Abstract:
In a messaging system, pruning messages from a data store according to similarity of topics and the number of requests for the individual messages and the time intervals between requests of messages of similar topic.

Description:
FIELD OF THE INVENTION  
       [0001]     The invention relates to the field of message subscribe systems. In particular the invention relates to a method and system for providing a pruning strategy.  
       BACKGROUND OF THE INVENTION  
       [0002]     Publish/subscribe communications involve information producers publishing information or events to a publish/subscribe system, and information consumers subscribing to particular categories of information or events and receiving relevant publications from the system. The publish/subscribe system may comprise a message broker, located between publisher and subscriber applications, which delivers published information or events to all interested subscribers.  
         [0003]     The publish/subscribe communication paradigm supports many-to-many communications in which individual publishers and subscribers may be anonymous to each other (communicating via an intermediate broker) and can be easily added and removed from the network without disruption. An example message broker is the IBM® WebSphere® Business Integration Message Broker product available from IBM. (IBM and WebSphere are registered trademarks of International Business Machines Corporation.)  
         [0004]     Many publish/subscribe messaging systems are subject-based. In these systems, each message belongs to one of a predefined set of subjects (also known as channels, or topics). Publishers label each message with a subject, and consumers subscribe to all the messages having a particular subject label. For example, a subject-based publish/subscribe system for stock trading may use a defined topic name for each stock issue—publishers post information using the appropriate topic name and subscribers include topic names when specifying which stocks they wish to receive information about.  
         [0005]     Some messaging systems provide a replay feature, for example retaining publications for replay to new subscribers (and newly recovered subscribers) so that the new subscribers are able to receive some or all of an earlier message feed.  
         [0006]     In a replay system the messages for replay are stored in a data store. There is a danger, that after a certain period of time, the data store can become over populated with stored messages and thus become difficult to manage. Thus a pruning strategy is deployed by the replay system to provide data management of the data store.  
         [0007]     A pruning strategy works by scanning the data store for messages that have been stored for a particular length of time, for example. The length of time is variable and can be altered by an administrator. If the pruning component locates messages that meet the pruning strategy&#39;s criterion, a pruning operation is performed and the identified messages are removed from the data store. A problem often occurs when a message is requested for replay, but the request has to be declined because the message has been pruned from the data store. Hence there is a need within the art to provide a solution to this problem.  
       SUMMARY OF THE INVENTION  
       [0008]     Viewed from a first aspect the present invention provides a method for pruning messages, from a data store, in a messaging system, the method comprising the steps of: detecting the number of requests for a message in the data store; determining a rating indicative of the number of detected requests; determining a relationship between the message and other messages stored in the data store; calculating a proximity value from the determined relationship; and in dependence of the rating and the proximity value, determining whether to prune the message from the data store.  
         [0009]     Advantageously, the present invention provides a pruning method, product, system and computer program product. The pruning method tracks requests for messages, for replay, from a reply server. A map is created to store a representation of the messages stored in the data store and each time a request is detected the message is flagged as read. For each message requested, the date and time of the request is captured and stored in an activity record. The pruning method uses a rules engine to apply rules to the data to determine a relevance rating for each message requested for replay.  
         [0010]     Advantageously, the pruning method detects patterns of activity for each of the messages, for example, the date and time the message was requested and how many times the message has been requested over a period of time. Varying levels of relevance can be determined and different parameters can be applied by the rules to determine the relevance rating. For example, a relevance rating may be determined by calculating the number of times a message has been requested for replay or, for a more complex relevance rating, activity data may be used in combination with the number of times a message has been requested for replay to determine the relevance rating.  
         [0011]     The pruning method also detects the proximity of a message to other messages stored in the data store. A proximity strategy is used to understand and determine if a relationship exists between one message and another. For example, a message may have a topic heading ‘dogs’ and thus messages requested about ‘dogs’ may also generate interest about ‘cats’. Thus the proximity strategy comprises complex rules to determine a number of relationships. Messages determined as having a relationship with another message are flagged in the data store. The total number of messages with a defined relationship to another message comprises the proximity value. Thus, the pruning method determines how relevant a message is to current requests for replay and the relevance the message has to other messages stored in the data store. Therefore when a request to prune the data store is received, the pruning method can determine which messages are currently relevant, i.e. the most requested over a given time period, and which other messages have also been requested for reply because of their relevance to the requested message. It is important to note that the above process is performed over a pre-defined time-cycle and thus a message&#39;s relevance and proximity may increase and decrease throughout the time-cycle.  
         [0012]     Hence a pruning operation will only prune the messages that have not been requested over a period of time and have no relevance to other messages in the data store.  
         [0013]     Viewed from a second embodiment, the present invention provides a pruning component for pruning messages from a data store in a messaging system, the system comprising: a tracking component for detecting the number of requests for a message from the data store; a relevance component for determining a rating indicative of the number of detected requests; a proximity component for determining a relationship between the message and other messages stored in the data store; the proximity component calculating a proximity value from the determined relationship; and a rules engine for determining whether to prune the message from the data store in dependence of the determined rating and the proximity value.  
