Abstract:
Systems and methods are provided for identifying data variable rules during initial data exploration. In one example, a computer-implemented method of determining a role for a data variable is disclosed. The method comprises identifying to a plurality of data nodes a set of data records containing data values assigned to each data node, a maximum number of levels to record in a sorted data structure at the data nodes, and the data node responsible for each of a plurality of variables. The method further comprises receiving for each variable from the data node responsible for the variable a plurality of unique data values for the variable, a count for each of the unique data values and an overflow count for the variable, wherein the number of unique data values does not exceed the maximum number of levels. A role for a variable can be determined based upon the unique data values, counts and overflow count for the variable.

Description:
TECHNICAL FIELD 
     The technology described herein relates generally to computer-implemented systems and methods for data mining, and in particular, to computer implemented systems and methods for initial data exploration before the start of data analysis. 
     BACKGROUND 
     Data mining can be used in various fields. Data mining may reveal information and insight into a data set. 
     SUMMARY 
     In accordance with the teachings provided herein, systems and methods are provided for identifying data variable roles during initial data exploration. In one example, a computer-implemented method of determining a role for a data variable for use in data modeling of a physical process is disclosed. The method comprises identifying to a plurality of data nodes a set of data records containing data values assigned to each data node, a maximum number of levels to record in a sorted data structure at the data nodes, and the data node responsible for each of a plurality of variables. The method further comprises receiving for each variable from the data node responsible for the variable a plurality of unique data values for the variable, a count for each of the unique data values and an overflow count for the variable, wherein the number of unique data values does not exceed the maximum number of levels. The data values, counts and overflow count having been generated at a plurality of data nodes by node data processors configured by data processing instructions to determine whether a next data value for a data record can be added to the sorted data structure at the data node and that a count associated with that next data value can be added to the sorted data structure when the next data value can be added, determine whether the next data value is already included in the sorted data structure and that the count associated with that next data value can be incremented when the next data value is already included, and determine whether the next data value should not be added to the data structure and that an overflow count at that node should be incremented when the next data value cannot be added. A role for a variable can be determined based upon the unique data values, counts and overflow count for the variable. 
     In another example, a computer-implemented system for determining a role for a data variable for use in data modeling of a physical process is provided. The system comprises a plurality of data nodes each comprising a node data processor configured to perform operations on a plurality of data records. Each data record includes a data value for a variable. The plurality of data nodes include non-transitory computer-readable media encoded with a sorted data structure and encoded with data processing instructions. The sorted data structure is provided for storing up to a predetermined number of unique data values for one or more variables, a count for each of the unique data values, and an overflow count for each of the one or more variables. The data processing instructions comprise instructions for configuring the data node to determine whether a next data value can be added to the sorted data structure at the data node and that a count associated with that next data value can be added to the sorted data structure when the next data value can be added, determine whether the next data value is already included in the sorted data structure and that the count associated with that next data value can be incremented when the next data value is already included, and determine whether the next data value should not be added to the data structure and that an overflow count at that node should be incremented when the next data value cannot be added. One of the data nodes is a root data node comprising a root data processor configured by instructions to communicate data record assignments to the data nodes and a maximum number of levels to record in the sorted data structure. The root data processor is also configured to receive for a plurality of variables a plurality of unique data values, a count for each of the unique data values and an overflow count for the variables. A role for a variable can be determined based upon the unique data values, counts and overflow count for the variable. 
     In yet another example, a computer-program product for performing data mining operations on data is provided. The computer-program product is tangibly embodied in a machine-readable non-transitory storage medium and includes instructions configured to cause a data processing apparatus to identify to a plurality of node data processors a set of data records containing data values, wherein a particular node data processor is assigned a particular set of data records. At the particular node data processor, the instructions are configured to cause a data processing apparatus to determine whether a data value for a next data record in the particular set of data records can be added to a sorted data structure at the particular node data processor, wherein the particular node data processor is configured for each variable to store up to a predetermined number of unique data values in the sorted data structure and a count for each of the unique data values, and wherein the particular node data processor is configured to store an overflow count of data values that cannot be added to the sorted data structure. The instructions are further configured to cause a data processing apparatus to increment the count associated with that data value when the data value can be added and the data value matches a data value in the sorted data structure, add the data value to the sorted data structure when the data value can be added and the data value does not match a data value in the sorted data structure, and increment the overflow count when the data value cannot be added. The instructions are further configured to cause a data processing apparatus to consolidate the data values and counts for each variable from the particular node data processor with data values and counts from other of the plurality of node data processors into a sorted consolidated data structure. A role for a variable can be determined based upon the unique data values, counts and overflow count for a variable. 
