Web-Based Collaboration Technology

Abstract -Now a day, web-based collaboration technology
becomes a key technology for a business. Such technology
spans the interactions between people and services across
the world. Service oriented system provides the mean of
collaboration. Organization implements various business
processes by using it. But flexible interaction model is the
basic requirement of every developing business. Service
oriented system is limited to provide that flexible model. To
get the flexibility, human must be the part of the system.
The environment in which human take a part to solve a
problem is referred as mixed service oriented system and
services provided by people are referred as Human
provided services (HPS).In this environment, people
offer their skills and capabilities as services over the web.
This human participation makes the model flexible.
Moreover to get the best service from mixed service
oriented system, discovery of right person is the challenging
task. For this purpose, an algorithm ExpertHITs which
supports the dynamic trust in collaborative network has
been introduced. Trust defines the behavior of human in
mixed service oriented system. The concept is inspired by
the concept of hub and authorities in web based
environments. This paper focuses on a study of the
importance of human and his behavior in service oriented
system.

Keywords: Mixed service oriented system, human provided
services, trust, Hub and authority
I. INTRODUCTION
For management purpose, many organizations use multiple
software systems. These different software systems need to
exchange data with each other. A web service is a method of
communication that allows two software systems to exchange
this data over the internet. Collaboration is a process where
two or more people or organizations work together to realize
shared goals and achieve that goals. Adopting web based
collaboration can put organizations in a better position to
address constraints and opportunities. Service oriented
architecture (SOA) acts as a collection of services in which
each service is a self contained unit of functionality. SOA
facilitates the communication among them via either simple
data passing or it could involve two or more services
coordinating some activity, requiring means of connecting
services to each other. Thus web services and SOA
technologies serve as the key technologies to get success in
business environment.
Flexible, dynamic interaction model is another requirement
of the organization. Because every time, there are changes
into the business process. By using a simple service oriented
system it is not possible to get flexibility, adaptability. It has
been realized that SOA is limited to provide a dynamic
interaction model which is required for every developing
business. For a success, human must be the part of the
system. Participation of human in SOA provides the better
solutions over the problem
Thus the environment in which human can take a part to
provide a service is referred as mixed service oriented
system. In this system people can offer their skills and
capabilities as services over the web. These human provided
services (HPS) enable flexible interactions in service oriented
system. Moreover to get the best service, it is necessary to
discover best or right actor from a system. For this purpose,
study of the behavior of human in the system becomes
essential. Dynamic trust affects the human interaction within
a system. This paper focuses study on the importance of
human and his behavior in mixed service oriented system.
II. MOTIVATING SCENARIO
A. Process modelConsider the simple task that is to produce a software
module. The required process model is presented in figure
2.1 First task is common requirement analysis. Finishing the
requirement analysis reusability of existing work carried out.
Then architecture can design the framework. The
implementation task is carried out by a software Developer
and at the end software testers evaluate the prototype
implementation. [1]