         [0014]     Viewed from a third aspect the present invention provides for a messaging system comprising a pruning component as described above.  
         [0015]     Viewed from a fourth aspect the present invention provides a computer program product claim for use with the pruning component as described above.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0016]     Embodiments of the invention are described below in detail, by way of example only, with reference to the accompanying drawings in which:  
         [0017]      FIG. 1  is a block diagram illustrating a publish/subscribe environment in which the present invention may be embodied;  
         [0018]      FIG. 2  is a block diagram illustrating how messages are categorized and stored in a publish/subscribe environment as is known in the art;  
         [0019]      FIG. 3  is a block diagram illustrating a proximity and relevance component in accordance with a preferred embodiment of the present invention;  
         [0020]      FIG. 4  is a map generated by a map component in accordance with a preferred embodiment of the present invention;  
         [0021]      FIG. 5  is a block diagram illustrating the components of the relevance component in accordance with a preferred embodiment of the present invention;  
         [0022]      FIG. 6  is a block diagram illustrating the components of the proximity model in accordance with a preferred embodiment of the present invention;  
         [0023]      FIG. 7  is a flow chart detailing the operational steps of the map component in accordance with a preferred embodiment of the present invention;  
         [0024]      FIG. 8  is a flow chart illustrating the operational steps of the relevance and proximity components in accordance with a preferred embodiment of the present invention; and  
         [0025]      FIG. 9  is a flow chart detailing the operational steps of the delete component in accordance with a preferred embodiment of the invention.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0026]      FIG. 1  illustrates the environment in which the present invention may be embodied in accordance with preferred embodiments. A replay server  105  participates in a messaging environment (for example a pub/sub environment  100 ) to store a copy of all messages received by a message broker ( 120 ) in a message store  125 . Note the replay server  105  may be integrated as part of the message broker  120  or may be a separate component (as shown in the figure). In addition to the message store  125 , the replay server  105  includes a replay component  110  and a persistence service component  115 . The replay server  105  subscribes via a network connection  150  to receive all messages transmitted to the message broker  120 .  
         [0027]     A requesting application  135  connects to the message broker  120  via the network connection  150 . The requesting application  135  comprises a replay requester  140  that is operable to make replay requests via the message broker  120 . Upon receipt of such a request, the message broker  120  informs the replay server  105  of the requester&#39;s requirements. For example, the replay requester  140  could request a replay of all messages received with a specified time period (e.g. the last hour). Such replay is achieved at the replay server  105  via replay component  110 . A publisher  145  connects to the message broker  120  and is operable for transmitting messages to the message broker  120  for storing by the replay server  105 .  
         [0028]     To manage the many messages that are stored in the message store  125 , a pruning component  130  cooperates with the message store and a persistence service  115  to periodically remove messages from the message store  125 . The pruning component  130  defines a number of pruning strategies to determine which messages should be removed from the message store  125 . For example, the pruning strategy might be to remove all messages from the message store  125  that have been stored for more than 60 days. Thus, the pruning component  130  scans the message store and removes all messages that have been stored in the message store for more than 60 days, regardless if the messages that are being removed are still being requested for replay by the requesting application  135 .  
         [0029]      FIG. 2  illustrates how messages are stored in the message store  125 . The message store  125  stores messages received from the message broker  120 . Messages are stored in database table  200 . Each message is categorised by a topic  230  and is stored and indexed by a sequence number  205 . A sequence number  205  is unique for each message within a topic  230 . The sequence number  205  is generated and assigned to a message, by the message store  125 , on receipt of the message.  
         [0030]     In a first database table  225  a number of topics  230  are defined, for example, football and cars. For each defined topic  230 , the topic  230  is assigned a topic ID  212 . On receiving a new message the message store  125  parses the message to identify the topic i.e. ‘cars’ and performs a lookup in the database table  225  to identify if the topic exists in the message store  125 . If it is determined there is a topic called ‘cars’, the message is assigned the topic id associated with the topic ‘cars’. The message is also assigned a sequence number  205 , which is unique for the topic ‘cars’ and stored in the data store  200  along with the message  215  and a timestamp  220  that indicates the date and time of storage. Alternatively, if a message is received and the message is concerning a topic that does not exist in the message store  125 , the topic is added to the data table  225  and assigned a unique topic ID. The process of storing the message proceeds as described above.  
         [0031]     Over time the message store  125  becomes populated with a high volume of messages and requires some form of data management. Thus the pruning component  130  cooperates with the message store  125  and the persistence component  115  to prune messages from the message store  125 . The pruning component  130  comprises a rules engine  240  and a number of rules  245 . The rules  245  comprise logic for a number of pruning strategies. For example, 
        Remove messages that has been stored for a determined period of time;     Remove a percentage of messages that have been stored for a determined period of time; or     When the message store  125  has reached a predetermined message limit, remove messages that have been stored for the greatest length of time—hence bringing the number of messages stored below the predetermined message limit.        
 