     In another example, a computer-implemented method of determining a role for a data variable for use in data modeling of a physical process is provided. The method comprises receiving the identity of a set of data records containing data values and a maximum number of levels to record in a sorted data structure, determining for a data variable whether a next data value for a data record can be added to the sorted data structure and that a count associated with that next data value can be added to the sorted data structure when the next data value can be added, determining for the data variable whether the next data value is already included in the sorted data structure and that the count associated with that next data value can be incremented when the next data value is already included, and determining for the data variable whether the next data value should not be added to the data structure and that an overflow count should be incremented when the next data value cannot be added. The method further comprises broadcasting for the data variable a plurality of unique data values, a count for each of the unique data values and an overflow count, wherein the number of unique data values does not exceed the maximum number of levels. A role for the variable can be determined based upon the unique data values, counts and overflow count. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram depicting an example environment wherein users can interact with a computing environment that can perform data mining operations. 
         FIG. 2  is a block diagram depicting an example grid-based computing environment that can perform data mining operations. 
         FIG. 3  is a block diagram depicting an example grid-based computing environment that can evaluate data value roles. 
         FIG. 4  is a process flow chart that depicts an example process performed by a control node data processor to identify data variable roles. 
         FIG. 5  is a process flow chart that depicts an example process performed by a control node data processor to assign tasks to a plurality of worker node data processors. 
         FIG. 6  is a process flow chart that depicts an example process performed by worker node data processors. 
         FIG. 7  is a process flow chart that depicts an example process performed by a particular worker node processor when executing its assignment. 
         FIG. 8  is a process flow chart that depicts an example process performed by a particular worker node processor to process its assigned observations. 
         FIG. 9  is a process flow chart that depicts an example process performed by a particular worker node data processor to update level caps and prune trees to create trees with the minimum n levels. 
         FIG. 10  is a process flow chart that depicts an example process performed by a particular worker node data processor to update level caps and prune trees to create trees with the maximum n levels. 
         FIG. 11  is a process flow chart that depicts an example process performed by worker node data processors when merging data from other worker node data processors. 
         FIG. 12  is a process flow chart that depicts an example process for identifying data variable rules during initial data exploration. 
         FIG. 13  contains two example tables that, respectively, show the observations that are assigned to example compute node 1 and example compute node 2 for a data table with two variables, C1 and N1. 
         FIGS. 14   a  and  14   b  contain a collection of example tables that show the state of observed levels recorded in the binary trees after each observation is processed. 
         FIG. 15  contains two example tables that, respectively, show the final level lists for the two example worker nodes after all of the example observations have been processed. 
         FIG. 16  contains a collection of tables that illustrate the merging of the values for variable C1. 
         FIGS. 17A-17E  contains a collection of tables that illustrate the merging of the values for variable N1. In particular,  FIG. 17A  illustrates the merger of level 17.3 from Worker 1 with the Worker 2 table.  FIG. 17B  illustrates the merger of level 21.2 from Worker 1 with the Worker 2 table.  FIG. 17C  illustrates the merger of level 29.2 from Worker 1 with the Worker 2 table.  FIG. 17D  illustrates the merger of the Other data from Worker 1 with the Other data in the Worker 2 table. And, the final tables in  FIG. 17E  illustrate the final result after the data merge is complete. 
         FIG. 18  is a block diagram of example hardware for either standalone or client/server computer architecture. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  depicts at  10  a computing environment for processing data for many different types of applications, such as for scientific, technical or business applications. One or more user computers  12  can interact with the computing environment  10  through a number of ways, including a network  14 . The computing environment  10  illustrated is a grid-based computing environment that includes multiple compute nodes, each containing one or more servers or data processors (not shown). However, a grid-based computing environment may not be required. One or more data stores  16  may be coupled to the computing environment  10  to store data to be processed in the computing environment  10  as well as to store any intermediate or final data generated by the computing environment. Computer-readable memory  18  may also be coupled to the computing environment  10  for use by the data processors when processing data. An example application for the computing environment  10  involves the performance of data mining, in general, and initial data exploration before the start of data analysis, in particular. 
