CONTEXT AWARE COLLABORATION IN ENTERPRISES

Harish Kammanahalli, Srividya Gopalan and Sridhar V

Applied Research Group, Satyam Computer Services Ltd., #14 Langford Avenue, Lalbagh Road, Bangalore, India

Krithi Ramamritham

Indian Institute of Technology-Bombay, Powai, Mumbai 400 076, INDIA

Keywords: Context, Collaboration, Agents

Abstract: Providing the most relevant information at the most appropriate time at the most appropriate lo

cation helps

in improving the overall enterprise productivity. In this paper, we discuss the various aspects of a context

and the ways and means of tracking the same so as to exploit the most recent and expectedly accurate

description of the business situation in delivering the information to assist in collaboration. Further, we

discuss the role of data and app grids in meeting the real-time delivery requirements.

1 INTRODUCTION

To build products and services of highest quality in

challenging time period and lower production costs

that enterprises need to collaborate with partners /

suppliers (O’Keefe 2001). With advancement of

technology as the driving force, virtual

collaborations are increasingly being adapted across

enterprises. Several commercial collaboration tools

like eRoom, NetMeeting are available.

While the utilities of intra-organization and inter-

rganization collaboration activities are desirable,

there are several factors that impede its practice.

These factors comprise both technological as well as

social issues. Heterogeneity - in communication

networks, communication devices, and information

sources across organizations imposes challenges on

creating effective collaboration technology. Also,

collaboration essentially requires large-scale video

and audio conferencing, transcoding to facilitate

communication through devices with diverse

characteristics, and such applications need high

computational resources. Further, with advancement

in mobile communication, service representatives

who are frequently travelling require coordinated

collaboration across departmental and corporate

boundaries for sustaining competitive advantage and

reducing business processing cycle times. As a

result, large enterprises are challenged to

simultaneously provide a unified solution to support

enterprise integration, mobile applications, and

collaboration. Such technological complexities in

large-scale multi-party collaboration could be

handled using grid technologies (Hinde S et al

2002). Grid technology provides highly efficient

resource sharing mechanisms in addition to security,

reliability, and fault tolerant mechanisms in

heterogeneous resource environments. Data grid

(Chervenak A et al 1999, Avaki 2003) provides an

efficient, scalable access to real-time data that is

globally distributed and in different formats. Data

and computational grids provide an easy way of

handling heterogeneity issues as well as

computational issues.

Technology enabled on-line collaboration

requ

ires systems that keep track of all activities,

often real-time events — providing real-time

visibility and control of information by the

collaborators involved. Technology provides several

support systems like ERP, CRM, and several

workflow systems to coordinate tasks that aid

enterprises in procedurally oriented transactions.

These systems coordinate asks, but do not provide

support for individuals or groups working with

them.

Effective knowledge sharing enhances the

efficiency of colla

boration (Perkins J et al 2003).

Kammanahalli et al (Kammanahalli H et al 2003)

have discussed a system that facilitates effective

interactions during a synchronous collaborative

443

Kammanahalli H., Gopalan S., V S. and Ramamritham K. (2004).

CONTEXT AWARE COLLABORATION IN ENTERPRISES.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 443-446

DOI: 10.5220/0002651804430446

 SciTePress

session by using proactive triggering based on

context (Abowd G. C et al 1999) information.

This paper highlights our solutions to address

effective context relevant knowledge management

through a system sitting on top of the grid

architecture, comprising data and app grids, to

facilitate effective collaboration. Since information

is accessed from different business scenarios there is

a need for contextual information. The knowledge

sharing among collaborators is achieved through

multiple collaborating agents that are specifically

related to the various aspects of a context in an

enterprise.

2 CONTEXT IN ENTERPRISES

A typical user and data perspective of an enterprise

is depicted in Figs. 1. An overall network of an

enterprise (a) covering globally distributed offices,

and (b) to provide global access to enterprise

resources for its employees comprises multiple,

heterogeneous sub-networks. Data is distributed

over these sub-networks and in order to provide

efficient, real-time, and ubiquitous access to data, it

is appropriate to set up dedicated data grids. There

are two distinct kinds of grids, namely, Department

data grids and Enterprise data grid. Department data

grids are specific to the different locations of the

enterprise and the enterprise data grid interconnects

these multiple Department data grids to form a

single, connected data network. Data grids are useful

especially during audio and videoconferences

demanding real-time access to the globally

distributed data. We propose multiple, collaborating

agents to provide context-aware access to the data

stored in data grids. The agents interact with the data

grid managers to efficiently retrieve data. In the

following, we provide a brief description of an

enterprise context to help provide the most relevant

information at the most appropriate time to the staff

of the enterprise. Context, as described below,

consists of two parts, namely, business context and

user context.