         [0035]     The rules engine  240  periodically monitors the message store  125  until a characteristic of the message store matches a condition of a pruning strategy rule. Once a match has been identified the rules engine  240  performs the pruning operation. For example, using the data in database table  200  the rules engine  240 , for each message, identifies the timestamp field  220 , in the database table  200 , and determines if the message meets the criterion of being stored for more than fourteen days. Hence if today&#39;s date is Aug. 8, 2005, the rules engine  240  identifies the message pertaining to Southampton Football Club  215  (sequence number  1 , topic ID  1 ) as meeting the criterion of the pruning strategy. Hence this message is removed from the message store  125 .  
         [0036]     As can clearly be seen a situation can arise wherein a request for replay of a message is received and the replay component  110  declines the request because the message has recently been removed from the message store  125 .  
         [0037]     The present invention overcomes the limitations of current pruning strategies by providing a relevance and proximity component, for intelligently managing the pruning process to ensure that as few replay requests as possible are refused.  
         [0038]      FIG. 3  illustrates the proximity and relevance component  300  in accordance with a preferred embodiment of the present invention. The proximity and relevance component  300  communicates with the message store  125 . The proximity and relevance component  300  in a preferred embodiment is embedded within the message store  125  but as will be understood by a person skilled in the art the proximity and relevance component  300  may be implemented outside of the message store  125  within the replay server  105  or pruning component  130 . The proximity and relevance component  300  defines degrees of relevance and proximity for each message in the message store to understand a level of importance of a message and thus whether the pruning component  130  should be allowed to remove the message from the message store or not.  
         [0039]     The proximity and relevance component  300  comprises a track component  305 , a map component  310 , a persistence component  315 , a relevance component  320 , a database delete component  325  and a proximity component  330 . Each of these components will now be explained in turn.  
         [0040]     The track component  305  interfaces with the message store  125  to track data reads on a message, for example, a request from the replay component  110  to replay a message. On determining a read operation the track component  305  determines the sequence number and topic id of the message being replayed and packages the data into a data packet for transmitting to a buffer of the map component  310 .  
         [0041]     The map component  310  builds and maintains a map  400  of detected read operations for each message stored in the message store  125 . With reference to  FIG. 4 , the map component  310  builds a map of the message store  125 . The map in this embodiment comprises a database table  400  comprising columns and rows. The columns represent the sequence number  205  and the rows represent the topic ID  212 . The map component  310  requests from the message store the topic ID&#39;s and the sequence numbers as stored in database tables  200  and  225 . The map component  310  uses this information to populate the map. On receiving a notification of a read operation the map component  310 , for example, detects data packets in the buffer and places an identifier in the appropriate column and row to indicate that the message has been read. If a request to replay a message (for example, message: topic id  1 , sequence number  1 ) is made by the message broker  120  to the replay server  105  the replay server  105  requests the message from the message store  125 . At this point the track component  305  detects that sequence number  1  topic ID  1  has been read from the message store  125 . The track component  305  transmits this information to the map component  310  and the map component  310  identifies the message in its map and marks it as read. This can be seen by referring to topic ID  1  and sequence number  1  in the map of  FIG. 4  and noting the notation ‘R’ in the corresponding box.  
         [0042]     The marking of read operations in the map  400  continues for each read operation tracked by the tracking component  305 . Thus over a given time period a map  400  of read operations is derived for all replay requests in the message store  125 . For example,  FIG. 4  shows a detected read operation for sequence number  1  topic ID  1 , sequence number  2  topic ID  1 , sequence number  4  topic ID  3  and sequence number  5  topic ID  5 .  
         [0043]     The map component  310  further maintains an activity record  405  for each message requested for replay. Each activity record  405  comprises the date and time of a replay request and a running total of the number of replay requests. An example is shown in Example 1.  
       EXAMPLE 1 
       [0044]    
       