       FIG. 2  illustrates hardware components for an example grid-based computing system  10 , which is the computing environment  10  in  FIG. 1 . The grid-based computing system  10  includes a number of data processing nodes  20 ,  22  comprising multi-core data processors  21 ,  24  in this example. One of the nodes is designated a control or root data processing node  20  and a plurality of the nodes are designated as worker data processing nodes  22 . Each data processing node  20 ,  22  may include computer-readable memory  26  that is accessible to the data processor associated with that node. 
     The various data processing nodes  20 ,  22  are connected via a network  28  and can communicate with each other using a predetermined communication protocol such as the Message Passing Interface (MPI). The root data processor  21  at the control node  20  can communicate with a client application  29  over a communication path  30  to receive ad hoc queries from a user and to respond to those ad hoc queries after processing data. 
       FIG. 3  depicts an example grid-based computing system that is configured to execute a method for fast identification of data variable roles during initial data exploration. This example system includes a root data processor  31  and a plurality of worker node data processors  32 ,  33 , wherein one of the worker node data processors is designated as a particular worker node data processor  33  for illustrative purposes. The root data processor  31  is operative to receive data records  34  of a data set on which fast identification of data variable roles will be performed during an initial data exploration operation. The root data processor  31  can communicate bi-directionally with each of the worker node data processors  32 ,  33 , and each of the worker node data processors  32 ,  33  can communicate bi-directionally with the other worker node data processors  32 ,  33 . Alternatively (or in addition), the worker node data processors  32 ,  33  may have data records  45  of a portion of the data set pre-distributed to the node instead of all data records  34  of the complete data set being initially stored at the root data processor node. 
     Also, depicted are computer-readable memory  35  coupled to the root data processor  31  and computer-readable memory  36  coupled to the particular node data processor  33 . In some implementations, the computer-readable memory  36  includes a sorted data structure  38  for capturing unique data values and unique data value counts for variables analyzed by the particular node data processor. The computer-readable memory  36  also captures an overflow count  40  for variables analyzed by the particular node data processor. The computer-readable memory  36  and its contents are illustrative of computer-readable memory (not shown) that is coupled to the other node data processors  32 . 
     The computer-readable memory  35  coupled to the root data processor  31  includes a consolidated data structure  42  for combining and recording consolidated data values and counts received from the sorted data structures  38  from the various node data processors  32 ,  33 . The computer-readable memory  35  also captures a consolidated overflow count  44  by combining and consolidating unique overflow counts  40  received from the various node data processors  32 ,  33 . 
       FIG. 4  depicts an example process performed by a control node data processor of one or more of the systems of  FIGS. 1-3  for identifying data variable roles during initial data exploration. At operation  100 , a request is received from a client application or user. The request  102  in this example includes the identity of the data set to be explored, the maximum number of levels allowed for each variable explored, and the identity of the variables to be explored. 
     At operation  104 , the control node assigns tasks to one or more worker nodes. The task assignments in this example may be broadcast to all worker nodes. The task assignments include assigning each variable a specific worker node for consolidation of level information. The consolidation information for all variables is eventually sent to the control node. Every worker node is sent the tasking for all worker nodes. The specific assignment  106  for each worker node may include the identity of the data set, the maximum number of levels allowed for each variable explored, the identity of the variables to be explored, a specific variable assigned to a particular worker node, and the portion of the data set assigned to a particular worker node if the data has not been pre-distributed in  45  of  FIG. 3 . 
     At operation  108 , the control node receives the results of the analysis performed by the worker nodes. The results  110  may include the data values and counts for variables in the data set. In this example, since certain worker nodes are assigned specific variables, the control node may receive from certain worker nodes the values and total counts for their assigned variables. The control node in this case would consolidate all task results from the various reporting worker nodes. 
     At operation  112 , the control node may report the consolidated results to the client application or user. The consolidated results  114  may include the data values and counts for the variables specified by the client application or user in the request  102 . 