Figure 1: User Perspetive of an Enterprise

LAN

Enterprise IP

Network

Internet

WLAN

3G NW

Laptop

PDA / Communicator

W orkstation

Business Context (BC): During collaboration

across an enterprise, it is very important to integrate

information sources that capture all the activities of

the domain across the organization. This integration

provides different views of the enterprise, relating

relevant tasks to be performed by the tools that

support them and also, establishing connections

between the information sources themselves.

Enterprise ontology provides a set of terms covering

the various activities of the enterprise. Ontology,

that is a communication medium, helps in resolving

the ambiguities and creating shared understanding of

relevant aspects of business enterprise. Ontology, in

an enterprise, contains definitions of several

business aspects that include information about

people and also information about computational

systems across the enterprise. The complete list of

enterprise ontology as given by AIAI (The

Enterprise Ontology) can form the basis for the

definition of Business Context so as to succinctly

capture the current business situation.

User Context (UC): User’s context represents the

user’s current situation. UC contains user’s Current

context (CC), defined by the user’s current activity

and the entities associated with it, and analysed

context (AC) gives detailed information about user

preferences. Analysing and aggregating user’s past

contexts generates AC. We consider an activity as a

discrete event, for example, receiving an email or

attending a meeting. Each context is associated with

the following entities: activity being performed, user

identity, user’s role, location of the user, data being

accessed, device being used, time, network

communication channel, and network condition.

Together with business context and user’s

context, the entire enterprise context is captured.

Business context provides the most recent and

relevant information, from all of the concerned

information sources in the current business situation

amongst the collaborators, while user’s context

provides information about which business

information is relevant to the user and how best it

could be presented to the user.

3 CONTEXT IDENTIFICATION

In order to provide the most relevant information at

the most appropriate time for a staff of an enterprise,

it is essential to track the context associated with the

staff on a continuous basis. This is achieved by

separately tracking each of the context categories. A

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generic, feedback-oriented context identification

approach is depicted in Fig. 2.

Observe that the contexts related to the business

aspects and user-specific aspects are separately

extracted and combined to arrive at an enterprise

context.

Business context is obtained based on (a) nature

of query inputted by the staff; (b) active tasks part of

a workflow; and (c) business schema providing

relationship among various business objects and

mapping between tasks of a workflow and the

business objects.

User context is obtained based on (a) the current

role of a staff; note that a staff can play multiple

roles at different times; (b) the current designation;

this plays a role in the kind of information that can

be accessed and provided; (c) the user profile

indicating the user-specific requirements; and (d) the

user activities that help relevance-based ordering of

the information.

Analyses and aggregation of past contexts helps

to derive the implicit information and delivery needs

of users pursuing a particular activity and the format

of presentation of this information in the current

context can be captured. This information is used for

context-aware information retrieval (Kammanahalli

et al 2004).

4 MULTI-AGENT

COLLABORATION

We identify distinct agents for each of the context

categories in an enterprise. There are basically six

broad categories resulting six class of agents: S

agents for tracking sensors, P agents for tracking

user presence on a network, W agents for tracking

user activities with respect to a workflow, A agents

for tracking user activities, N agents for tracking

agenda items, and G agents for tracking a goal.

Agelets (Slets, Plets, and so on), instantiated version

of these agents, are distributed over the enterprise

network and they constantly track and provide the

changed information to their respective servers. This

is depicted in Fig. 3. Observe the both raw data and

the analysis results are store in a distributed manner

over the enterprise network. For example, SDB

corresponds to sensory data and PDB corresponds to

presence data (refer to Fig. 3). Agelets can be very

thin and can just provide the raw data to the servers.

However, in certain cases, especially to reduce the

load on the enterprise network, the agelets can

undertake some pre-processing before

communicating the data to the servers. We further

discuss this issue later in this section.

