TECHNICAL FIELD
The present disclosure relates to capturing and sharing information in a distributed network. More particularly  the embodiments of the disclosure relate to a method for identifying information needs of different nodes in a distributed network and to create an information sharing path between the nodes.
BACKGROUND
One important emerging class of problems in connected networks involves relying on expertise of individual nodes to find responses to specific information needs. Within this context  one can assume that any two nodes (for example people) in a connected network are connected by one or more paths (a concept similar to "six degrees of separation" property). Moreover  expertise tends to be distributed throughout a connected network such that  for any information need  there are one or more nodes within the network for which  partial or full answer to the query is easily at-hand. Thus  in general  there exists  for most queries  one or more nodes at varying distances from the query originator node  which has full or partial answer to the query. The problem  however  is that while a path to a query""s answer node(s) may exist within the connected network  that path is typically hard to identify. Moreover  individual nodes in the network do not have a mechanism to identify  capture and publish the nodes implicit and explicit expertise  which is constantly evolving  due to continuous learning by individual nodes.

The current technologies have limitation in identifying implicit and explicit information needs of each node in the distributed network  in capturing implicit or explicit information expertise of the nodes  in match-making between nodes with information needs and one or more nodes which can individually or jointly fulfill the information need  and in creating an information transfer/communication path between the nodes so that information can flow between the nodes.
Hence  there is a need for a method to identify information needs of different nodes in a distributed network  for deriving and capturing the information expertise of individual nodes  for match-making between node with information need and one or more nodes which can individually or jointly respond to the query and for creating an information sharing path(s) between the node which requires an information and one or more nodes which has responses to the query.
SUMMARY
The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a method and a system as described in the description.

In one embodiment  the present disclosure provides a method for capturing implicit  explicit and ever evolving (due to continuous learning) expertise (information knowledge) of each node in a distributed and connected network.

In one embodiment  the method identifies information needs of different nodes in the distributed network and creates a information sharing path between the node which requires information (source node) and one or more node which can provide the full or partial information (destination nodes).
In one embodiment  the present disclosure identifies dynamic path(s) between the source node with a query (request for information)  one or more destination nodes which has information and a set of intermediary nodes connecting the source node to the destination nodes.
In one embodiment  the present disclosure creates an incentive system to engage the source node  the intermediary nodes and destination node so that the dynamic path can be identified and traversed so that information flows between source and destination nodes. The source node  intermediary node and the destination node incur cost for certain actions and are rewarded for certain other actions.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects and features described above  further aspects  and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS
The novel features and characteristics of the disclosure are set forth in the appended claims. The embodiments of the disclosure itself  however  as well as a preferred mode of use  further objectives and advantages thereof  will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described  by way of example only  with reference to the accompanying drawings in which:
Figs.1 illustrates process of capturing and sharing information in a distributed network.
Fig.2 shows evolving roles of nodes in the distributed network.
Fig.3 shows example process of obtaining a message in a distributed network.
Fig.4 illustrates working process of intermediary nodes to find response to a query.
Figs.5- 7 illustrates process of sending messages between nodes in a distributed network.
Fig.8 shows flow chart illustrating process of opening or closing message link between the nodes.
Fig.9 shows a flow chart which illustrates functions of source node of the distributed network.
Fig.10 shows a flow chart which illustrates functions of intermediate node or the destination node of the distributed network.
Fig.11 shows process of finding node’s reputation in the distributed network.
Fig.12 shows flow chart illustrating process of recommending nodes which has high probability of finding a response to a query.
Fig.13 shows process of gaining expertise.
Fig.14 shows process of attracting queries within a radius ring of a node.
The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION
An embodiment of the present disclosure provides a method for capturing and sharing information in a distributed network. A distributed network is a network structure in which the individual nodes are connected to one or more nodes via links.
Each node of the distributed network accumulates explicit and implicit knowledge by way of continuous learning. Learning occurs by way of constant interaction between the nodes  by way of its position in the network  due to past experiences  by way of insights (maturity) gained by passage of time etc. This information is valuable not only to directly connected nodes  but also to nodes which are very distant. In one embodiment  the present disclosure provides a method to identify information needs of different nodes in the distributed network and to create an information sharing path between the node which has the (part or full) information and the node which requires that information.
In one embodiment  the present disclosure creates an engagement model for the nodes in the distributed and connected network to interact with the other nodes. This engagement process exposes the participating nodes to new and improved information. The node then captures this implicit and explicit information in their node. Also the present disclosure provides a mechanism for each node to implicitly or explicitly express its information needs so that the system can identify the node(s) which can provide the required information for that particular node.