       FIG. 5  depicts an example process performed by a control node data processor to assign tasks to a plurality of worker node data processors. At operation  116 , the control node broadcasts the task assignments to all worker nodes. The assignment for each worker node may include the identity of the data set, the maximum number of levels (n) allowed for each variable explored, the identity of the variables to be explored, a specific variable assigned to a particular worker node, the portion of the data set assigned to a particular worker node, and a batch size (b) identifying the number of observations for a worker node to process before reporting its progress to other worker nodes. After the broadcast, the worker nodes may proceed with processing their portions of the data set (operation  118 ). 
       FIG. 6  depicts an example process performed by worker node data processors. At operation  120 , the worker node processors receive the broadcast information. The broadcast information  122  may include the identity of the data set, the maximum number of levels (n) allowed for each variable explored, the identity of the variables to be explored, a specific variable assigned to a particular worker node, the portion of the data set assigned to a particular worker node, and a batch size (b) identifying the number of observations for a worker node to process before reporting its progress to other worker nodes. 
     At operation  124 , each worker node processor begins executing its assignment. Assignment execution may involve retrieving its assigned portion of the data set, which contains observations to be processed, and processing a first batch of observations (operation  126 ). Processing observations may involve generating and updating a binary tree for each encountered variable, wherein the binary tree can have no more than the maximum number of levels (n). After a batch size (b) of observations has been processed, each worker node processor broadcasts information regarding its binary trees to allow the collective group of worker node processors to update level caps and prune their binary trees (operation  128 ). After tree pruning, each worker node processor processes another batch size (b) of observations (operation  126 ) followed by additional level cap updates and binary tree pruning (operation  128 ). This cycle repeats until all of the observations are processed. After all of the observations are processed, the worker node processors begin to merge their data (operation  130 ). After the data merge, the worker node processors report the results relating to their assigned variables to the control node processor (operation  132 ). The results  134  may include the data values and counts for the variables specified by the client application or user in the request that initiated the analysis. 
       FIG. 7  depicts an example process performed by a particular worker node processor when executing its assignment. At operation  136 , a particular worker node processor processes a first batch of observations. Processing observations may involve generating and updating a binary tree for each encountered variable, wherein the binary tree can have no more than the maximum number of levels (n). After a batch size (b) of observations has been processed, the worker node processor begins the process of updating the level caps for its binary trees and pruning the binary trees (operation  138 ). During this operation, the worker node processor broadcasts information regarding its binary trees. In particular, the worker node processor broadcasts for each variable the value of the nth level in the variable&#39;s binary tree to the other worker node processors (operation  140 ). The particular worker node processor at operation  140  also listens for the nth level of corresponding binary trees prepared by the other worker node processors. After receiving the nth level of corresponding binary trees, the particular worker node processor adjusts its binary trees (operation  142 ) by setting its cap level to the most restrictive of the nth levels received from the other worker node processors and prunes its binary trees. 
     After tree pruning, the particular worker node processor determines if there are more observations to be processed (operation  144 ) and processes another batch size (b) of observations (operation  136 ) if more observations are available for processing. If no more observations are available for processing, the particular worker node processor begins the process of updating the level caps for its binary trees and pruning the binary trees (operation  146 ) one last time. During this operation, the worker node processor broadcasts for each of its variables the value of the nth level in the variable&#39;s binary tree to the other worker node processors (operation  148 ) and listens for the nth level of corresponding binary trees prepared by the other worker node processors. After receiving the nth level of corresponding binary trees, the particular worker node processor adjusts its binary trees (operation  150 ) by setting its cap level to the most restrictive of the nth level received from the other worker node processors and prunes its binary trees. After tree pruning, the particular worker node processor begins the data merge process (operation  152 ). 