Fig. 2.1 Process Model
Study on Human Behavior in Mixed Service
Oriented System
Ashwini Mone
1
, Hari K.Chavan
2
1.2
Department of Information Technology,
Terna Engineering College, Nerul, Navi Mumbai, India
However in developing business, requirements are frequently
changes. To adopt the changes business requires the flexible
interaction model. But this existing process model does not
support the flexibility concept. To get the flexible interaction
model we go for mixed service oriented system where people
offer their skills and capabilities as services commonly
referred as Human Provided services.
B. Manual DiscoveryManual discovery is another approach .It supports the offline
interaction analysis. The expert seeker starts asking
information for an expert by asking other people for their
opinion or to provide recommendations are performed by
asking friends or colleagues who are all faced similar
problems in the past. A person needs to know trusted experts
and what data needs to be exchanged to solve a particular
problem. The drawback is that people need extensive
knowledge about the skills of colleagues and also if number
of people increases the task will be very difficult.
III. EXPERT WEB
To overcome from the drawbacks of traditional process
model we go for the Expert Web [1]. Expert web supports
the concept of distributed collaboration. It is an online
platform consisting of connected experts. Every Expert acts
as a member of web and provides the help and support
services. A member of the Expert Web may receive an
Request For Support(RFS) and delegate work to some other
member in the network (characterizing hubs in the
network).Receivers of the delegated work, however, expect
RFSs fitting their skills and expertise (i.e., being an authority
in the given domain). Careless delegations of work will
overload these peers resulting in degraded processing time
due to missing expertise. For this, within the Expert Web,
authorities give feedback to hub using rating mechanism
(e.g., a number on the scale from 1 to 5) to indicate their
satisfaction'whether a particular hub distributes work
according to their skills and interest. Thus, participation of
experts provides the better services than only software based
services.
IV.EXPERTISE MODEL
In this section we will present the discovery of Expert from
expert web. For a given query context, who is the Expert i)
satisfying the demanded skills and ii) how well that member
is connected to other members in Web. So first job is finding
the members of expert web related to given query context.
A. Skill model
Tree structure helps to discover members of expert web
related to demanded query context. Tree organizes the
members in well fashion. Different weights are assigned for
each level in the tree. The top-most level (the root node) has
the lowest weight since top levels in the skill tree denote
broad areas of expertise. The weights increase depending on
the tree depth because lower levels contain fine-grained skill
and expertise information. All nodes in the skill tree that do
not have successor nodes are called leaf nodes. By matching
the skills, members are discovered. Interactions between the
members of expert web mainly depend on trust factor.
B. Trust
Trust in the expert web reflect the expectation of one expert
has about another's future behavior to perform delegated
RFSs dependably, securely, and reliably based on
experiences collected from previous interactions[1]-[3].Means after the arrival of RFS, member can either
process the RFS or delegate the RFS. Delegation of RFS
mainly depends on trust factor. Member can delegate the
RFS only to the member who is competent and trustworthy.