         
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                   
               
               
                   
                 Sequence number 
                 Topic ID 
                 Timestamp 
                 Total 
               
               
                   
                   
               
             
             
               
                   
                 1 
                 1 
                 08.01.05, 10:21 
                 2 
               
               
                   
                   
                   
                 08.01.05, 10:20 
               
               
                   
                 1 
                 2 
                 07.15.05, 15:35 
                 3 
               
               
                   
                   
                   
                 06.29.05, 23:55 
               
               
                   
                   
                   
                 06.29.05, 23:56 
               
               
                   
                 2 
                 2 
                   
                 0 
               
               
                   
                   
               
             
          
         
       
     
         [0045]     As can be seen from Example 1, the message associated with sequence number  1  with a topic id of  1  was requested for replay twice—once on the 08.01.05 at 10:20 and again on the same date at 10:21. Likewise, the message associated with sequence number  2  with a topic id of  1  was requested for reply three times—namely once on the 07.01505 at 15:35, secondly on the 06.29.05 at 23:55 and thirdly, on the 06.29.05 at 23:56. Lastly, the message associated with sequence ID  2  and topic ID  2  has not been requested for replay and its total is zero. The activity records  405  are used as input into the database delete component  325  and the relevance component  320 .  
         [0046]     The proximity and relevance component  300  as previously mentioned comprises a relevance component  320 . The relevance component  320  determines the relevance of each message that is identified as read in the map  400 , i.e. identified as requested for replay.  
         [0047]     With reference to  FIG. 5 , the relevance component  320  comprises a determiner component  500 , and a degree of relevance model  510 . The determiner component  500  interfaces with the map component  310  and the activity records  405 , to determine the degree of relevance for each message identified as requested for replay. The determiner component  500  communicates with the relevance model component  510  to deploy one of a set of calculation components. The calculation components may take the form of a linear calculation component  515 , an exponential calculation component  520  or a logarithmic calculation component  525 . Of course other types of calculations may be deployed. What type of calculation component deployed is dependent on the operational environment of the replay server  105  and can be tailored to suit many different purposes.  
         [0048]     The relevance model component  510  determines the degree of relevance by determining the number of times the message has been identified as requested for replay and how recently these requests occurred. Thus the relevance model component  510  via the determiner component  500  requests the activity records  405  of each of the messages identified as requested for replay. Using the data of Example 1, the message associated with sequence number  1  has been requested twice and the message associated with sequence number  2  has been requested three times. But although it would appear that the message associated with sequence number  2  has a higher degree of relevance than the message associated with sequence number  1 , the last time the message associated with sequence number  2  was requested for replay was some time before the message associated with sequence number  1 . Hence in this case the message associated with sequence number  1  has a higher degree of relevance than the message associated with sequence number  2 . Conversely, the message associated with sequence number  3  has no degree of relevance as the message has not been requested for reply. The degree of relevance increases the more the message is requested for replay. Conversely, the degree of relevance decreases as the message is less frequently requested for replay. The decrease in the degree of relevance can either be linear, exponential or logarithmic. The decrease in the degree of relevancy can be arrested or reversed by new replay requests for a message. The degree of relevance is therefore dynamic and changes in a timed cycle of requests.  
         [0049]     The level of granularity deployed by the relevance model  510  is dependent on the environment the invention is operating within. For example, the relevance model  510  described above may be suitable for use with a message store  125  that receives a low number of requests each month, but a finer granularity of a relevance model may need to be deployed with a message store  125  storing high volume of messages over a predetermined time limit.  
         [0050]     Along with the relevance of a message it is important to determine the proximity of the message in relation to other messages in the message store  125 . The degree of proximity is defined by a function of its location in relation to other relevant data (i.e. other messages that have been identified as requested for replay) in the message store  125 .  
