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Friday, 2 February 2018

Bridging Socially-Enhanced Virtual Communities(2011)

Bridging Socially-Enhanced Virtual Communities(2011)

  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.
   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.
     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.
1)    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)
2)    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.
3)    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.
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.

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