Figure 2: Context Extraction

Business

Objects

Management

Business

Schema

Management

Business Apps

Business

Context

Extraction

User / System

Query

Business

Workflow

Management

User Context

Extraction

User Role

Tracking

User

Designation

Tracking

User Profile

Tracking

User Activity

Tracking

Enterprise

Context

Query

Generation

Distributed

Agents

Figure 3: Agents and Servers

Enterprise Network

A: Location agent

B: Access control agent

C: Camera agent

D: Login agent

E: Call agent

F: Workflow agent

G:Doc agent

H: Mail agent

I: Discourse agent

J: Meeting agent

K: Complaints agent

L: Issues agent

M:Invoicing agent

N: Video conf agent

O:Audio conf agent

P: Collab Agent

SDB

PDB

ADB

NDB

WDB

GDB

Distributed

Servers

Sensory Data

Analysis

Goal Data

Analysis

Workflow Data

Analysis

Agenda Data

Analysis

Activity Data

Analysis

Presence

Discovery

In order for agelets to undertake some pre-

processing, the data and the available applications

within the enterprise are distributed over data grid

and app grid respectively. Observe that the app grid

contains the enterprise application resources for

sharing efficiently the same across the enterprise.

Such paired grid architecture helps agelets (that need

to operate in real-time) to obtain raw data, get

additional data from the data grid, use the

appropriate application for pre-processing and

required level of analysis, and finally communicate

the results to the appropriate DS for context tracking

purposes. The collaboration among multiple agents

can be either explicit (that is, collaboration in

response to a user request) or implicit (that is,

collaboration to achieve a system task). An example

of these two kinds of collaborations is provided in

Fig. 4.

5 DISCUSSION AND

CONCLUSION

There is several business scenarios wherein people

involved collaborate and negotiate to arrive at a

decision. In such situations, providing the right

CONTEXT AWARE COLLABORATION IN ENTERPRISES

445

information for each of the participants helps carry

out their tasks more effectively thereby enhancing

the overall enterprise productivity. Considering a

business scenario in which a group consisting of a

purchase manager, an R & D manager, and a finance

manager are meeting to negotiate on the

procurement of items necessary for a research

project requires that the purchase manager is well

prepared to answer the queries from both the R & D

manager and the finance manager. The required

information to help the purchase manager is

obtained both from the internal information sources

and the external information sources. As described

in this paper, both business context and user context

are used to retrieve the most relevant information

from the multiple information sources. Further, as

the meeting progresses and issues get discussed in

depth, the context changes and additional

information needs to be retrieved and provided to

enable the purchase manage to continue to be

effective. To achieve this real-time provisioning of

information, that may also involve real-time analysis

to be performed, combined data and app grids seem

to be very appropriate.

Figure 4: M ulti-Agent Collaboration Scenarios

Implicit multi-agent collaboration

Explicit multi-agent collaboration

Clet1

Nlet1

Goal: To satisfy the customers

0. Obtain the current goal

1. Obtain the relevant info for the next

agenda item

2. Submit a query to EIS to obtain the list of

participants and their issues

3. Obtain the information

4. Obtain the information

5. Request for the possible locations

locations of the participants

6. Obtain the information

7. Request for the necessary documents,

emails, and discussion notes;

8. Obtain the information

Glet1

Slet1

Alet1

Clet1

Nlet1

Slet1

Plet1

Goal: To set up a video conference

1. Interact to obtain agenda of the participants

2. Interact to obtain physical location information of the

pa rticip a nts

3. Interact to obtain the information related to the presence on

the network

4. De term ine (a) availability o f the participants; (b) possible

locations; (c) possible network configurations; and (d) possible

devices for setting up of the video conference

5. Interact with Clet of participant 1 to agree on video

conference parameters

6. Interact with Clet of participant 2 to agree on video

conference parameters

7. Interact with Clet of participant 3 to agree on video

conference parameters

8. Interact with system to set up the video conference on the

agreed parameters

Clet1

System

In this paper, we have discussed the various

aspects of the notion of context in an enterprise and

have described approaches for tracking the various

aspects of the context in order to provide the most

relevant information at the most appropriate time.

We are working on extending a presently under

development system for enterprise related business

scenarios.

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