In one embodiment  the present disclosure creates an incentives based mechanism so that nodes with relevant information are identified; a dynamic path(s) is identified between the source node and the destination node(s) through a link of zero or more intermediary nodes. Further the incentive mechanism enables the co-ordination among all the participating nodes in the distributed network i.e. source  intermediary and destination nodes so that the information can traverse the dynamic path formed between source and destination nodes through zero or more intermediary nodes.

Fig.1 illustrates the engagement model between source  intermediary and destination nodes. The source node of the distributed network  say for example node-A describes a query or a message and provides My2Cents. My2Cents is a node’s recommendation about how to achieve result for the query. My2cents for each node may be computed from node’s expertise or fed to the node by an external agent or read from a configuration.
The system recommends few nodes for node-A in the distributed network to pass the message. Node-A might choose system recommended nodes or alternatively selects other nodes to pass the message. For example one of the nodes selected by the source node to pass the message is node-B. In one embodiment node-B is an intermediate node. Node-B will also give My2cents for the message. Through its own My2cents  Node-B recommends new directions for the message as it proceeds further into the connected network. Based on Node-B’s My2Cents as well as the collective global My2Cents gathered by the system for the same message and based on the other direct and derived metadata about the source-message the system recommends a list of nodes connected to node-B to forward the message. Node-B might take the system recommended nodes or other nodes to forward the message. Suppose one of the nodes selected by node-B to further forward the message is node-D  which has the information requested by the source node-A. Node-D here acts as a destination node. The destination node gives full or partial response to the query.

Additionally  Node-D may also play the role of an intermediary node giving my2cents and further forwards the message to more of its connected nodes. In one embodiment  each node giving the My2cents may or may not be aware of other advices or My2cents given by the other nodes participating in a single message flow. The above mentioned engagement enables each participating node to learn about new information and thereby improves its own expertise. Each node captures this explicit and implicit knowledge in its node for future engagements.

In one embodiment  if any of the intermediary nodes responses to the query then those intermediary nodes become the destination nodes as shown in Fig.2. For ex. node A is the source node which creates and shares the query with different intermediary nodes say  node B  C  D E  and F. Here node C gives response to the query and hence node C becomes the destination node. Further node E forwards the query to other nodes in the distributed network. Here node E becomes the source node and the other nodes becomes the intermediary nodes.

Fig.3 shows example process of obtaining a message in the distributed network. The participating node(s) in the distributed network can get a message from the other node in a variety of ways. The node-A can push/send a message to the node-B  or node-B can pull/claim the message from the node-A. In one embodiment  the system can assign a message to the node-B. Each of the other participating node(s) follows the same process for receiving the message.
In one embodiment  the intermediary nodes may operate individually  compete (individually or as part of a sub-group) with all or a subset of other nodes in the distributed network or cooperate (individually or as part of a sub-group) with all or a subset of the other nodes in the distributed network to find a response to the query described by the source node as shown in Fig.4. For example when the source node  node-A sends a message to the intermediary nodes  node-B  C  D  E  and F  in response to the received or claimed message each intermediary node may work independently to find responses to the query from the node-A  or join or form a sub-group  or each node might compete with each other or a subset of the nodes to find a response to the query from node-A  or each node might co-operate with each other or a subset of the nodes to find a response to the query from node-A. In one embodiment  each node may choose to operate in a hybrid/combination of the methods mentioned above.
In one embodiment  the source node (node-A) which requested the information incurs a cost for posting the message. The intermediary node like node-B is rewarded for accepting the message from the source node  but incurs a cost for further forwarding the message to more nodes. The intermediary nodes are also rewarded for using its network  for helping to reach the destination node faster  for finding the shortest path etc. The destination node is further rewarded for responding to the message by providing a full or partial response to the query. In one embodiment  the source node  the intermediary nodes  and the destination nodes can give feedback information to the message or the query for which they will be rewarded.