         [0051]     With reference to  FIG. 6  the proximity component  330  comprises a calculation component  600  and a number of rules defining a proximity strategy  605 . A proximity strategy  605  enables the proximity component  330  to determine which messages should be defined as having a relationship with other messages, in the message store  125 . For example, a strategy may define all messages having the same topic have a defined relationship.  
         [0052]     The calculation component  600  on trigger from the map component  310  parses the map  400  and for every database row and column that has been logged with a symbol that indicates that the message has been requested for replay, the calculation component logs a further symbol in each surrounding column/row to indicate that that message has a defined relationship.  
         [0053]     For example referring to  FIG. 4 , the map  400  as explained earlier is updated with an ‘R’ or other symbol that indicates the message has been requested for replay. The proximity component  330  parses the map  400  and puts a further symbol—in this example the character ‘P’ in each of the surrounding column/row to indicate that the message has a proximity relationship with the message marked with ‘R’. For example, the message associated with sequence number  1  and topic id  1  has a read operation recorded against it—identified by an ‘R’ in the map. The proximity strategy  605  may state for every detected read operation a proximity relationship should be recorded for each column/row before it, behind it and either side of it. In this example, a proximity relationship is recorded in topic ID  2  column/sequence number  1  column. It is not necessary to record a proximity relationship for topic ID  1 /sequence number  2  as this already has a read operation recorded. This sequence of operations is continued for each detected read operation until each read operation has a number of proximity relationships associated with it as is shown by the map  400 . The blank spaces in the map signify that the message has not been requested for replay and has no defined proximity relationship. These messages, therefore, are candidates for removing from the message store  125 .  
         [0054]     How the proximity relationships are defined is dependent on the proximity strategy  605 . The proximity strategy  605  defines which topics are similar. For example topics about cats also invoke interest in dogs etc and therefore it is advisable not to remove messages about dogs when there are messages about cats etc. The relationships may be defined at many different levels that can be reflected in the map. The relationships may be complex or simple—analogous to the moves of chess pieces on a chess board. For example, for grid reference 2 (sequence number), 2 (topic ID) place a ‘P’ at grid reference 1,1, 1,2, 1,3, 2,1 and 2,3 etc.  
         [0055]     Although the proximity strategy has been described with reference to a two-dimensional database model, it will be understood by a person skilled in the art that the proximity strategy could equally be applied to a three-dimensional or N-dimensional database model.  
         [0056]     Once the proximity component  330  has completed the steps of identifying and recording each of the proximity relationships, the proximity component  330  proceeds to determine the degree of proximity by summing the number of proximity relationships for each read operation. The degree of proximity is recorded in the activity record as is shown in Example 2.  
       EXAMPLE 2 
       [0057]    
       
         
               
               
               
               
               
             
           
               
                   
               
               
                   
               
               
                 Sequence 
                   
                   
                 Degree of 
                 Degree of 
               
               
                 number 
                 Topic ID 
                 Timestamp 
                 relevance 
                 proximity 
               
               
                   
               
             
             
               
                 1 
                 1 
                 08.01.05, 
                 2 
                 3 
               
               
                   
                   
                 10:21 
               
               
                   
                   
                 08.01.05, 
               
               
                   
                   
                 10:20 
               
               
                 1 
                 2 
                 07.15.05, 
                 3 
                 2 
               
               
                   
                   
                 15:35 
               
               
                   
                   
                 06.29.05, 
               
               
                   
                   
                 23:55 
               
               
                   
                   
                 06.29.05, 
               
               
                   
                   
                 23:56 
               
               
                 2 
                 2 
                   
                 0 
                 2 
               
               
                   
               
             
          
         
       
     