       FIG. 8  depicts an example process performed by a particular worker node data processor to process its assigned observations. Each worker node passes through its data creating binary trees of the top n values of each variable. With each new observation, each binary tree is updated. At operation  154 , the particular worker node data processor determines if the observation value for a variable is greater than a level cap in the binary tree for that variable. If the value is greater than the cap, then an “other count” counter is incremented (operation  156 ) and the processing of that observation is completed. If the value is not greater than the cap, then the particular worker node data processor determines if the observation value has already been seen (operation  158 ). If the level has already been seen, then a frequency counter for that level is incremented (operation  160 ) and the processing of that observation is completed. If the level has not already been seen, then the particular worker node data processor determines if the binary tree already has n levels (operation  162 ). If the binary tree does not have n levels, then a level equal to the value of the observation is inserted into the binary tree (operation  164 ), a frequency count for the level is established, and the processing of that observation is completed. If the binary tree does have n levels, then a level equal to the value of the observation is inserted into the binary tree, the largest level is pruned (or deleted) from the binary tree, the other count counter is incremented by the number in the frequency counter for the pruned level, the level cap is updated to be equal to the value of the largest level (operation  166 ), and a frequency count for the level is established. The processing of that observation is then completed. After an observation is processed, then the next observation is processed until the batch (b) number of observations has been processed or the last observation has been processed, whichever occurs first. 
       FIG. 9  depicts an example process performed by a particular worker node data processor to update level caps and prune trees after a batch (b) number of observations has been processed or the last observation has been processed. Periodically, every worker node will broadcast to every other worker node the value cap in each of their trees if the trees are of size n. If the trees are of a size strictly less than n, then a special value indicating that no value cap is available for this particular worker node is broadcast. The most restrictive of these value caps is a bound on the value of the nth distinct value. All tree nodes violating this value bound are pruned from every tree. Local memory usage may be reduced as a result of pruning. At operation  170 , the particular worker node data processor broadcast to other worker nodes the maximum value in each tree if tree has size n. At operation  172 , the particular worker node data processor receives broadcasts from other worker nodes containing the maximum value in their trees. Although this example shows operation  170  occurring before operation  172 , this is not required. In appropriate situations, operation  172  may occur at the same time as or prior to operation  170 . At operation  174 , the particular worker node data processor determines for each tree at the node if the minimum broadcasted value for the tree is less than the level cap for the tree, then prunes the largest value from the tree, inserts the minimum broadcasted value in the tree, and sets the level cap to the minimum broadcasted value. 
       FIG. 10  depicts an example process performed by a particular worker node data processor to update level caps and prune trees after a batch (b) number of observations has been processed or the last observation has been processed. The process depicted in  FIG. 10  is similar to that of  FIG. 9  except that instead of creating trees with the minimum n levels the process creates trees with the maximum n levels. Periodically, every worker node will broadcast to every other worker node the value cap in each of their trees if the trees are of size n. If the trees are of a size strictly less than n, then a special value indicating that no value cap is available for this particular worker node is broadcast. The most restrictive of these value caps is a bound on the value of the nth distinct value. All tree nodes violating this value bound are pruned from every tree. Local memory usage may be reduced as a result of pruning. At operation  171 , the particular worker node data processor broadcast to other worker nodes the minimum value in each tree if tree has size n. At operation  173 , the particular worker node data processor receives broadcasts from other worker nodes containing the minimum value in their trees. Although this example shows operation  171  occurring before operation  173 , this is not required. In appropriate situations, operation  173  may occur at the same time as or prior to operation  171 . At operation  175 , the particular worker node data processor determines for each tree at the node if the maximum broadcasted value for the tree is greater than the level cap for the tree, then prunes the smallest value from the tree, inserts the maximum broadcasted value in the tree, and sets the level cap to the maximum broadcasted value. 
     Depicted in  FIG. 11  is a flow chart illustrating an example process performed by worker node data processors when merging data from other worker node data processors relating to an assigned variable. At operation  176 , a particular worker node processor receives for its assigned variable a level value and a frequency count for that level from another worker node processor. At operation  178 , the particular worker node processor processes that level as if it was an observation and adds the frequency count to the appropriate counter. After the level is processed, an additional level is processed if it exists (operation  180 ). 
     In particular, to process a level a particular worker node processor determines if the observation level has a value that is greater than a level cap in the binary tree for that variable. If the value is greater than the cap, then the “other count” counter is incremented by the amount of the frequency count for the level. If the value is not greater than the cap, then the particular worker node data processor determines if the level value is already in the binary tree. If the level value is already in the binary tree, then the frequency count for that level in the binary tree is incremented by the amount of the frequency count for the received level. If the level value is not already in the binary tree, then the particular worker node data processor determines if the binary tree already has n levels. If the binary tree does not have n levels, then a level equal to the value of the received level is inserted into the binary tree and a frequency count for the level is set to the frequency count for the received level. If the binary tree does have n levels, then a level equal to the value of the received level is inserted into the binary tree, the largest level is pruned (or deleted) from the binary tree, the other count counter is incremented by the number in the frequency counter for the pruned level, frequency count for the new level is set to the frequency count for the received level, and the level cap is updated to be equal to the value of the largest level, a frequency count for the level is established. 
