functional features,internal structure (architecture and design)

and implementation (the program code) of

the software. The team needs a more formal

knowledge representation to respond to the

current scale and complexity.

Creating this shared understanding

of applications within the IT portfolio and

its context involves making implicit and

tacit knowledge explicit in ontologies and

employing knowledge engineering methods.

However, creating formal business ontology

from scratch is not feasible because of the scale

involved and the specialized skill sets required.

This paper outlines a more effective approach

to exploit existing IT artifacts (primarily

program code) across the IT portfolio to recover

fragments of the business ontology. Users can

then respond to this initial representation and

refine it to incrementally build the business

ontology.

Information overload is a key problem

in the knowledge management arena. To

analyze raw data one consumes expensive

resources. Previous research in cognitive

science has shown that human mind creates

mental models to comprehend [1]. A developer

creates a mental model during the process ofunderstanding the software system. The mental

model constitutes of key abstractions that exist

in the code.

The concepts in a domain form the

key abstractions in the mental model. The

mental model resides in the head of the

expert. In the current software development

scenario where there is continuous transition

of developers, the knowledge gathered about

the system should be made available to each

stakeholder. It is therefore essential that


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one captures the knowledge of the expertsformally and utilizes it for the benefit of

lesser experienced developers. This will help

reduce the comprehension time as well as

spread uniform understanding among the

developers.

Knowledge engineering (KE), an offshoot

of articial intelligence (AI), has been working

to resolve the problem of formal knowledge

representation. Research in this area suggests

that one needs domain ontology for background

knowledge, a knowledge base (KB) to store

information and an inference mechanism to

comprehend.

OBSERVATIONS

Problem domain or business domain concepts

are represented using programming language

concepts:

e.g., Concept::Account Class::Account

Some key problems that prevail in

software engineering and hinder comprehension

of code are:

Concept Scattering: Single concept in

business or problem domain gets represented

in multiple artifacts in multiple programming

languages.

Missing of Semantic Links between

Program Artifacts: Concept attributes across

multiple languages share same value spaces. Forinstance, a java program uses values of database

location that is available in a XML le. Hence

the database location is dened in a XML le

whereas the consumer of the information is a

java program. This multi-language-multi-artifact

dene-use leads to semantic integrity issues.

During program comprehension the

developer attempts to learn or gain knowledge

about various aspects of the program. This

involves learning new knowledge concepts

as well as mapping linguistic terms in the

program text to concepts in one of the domains.The programming language domain or the

implementation domain, architecture and

design domain and business or problem

domain are the three distinct knowledge

domains involved in software engineering

[Fig. 1].

A formal representation of knowledge in

a domain consists of concepts and relationships

in that domain and this is what is known as

Ontology[2, 3].

Ontology can be seen as a shared

agreement to represent knowledge withina community of stakeholders as a formal

representation.

Ontology, therefore, has a social

dimension of agreement and commitment

from the members of the community to what

is represented and also a formal dimension of

machine interpretation with precise semantics.

Thus, ontologies form the foundation for building

integrated knowledge repositories or knowledge

bases that capture shared understanding within

a software engineering team.

DomainOntology

Architectureand Design

Ontology

ImplementationLanguageOntology

Figure 1: Multi Domain OntologiesSource: Infosys Research


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Recently, the use of ontologies toaddress some of these knowledge management

problems within software engineering has

been proposed [4]. One of the specic software

reverse engineering problems we address is

the recovery of the business domain concepts

and relations from the structured program

artifacts. Previous work in this eld of ontology

learning has primarily focused on the web and

unstructured text corpus as data sources [5].

CREATING ONTOLOGY

Two primary reasons that inhibit clean slate

ontology creation are:

Existing ontology languages present a

high barrier of entry that inhibit their use

and deployment in the enterprise context

The enterprise already has amplestructured and unstructured information

that can be mined to learn the business

ontology rather than attempting to create

it from scratch.

Our discussion brings forth an approach

to exploit structured data sources within the

enterprise IT portfolio that include program

artifacts, structured web content, web services

descriptions, XML and messaging artifacts,

database schema and business process models

to recover elements of the business ontology.The key idea presented is to extract and exploit

the identifier names within the respective

formal languages and apply lingo-syntactic

patterns that govern the composition of

identier names from basic tokens.

APPROACH

Previous studies have already established the

contribution and role of meaningful identiers

within program code in comprehension [6, 7].

Research indicates that almost 47-62% of the

development time is spent in understanding

the previously written code [8].

An attempt is made to build an extensible

knowledge base that is capable of aspects like

extracting identifiers from the code base and

tokens from identifiers, storing identifiers,

tokens, programming language elementsand syntactic relations between the program

elements. It should also be able to analyze

how multiple tokens are composed to form

program identifier names and exploit syntactic

rules within the language to identify language

relations between the tokens. Other features

of the extensible knowledge base should

include features viz., leveraging existing

machine processable semantic lexicons for

disambiguation of the meaning of a token in

the context; identifying basic linguistic tokens

Auto recovery of ontology from the formal software artifactsshould be seen as a feasible approach than creating ontology

from scratch


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that model business concepts and separatingtokens from business and technology domain;

and applying axiomatic token rules to identify

concept tokens and relations between them.