For example say  a hiring manager of a particular company wants to hire a person for a particular job. Hiring manager here is a source node. The hiring manager describes the job description  which constitutes the message or a query. The hiring manager then gives my2cents i.e. advice about how to find an ideal candidate. The my2cents may contain the current work location of ideal candidate(s)  company(s) where ideal candidate might be currently or previously worked  skill set of ideal candidate  colleges in which the ideal candidate might have studied etc. The system recommends friends in hiring manager’s immediate connected network who might meet some or all of the criteria of the job description and My2cents given. The hiring manager might choose the system recommended friends or alternatively selects other friends to pass the message. The other friends selected by the hiring manager here are intermediate nodes or destination nodes.

The hiring manager now sends the message to his/her friends. These friends might provide a partial response to the query i.e they might recommend one or more candidates who fits the job profile described by the hiring manager. They might also provide their My2cents (their own advice about how to find the right candidate) and forward the message to other friends in their immediate network. They might also respond (apply to the job profile) to the message. This process of providing My2cents  forwarding the message to more connected friends  giving partial or full response continues unless the hiring manager finds a set of candidates for the job description

Figs.5-7 illustrates process of sending a message between connected nodes in the distributed network. For any node to send a message in the distributed network  the message link between the communicating nodes has to be open. For ex.  for node A to send a message to node-B  the message link has to be open in the direction of the flow as shown below.
Message link from A to B (A->B)
Whether the link in the direction of the flow is open or closed is determined depending on the history of transactions between node-A and node-B. When node-A sends a message to node-B node-A’s debt in the direction A->B increases. When node-B receives a message from node-A  node-B’s credit in the direction B->A increases. For any event that occurs between node-A & node-B in the context of a particular message-1  the credit gained by node B is related to the debit gained by A. Debit (A->B) = [weight factor] x Credit (B->A) and Credit (A->B = [weight factor] x Debit (B->A)
Reciprocal (A->B)
• Reciprocal score for the direction A to B
• Equals Credit(A->B) – Debit (A->B)
Reciprocal (B->A)
• Reciprocal score for the direction B to A
• Equals Credit(B->A) – Debit (B->A)
When node-A sends a message to node-B or if node-B claims a message from node-A  Credit (B->A) increases. The Credit for node-B in the direction of node-A increases for all the events shown in the Fig.7.
Fig.8 shows flow chart illustrating the process of opening or closing the message link between the communicating nodes. The opening or closing of the message link is identified using one or more predetermined criteria  for example a reciprocal score criteria can be used. If the reciprocal score is within the acceptable range then message link is open. If the reciprocal score is not in the acceptable range then message link is closed. Node-A can send a message to Node-B only if the message link in the intended direction A to B is open. Link from A to B may be open or close. At the same time Link from B to A can independently be open or close.
Fig.9 shows a flow chart which illustrates functions of source node of the distributed network. The source node first describes a query. It then checks for the message link to be open. If at least enough message links to adjacent node are open then the source node provides my2cents for the query. The source node then sends the messages to enough number of adjacent nodes that meet the criteria of the query. It then checks whether end criteria for the query has been achieved or not. If the end criteria for the query  has been achieved then the process stops. Until the end criteria is met  the above process continues. The process ends when the end criteria are met – which could be when enough number of responses are generated for the message or when the system receives enough number of feedback (for the messages or its responses) or when the maximum allowed time-limit for message sharing expired etc.
Fig.10 shows a flow chart which illustrates functions of intermediate node or the destination node of the distributed network. The intermediate node or the destination node receives the query from the source node. If the end-criteria of the message is not met  an intermediate or the destination node may choose to respond to the query. Additionally if the message links are open  the intermediate or the destination node provides my2cents for the received query. It will then select enough number of adjacent nodes that meet the criteria of the query and send the query to the identified adjacent nodes. It will then again check whether or not the end-criteria have been achieved for the query. The process ends when the end criteria are met – which could be when enough number of responses are generated for the message or when the system receives enough number of feedback (for the messages or its responses) are receive or when the maximum allowed time-limit for message sharing expired etc.