       FIG. 12  depicts another example method for identifying data variable roles during initial data exploration. This method is appropriate for either a grid-based computing environment or a standalone computing environment. In this example a computing system having a control node and two compute nodes are used. The control node and compute nodes may be in either a grid-based computing environment or a standalone computing environment. At operation  200  the control node receives a request from a user specifying the data set, the variables (C1 and N1 in the examples in  FIGS. 13-17 ) and a threshold for the number of levels returned (4 in the examples in  FIGS. 13-17 ). 
     At operation  202 , the problem description is sent to the compute nodes. The control node sends the complete problem description to each of the two compute nodes. This includes operational information such as the number of records to process before broadcasting the 4 th  largest observed level and information regarding which compute node is assigned to perform the final aggregation of levels for each variable. 
     Depicted in  FIG. 13  are two tables that, respectively, show the observations that are assigned to compute node 1 and compute node 2. Each table contains observation values for the two variables, C1 and N1. 
     Referring again to  FIG. 12 , at operation  204 , the compute nodes process observations. Each compute node processes its assigned observations and creates a local tree of the top 4 levels for each variable. Local binary trees are used to keep an ordered copy of the top 4 levels. A description of an example type of binary tree that may be used can be found at Donald Knuth. The Art of Computer Programming, Volume 3, Second Edition. Addison-Wesley, 1998. Pages 426-454, although many other binary tree implementations may be used. The compute nodes process observations in parallel and each variable is processed in a single pass. 
     The collection of tables at  FIGS. 14   a  and  14   b  show the state of observed levels recorded in the binary trees after each observation is processed. Changes from the previous state are indicated in bold type. Each of observations 1-4 are recorded in the binary trees and are shown in the tables of  FIGS. 14   a  and  14   b . It is not until observation 5 for each compute node is processed that levels are pruned from the binary trees. 
     The fifth observation is the first instance where there are more than 4 observed levels of Variable N1. At Worker1, the addition of the 29.9 level causes the largest level, 65.3, to be removed from the list and its frequency added to the “Other” level. On Worker2, the 60.5 level causes the 72.1 level to be removed from the list and its frequency added to the “Other” level. 
     After processing the fifth observation, an intermediate pruning of the variable N1 occurs. Each compute node broadcasts its current 4 th  level (Worker1 sends 51, Worker2 sends 60.5). The value 60.5 is removed from Worker2 (since 60.5&gt;51) and its frequency is added to the “Other” level. The stored levels after this pruning operation are shown in row  5 P. Notably, the list for Variable N1 on Worker2 has only 3 levels. When a new level is observed, it will only be added to the list if it is less than or equal to the value used during the last pruning phase, 51. The intermediate pruning done in this operation is optional. 
     During processing of the sixth observation at Worker1, another pruning of the variable N1 occurs. The value of 51 is removed and its frequency is added to “Other”. N1=38.6 is the last value in its tree. During processing of the sixth observation at Worker2, the level 2.1 is added and no pruning is necessary. Shown in the final two tables of  FIG. 14   b , are the states of the binary trees after all six observations have been processed at both compute nodes. 
     No pruning was needed for the variable C1 on either compute node since the cardinality of C1 was not greater than 4. Also, during the processing of the observations, pruning of any variable can take place as soon as the cardinality of the variable processed at any node reaches the maximum level set by the user. 
     Referring again to  FIG. 12 , at operation  206 , the compute nodes broadcast maximum level values. After all observations are processed, each compute node broadcasts its 4th level of each variable one last time (or a special value indicating there is no 4th level). 
     At operation  208 , final pruning is done. Once the broadcast of largest level values occurs final pruning can begin. For Variable C1, none of the worker node has attained the preset maximum number of level so no pruning occurs for Variable C1 levels. For Variable N1, Worker1 broadcasts 38.6 and Worker2 broadcasts 35.2. Since the Worker2 maximum level is lower, the Worker 1 level list is pruned. The final level lists for both worker nodes are shown in  FIG. 15 . 