CERTAIN FACTS ABOUT IDENTIFIERS

Object oriented program code is composed of

classes, methods, variables which are qualied

by identiers.

Deibenbock and Pizkas work in the

past highlights facts that state that about

70% of the code comprises of identiers and

every programming language available today

allows use of arbitrary sequence of characters.

There exists no mechanism available to check

if the identier names are meaningful. They

also opine that consistent and well-formed

variable names, can improve code quality and

that programming guidelines have naming

conventions intended to improve the readability

of the code. Identier names are intended to

communicate the concepts that they represent.

This helps comprehension and maintenance.

Also, multi programmer development does not

guarantee uniform usage of identier names.

One nds synonyms of the concepts being used

heavily. Thus, single concept gets represented

with multiple names in the code. Programming

styles of programmers also create additional

challenges during identifier analysis. Theprogrammers tend to use prexes like I for

interfaces, or may use sufxes like impl for

pure implementation of the types [6].

Recovering the mapping from program

or linguistic lexical tokens to business concepts

that are vehicles of domain semantics is the goal

of our work.

THE RECOVERY PROCESS

A eight phased approach as has been detailed

below has been adopted for ontology extraction.

Identier Extraction: Extractors are built forparsing the programming language artifacts

and the facts are extracted. Reverse engineering

techniques are used for extracting data. The

identiers are part of the facts that are extracted.

Every extracted fact is augmented with the

concept information to which it belongs. For

e.g., sample code:

C l a s s S a v i n g A c c o u n t e x t e n d s

AbstractAccount

{}

Extracted Identiers:

SavingAccount

AbstractAccount

Extracted Relations:

InstanceOf(AbstractAccount, Class)

IsNameOf(AbstractAccount, Class)

InstanceOf(SavingAccount, Class)

IsNameOf (SavingAccount, Class)

Extends(SavingAccount,

AbstractAccount)

Token Extraction: Identiers or terms comprise

of one or more words or tokens. Identiers

are separated either by some separator (e.g.,

underscore, periods) or camel case (e.g.,

AbstractAccount).

For e.g.,

SavingAccount = {t0: Account, t1:

Saving}

AbstractAccount = {t0: Account, t1:

Abstract}

Token Filtering and Validation: WordNet

database of English is used for separating valid

and invalid tokens [6]. The tokens that have

a meaning are valid tokens and are tagged

appropriately.


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Acronym Generation: A list of possibleacronyms/abbreviations is generated for every

valid token detected by WordNet. Criteria for

acronyms are that they begin with the same

letter as the token, maintain letter sequence and

are three letters or longer.

For e.g., Token {Tok, Toe, Ton, Tke,

Tkn, Ten, Toke, Tokn, Tken}

Invalid Token Analysis: Invalid tokens are

mostly popular acronyms of some valid word.

The knowledge base is queried if this invalid

token is acronym of some word. A table is also

maintained where most common acronyms

(also called as stop list) are available, for e.g., str,

int, lang, acnt, etc [7]. This table is also queried

to convert the invalid token to valid meaning

word. If there are multiple possible expansions

then the token with highest frequency isselected.

Valid Token Analysis: Every token is assigned

a part of speech using WordNet. Noun,

adjective, verb, adverb are of our typical

interest as they follow certain grammatical

rules. Identifiers may have multiple parts

of speech. This can be referred to as parts of

speech disambiguation.

In addit ion, a unique stemming

algorithm, also known as stem finder is used

for identification of the stems of each token.The tokens are clustered based on the stem.

Each stem that is discovered by the above

process represents concept in either the

software technology domain or the business

domain. For e.g., Order is stem available in

identifiers:

PurchaseOrder,

SupplierOrder,

ProxyOrder,

ServiceOrder,

CompletedPurchaseOrder,

ShoppingcardEmptyPurchaseOrder,

MailPurchaseOrder,

UnknownPurchaseOrder,

PetStorePurchaseOrder,

AdminServicePurchaseOrder

Stem Filtering: This stage requires programmingdomain concepts to be captured as a stop

list. Post ltering, the domain concepts can

be recovered and can be suitably tagged.

Knowledge-base post ltering has mapping

of domain concepts to the artifact in which

they reside. This information is crucial while

performing impact analysis.

Applying Empirical Rules over the Recovered

Concepts: The last business noun (BN) in a

homogenous sequence is a major concept.

The token position and application ofempirical rules aid in recovery of ontological

concepts and relations


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For e.g., createSavingAccountEJB.

The last business noun is Account and

hence the major concept.

When rst token is a Verb it indicates

action (domain/ technology)

e.g., transferFunds() -> {transfer, Funds}

(transfer i

Documents

Knowledge Engineering Management