Fig.11 shows process of finding node’s reputation in the distributed network. As the messages passes from node to node the reputation of each node changes. The three parameters which help in finding the reputation of the node in the distributed network are (1) network power  (2) contribution quotient and (3) the node’s expertise. The parameter “network power” is an indicator of the power of each node’s network to get responses for a given query. This parameter is a reflection of the accumulated contribution of a node’s network in finding responses to queries that flowed through a particular node. The contribution quotient parameter measures the contribution factor of the node in finding a result. The expertise parameter measures the overall accumulated expertise of the node. Finally  the reputation of node is a weighted sum of all the above parameters.
Fig.12 shows flow chart illustrating the process of recommending nodes which has high probability of finding a response to a given query. When an intermediary node receives the query  the system extracts the metadata about the query and the associated weight factor associated with each metadata element. The intermediary nodes then check for more adjacent nodes. If the adjacent nodes are found  then it further checks for the message link to be open. If the message link is open  then the intermediate node will compute the expertise of the adjacent node for each of the metadata element of the query. The intermediate node will then add the adjacent node to a rank list. The intermediate will check for further adjacent nodes in the distributed network. If there are no more adjacent nodes  the intermediate node will rank the nodes with the open links and recommend the #n-nodes which meets the recommendation criteria (for example  Top 5 nodes with fastest link  Top 5 nodes with shortest distance etc.).
For ex. say  there are five nodes adjacent to the intermediate node. The five nodes are node-1  node-2  node-3  node-4  and node-5. Suppose the metadata elements for the query are MetaD-a  MetaD-b  MetaD-c and MetaD-d. For each node  the system will check whether the node has expertise in any of the metadata elements and the accumulated expertise value. Suppose node-1 has expertise in MetaD-b (current expertise value of 7) and MetaD-c (current expertise value of 5)  in the simplest form the system computes the rank score for node-a as 12 (7+5).In more advanced version  the system may assign a particular weight to some of the metadata elements which affects the computed rank score. Similarly the expertise value of each adjacent node is computed. Finally from the rank list nodes  the system recommends a set of nodes. The system can impose criteria for selecting the recommended nodes (for example  top 5 nodes  bottom 7 nodes etc)
Fig.13 shows process of gaining expertise. For each event  the expertise engine identifies the list of expertise metadata element(s) - name space  label and the value(s). For example if an event occurred at geographic location Bangalore  the expertise engine will capture expertise as a tuple consisting of Namespace: Label: Value
• Geography: Bangalore: (+n) 
• Geography: Karnataka: (+m)
• Zip code: 560001: (+l) 
• Latitude: 12° 58"" N: (+o) 
• Longitude: 77° 38"" E: (+p).
When the events repeats  the expertise elements  if already exist will be updated. If the expertise element is not present  a new expertise element will be created. The events as well as the related gaining of expertise make the system a continuous learning system.
Fig.14 shows process of attracting queries within a radius ring of a node. Node-0 as shown in the Fig.14 can implicitly or explicitly loan its expertise to its connected nodes in the network. This loaning can be conditions based so that not all connected nodes get the equal share of loaned expertise. Each of the adjacent nodes can again choose to further loan the received expertise to their adjacent nodes. This loaning of expertise creates an expertise ripple around node-0 there by making node-0’s expertise visible to a wider radius and many hops away from node-0. This expertise boost attracts queries with metadata that are relevant to the boosted expertise. In one embodiment  the node-0 is able to attract queries which are far and wide (many hops away) and are relevant to node-0’s expertise.

Finally  the language used in the specification has been principally selected for readability and instructional purposes  and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description  but rather by any claims that issue on an application based here on. Accordingly  the disclosure of the embodiments of the invention is intended to be illustrative  but not limiting  of the scope of the invention  which is set forth in the following claims.
With respect to the use of substantially any plural and/or singular terms herein  those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
In addition  where features or aspects of the disclosure are described in terms of Markush groups  those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.
While various aspects and embodiments have been disclosed herein  other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting  with the true scope and spirit being indicated by the following claims.