         [0058]     Thus it is shown in Example 2 the message associated with sequence  1 , topic ID  1  has a degree of relevance of 2 and a degree of proximity of 3. The message associated with sequence number  2 , topic ID  1  has a degree of relevance of 3 and a degree of proximity of 2. Lastly, the message associated with sequence number  1 , topic id  2  has a degree of relevance of 0 and a degree of proximity of 2. Each of the statistical calculations are analysed by the database delete component  325  to understand whether the message should be pruned from the message store  125 .  
         [0059]     The database delete component  325  cooperates with the pruning component  130  and the map component  310  to determine whether a prune operation can proceed or not. The database delete component  325  comprises a number of rules that allow the database delete component to interpret the calculated degree of relevance and the degree of proximity as stored in the activity records  405 . For any message the following combination of degrees of relevance and proximity can occur:  
                                                   Degree of relevance   Degree of proximity                           High (sum of 10 and over)   High (sum of 10 and over)           High   Medium (sum of 5 and over)           High   Low (less than 4)           Medium (sum of 5 and over)   High           Medium   Medium           Medium   Low           Low (less than 4)   High           Low   Medium           Low   Low                      
 
         [0060]     Attached to each of the combinations of relevance are rules that state whether the message can be removed from the message store  125  or not. For example, if the degree of relevance is high and the degree of proximity is high the message can not be removed from the message store  125 . If the degree of relevance is low and the degree of proximity is low the message can be removed from the message store  125 . More complex rules are required to interpret the combinations of relevance in between high and low. For example, it may be advisable to retain any message with a high degree of relevance or a high degree of proximity, but any message with a medium degree of relevance or proximity may be removed from the message store  125 .  
         [0061]     Referring to  FIG. 7  the operational steps of the tracking component  305  and map component  310  are shown in use. At step  700 , the tracking component  305  detects a request for a message to be replayed. The track component  305  determines the message being replayed, at step  705 , and identifies the sequence number and topic id of the message. At step  710 , the track component  305  packages the sequence number and topic id into a data packet and submits the data packet to the map component&#39;s  310  buffer at step  715 . At step  720 , the data packet is held in the buffer until the proximity component  330  and relevance component  320  have updated the map  400  and the associated activity records  405 . Once the map  400  and the activity records  405  are updated the map component  310  receives the data packet from the buffer. The map component  310  parses the data packet to determine the sequence number and the topic id of the message that is being requested for replay and updates the map and activity record  405  associated with the message.  
         [0062]     On updating the map  400  the map component  310  sends a trigger to the relevance component  320  and the proximity component  330  to begin determining the degree of relevance and proximity for each message in the map  400 .  
         [0063]     Referring to  FIG. 8  the relevance component  320  begins at step  800  to apply a relevance model to determine the degree of relevance for each recorded read operation in the map  400 . The relevance model parses a message&#39;s activity record  405  to determine the number of times the messages has been requested to be replayed and in what time period. The degree of relevance for each message is calculated and recorded in its activity record  405 , at step  805 . At step  810 , the proximity component parses the map  400  and determines, based on a proximity strategy, the proximity relationship between a message that has been requested for replay and other messages in the message store  125 . The proximity component  330  records the proximity relationships in the map  400  and calculates the degree of proximity that is recorded in the message&#39;s activity record  405  at step  815 . The proximity component  330  and the relevance component  320  wait until the map component  310  updates the map, with the data packets held in the buffer, before proceeding their calculations again, at step  820 .  
         [0064]      FIG. 9  shows the database delete component  325  in operation. At step  900 , the database delete component  325  receives a pruning request from the pruning component  130 . The delete component  325  identifies the topic id and the sequence number of the message to be pruned from the message store  125 . The database delete component  325  requests from the map component  310  access to the message&#39;s activity record  405  at step  910 . The database delete component  325 , using a rule set, determines whether the message can be pruned from the message store  125 . Control moves to step  915  and the database delete component  325  determines whether the prune operation is allowed. For example, if the activity record  405 , for a message, states the message has a high degree of relevance and a high degree of proximity a rule may state the message can not be deleted and control passes to step  930  whereon an exception message is generated and transmitted to the pruning component  130 . Alternatively, if the activity record  405  shows the message having a low degree of relevance and a low degree of proximity a rule may state the message can be deleted and control moves to step  925  wherein the prune component is allowed to proceed with its prune operation.