ABSTRACT:
Interactions spanning multiple
organizations have become an important aspect in today’s collaboration
landscape. Organizations create alliances to fulfill strategic objectives. The
dynamic nature of collaborations increasingly demands for automated techniques
and algorithms to support the creation of such alliances. Our approach bases on
the recommendation of potential alliances by discovery of currently relevant
competence sources and the support of semi-automatic formation. The environment
is service-oriented comprising humans and software services with distinct
capabilities. To mediate between previously separated groups and organizations,
we introduce the broker concept that bridges disconnected networks. We present
a dynamic broker discovery approach based on interaction mining techniques and
trust metrics.
EXISTING SYSTEM:
While existing platforms only support
simple interaction models (tasks are assigned to individuals), social network
principles support more advanced techniques such as formation and adaptive
coordination.
PROPOSED SYSTEM:
Our approach is based on interaction
mining and metrics to discover brokers suitable for connecting communities in
service-oriented collaborations. The availability of rich and plentiful data on
human interactions in social networks has closed an important loop, allowing
one to model social phenomena and to use these models in the design of new
computing applications such as crowd sourcing techniques .A wide range of
computational trust models have been proposed. We focus on social trust that
relies on user interests and collaboration behavior. Technically, the focus of
BQDL is to provide an intuitive Mechanism for querying data from social
networks. These networks are established upon mining and metrics.
MODULES:
Supporting the Formation of Expert Groups:
Successfully performed compositions of
actors should not be dissolved but actively facilitated for future
collaborations. Thus, tight trust relations can be dynamically converted to
FOAF relations (i.e., discovery of relevant social networks)
Controlling Interactions and Delegations:
Discovery and interactions between members
can be based on FOAF relations. People tend to favor requests from well-known
members compared to unknown parties.
Establishment of new Social Relations:
The emergence of new personal relations is
actively facilitated through brokers. The introduction of new partners through
brokers (e.g., b introduces u and j to each other) leads to future trustworthy
compositions.
ALGORITHM:
PAGE RANK ALGORITHM:
This can be
accomplished by using eigenvector methods in social networks such as the Page
Rank algorithm to establish authority scores (the importance or social standing
of a node in the network) or advanced game-theoretic techniques based on the
concept of structural holes.
Consider two initially disconnected
communities (sets of nodes) depicted as variables var source = {n1, n2, . . . ,
ni} and var target = {nj , nj+1, . . . , nj+m} residing in the graph G. R1: The
goal is to find a broker connecting disjoint sets of nodes (i.e., not having
any direct links between each other). A1:
Two sub graphs G1 and G2 are created to
determine brokers which connect the source community {u, v, w} with the target
community {g, h, i}. O1: The output of the query is a list of brokers
connecting {u, v, w} and {g, h, i}. Specify the input/output parameters of the
query. D1: As a first step, a (sub) select is performed using the statement as
shown by the lines 6-11. The statement distinct (node) means that a set of
unique brokers shall be selected based on the condition denoted as the Where
clause with a filter. The term ‘[1...*] n in source’.
HARDWARE
REQUIRED:
System : Pentium
IV 2.4 GHz
Hard Disk : 40
GB
Floppy Drive : 1.44
MB
Monitor
: 15
VGA color
Mouse : Logitech.
Keyboard : 110
keys enhanced
RAM : 256
MB
SOFTWARE
REQUIRED:
O/S :
Windows XP.
Language : Asp.Net,
c#.
Data Base : Sql
Server 2005.
No comments:
Post a Comment