C. Hub and Authority
Hub and authority is the concept of web application. A good
hub is a page that pointed to many other pages and good
authority is a page that was linked by many other different
hubs. Same concept is utilized in Expert web[6]. When a
member of expert web delegates the RFS to other member,
sender member acts as a hub while receiver member acts as
an authority. After receiving the RFS, receiver member
checks that the arrival RFS is fitted with his skills and
expertise area. Depends on this, Receiver gives the feedback
using rating mechanism to indicate their satisfaction whether
a particular hub distributes work according to their skill and
interest. Thus for each member of Expert web, we calculate
the hub score and authority score. The member who has
highest score can act as Expert in that domain. Thus, a good
hub is characterized by a neighborhood of peers that are
satisfied with received RFS while delegation of RFS is
strongly influenced by trust.
Figure 4.1 shows Expert web scenario [1]. There is a
demanded query Q
A
.The purpose of a query is to return a set
of experts who can process RFSs, either by working on the
RFSs or delegation. Thus, Q
A
would returns H
A
as the user
who is well connected to authorities in query context Q
A
.
There are two influencing factors, i.e., relations, determining
hub- and authority scores:
1) How much hubs trust authorities (depicted as filled arrows
from hubs to authorities) and
2) Ratings hubs receive from authorities (open arrows to
hubs). Trust mainly influences the potential number of users
(e.g., known by H
A
) who can process delegated RFSs. On the
other hand, receivers can associate ratings to RFSs to express
their opinion whether the delegated RFSs fit their expertise.
Fig. 4.1 Expert Web
The algorithm can be explained as follows-Input: Given a query context Q to discover expert hubs.
1) Find experts matching demanded set of skills.
2) Start from the root node and match the query to the root
node.
3) Iterate through each level and calculate overlap similarity
of property in query at current level i. If the node will be
empty then go to next node.
4) Calculate hub-expertise of expert given query context Q,
for each expert calculate hub score. Hub score can be
calculated as the rating through authorities based on
delegation behavior.
5) For each expert calculate authority score. Authority score
can be calculated as the rating through hubs based on
reliability in processing delegated tasks.
6) Ranked expert are listed.
Output: Ranked elements [6].
V. PERFORMANCE EVALUATION
The above Expertise model provides the required dynamic
and context-based interactions model [2]. It involves the
interaction between peoples who are expertise in given query
context. Model provides the run time adaption with following
aspectsi)Discovery of Network Members and Resources-In many
networks, for example social networks, the discovery and
selection process relies on matching of user profiles and
resource features that are mainly static. In contrast, utilizing
periodically updated trust relations better accounts for
varying user preferences and avoids lookup based on stale
information.
ii)Access to and Sharing of Information-Traditional
approaches to access rights management are based on
manually assigned static user roles. However, the user is
often not able to keep track of configurations in complex
networks such as dynamically changing roles.
iii)Coordination and Compositions-Especially in flexible
environments, compositions of humans and services cannot
only rely on static structures, but have to be flexibly adapted
based on their run-time behavior.
iv) Interaction Policies and Patterns-In common enterprise
networks, policies and interaction patterns describe and
restrict communication paths between network members.
Therefore, periodic adaptation upon ongoing collaborations
enables optimizations according to the outcome of
interactions.
VI. CONCLUSION
Emerging service-oriented platforms no longer operate in
closed enterprises. An increasing trend can be observed
towards temporary alliances between companies requiring
composition models to control and automate interactions
between services. The resulting service-oriented application
needs to be flexible supporting adaptive interactions. Mixed
service oriented system provides the adaptive interaction
model. People can offer their skills and capabilities as
services on demand. This model is based on dynamic trust
concept. By delegating the RFSs to trusted members load
balancing and scalability goals of business are achieved. This
paper acts as basis to study how the people work together in
mixed service oriented system and how they provide a
qualitative service by discovering expert from collaborative
work.
REFERENCES
[1] Daniel Schall, Florian Skopik, and Schahram Dustdar,
'Expert Discovery and Interactions in Mixed ServiceOriented Systems', IEEE transactions on services computing
,VOL. 5, NO. 2, April-June2012
[2] Florian Skopik, Daniel Schall, Schahram Dustdar,
'Modeling and mining of dynamic trust in complex serviceoriented systems', publish in Elsevier journal in 2010
[3] Florian Skopik, Daniel Schall, Schahram Dustdar, 'The
Cycle of Trust in Mixed Service-oriented Systems',
European Union through the IP project COIN (FP7-216256).
[4] J. Zhang, M.S. Ackerman, and L. Adamic, 'Expertise
Networks in Online Communities: Structure and
Algorithms,' Proc. 16
th
Int'l Conf. World Wide Web (WWW
'07),pp. 221-230, 2007.
[5] E.Thivya, P.Outhirai, R.Deebiga and V.Aileen Emeld,
'Mixed Service Oriented in Expert Discovery and
Interactions System', International Journal of Futuristic
Science Engineering and Technology, Vol 1 Issue 4 April
2013ISSN 2320 ' 4486
[6] S. TAMIL SELVAN and S. ARUN, 'User guided web
based interaction in mixed service oriented system',
International Journal of Computer Science and Mobile
Computing, IJCSMC, Vol. 2, Issue. 7
[7] Florian Skopik, Daniel Schall, Schahram Dustdar, 'Trustbased Adaptation in Complex Service-oriented Systems'
[8] Florian Skopik, Daniel Schall, Schahram Dustdar,
'Trustworthy Interaction Balancing in Mixed Serviceoriented Systems'.
[9] S. Srivalli, B. Bhargavi, S.Srinivasulu , 'Cognoscent
invention for interactions in mixed service oriented systems',
International Journal for Research in Science & Advanced
Technologies , Issue -2, Volume-5, 198-209
[10] J.Sowmya Lakshmi, Dr.R.V.Krishnaiah, 'Expert
Discovery and Interactions in Mixed Service Oriented
Systems', International Journal of Advanced Research in
Computer and Communication Engineering
Vol. 2, Issue 8, August 2013.

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