     At operation  210 , data merge takes place. The values for Variable C1 are merged on Worker1. In this case C1 has the same levels on both nodes. An upper bound of 6 on the cardinality after the merge of C1 is possible depending on the levels on each node. Since the two worker nodes contain the same levels, the cardinality of C1 (3) after the 3 merges will remain the same. Only the frequency values will be updated. Arrows in  FIG. 16  indicate how levels on one compute node are merged with the other compute node. 
     Merging of the values for N1 is illustrated in  FIGS. 17A-17E . In this example, merging involves adding the nodes from the tree on Worker 1 to the nodes of the tree that exists on Worker2. In particular,  FIG. 17A  illustrates the merger of level 17.3 from Worker 1 with the Worker 2 table.  FIG. 17B  illustrates the merger of level 21.2 from Worker 1 with the Worker 2 table.  FIG. 17C  illustrates the merger of level 29.2 from Worker 1 with the Worker 2 table.  FIG. 17D  illustrates the merger of the Other data from Worker 1 with the Other data in the Worker 2 table. And, the final tables in  FIG. 17E  illustrate the final result after the data merge is complete. The final tables resulting from the data merge can either be written to a distributed data set or sent to the control node and then output to a client. 
     In the examples of  FIGS. 8-9  and  13 - 17 , the level having the largest value is pruned when tree pruning is performed. Any of these examples, however, could alternatively be modified so that the level having the smallest value is pruned instead as illustrated in  FIG. 10 . 
     The operations depicted in  FIGS. 4-17  may be implemented by one or more processors executing programming instructions. The programming instructions may be stored in data stores and/or computer-readable memory. 
     The foregoing examples illustrate systems having separate control and worker nodes. Separate control and worker nodes, however, are not required. A control node may also function as a worker node. 
     Referring back to  FIGS. 1 and 2 , depicted are examples of systems that may be used to identify data variable roles during initial data exploration.  FIG. 1 , in particular, depicts an example client/server environment, and  FIG. 2  depicts a system that can be used in either a standalone environment or a client/server environment. 
       FIGS. 1 and 2  also depict example grid-based computing systems that may be used to identify data variable roles during initial data exploration, but a grid-based computing system is not required. The control node could also function as a worker node in a system containing only a single worker node. In that case, the system may comprise a single computer. 
       FIG. 18  shows a block diagram of example hardware for either standalone or client/server computer architecture  850 , such as the architecture depicted in  FIGS. 1 and 2  that may be used to contain and/or implement the program instructions of system embodiments of the present disclosure. A bus  852  may connect the other illustrated components of the hardware. A processing system  854  labeled CPU (central processing unit) (e.g., one or more computer processors), may perform calculations and logic operations required to execute a program. A processor-readable storage medium, such as read only memory (ROM)  856  and random access memory (RAM)  858 , may be in communication with the processing system  854  and may contain one or more programming instructions for performing an index join operation. Optionally, program instructions may be stored on a computer readable storage medium such as a magnetic disk, optical disk, recordable memory device, flash memory, or other physical storage medium. Computer instructions may also be communicated to other systems, components or devices. 
     A disk controller  860  interfaces one or more optional disk drives to the system bus  852 . These disk drives may be external or internal floppy disk drives such as  862 , external or internal CD-ROM, CD-R, CD-RW or DVD drives such as  864 , or external or internal hard drives  866 . As indicated previously, these various disk drives and disk controllers are optional devices. 
     Each of the element managers, real-time data buffer, conveyors, file input processor, database index shared access memory loader, reference data buffer and data managers may include a software application stored in one or more of the disk drives connected to the disk controller  860 , the ROM  856  and/or the RAM  858 . Preferably, the processing system  854  may access each component as required. 
     A display interface  868  may permit information from the bus  852  to be displayed on a display  870  in audio, graphic, or alphanumeric format. Communication with external devices may optionally occur using various communication ports  872 . 
     In addition to the standard computer-type components, the hardware may also include data input devices, such as a keyboard  874 , or other input device  876 , such as a microphone, remote control, pointer, mouse and/or joystick. 
     In some implementations, before performing analytics on a possibly large and distributed data set a determination can be made regarding the variables that can potentially be used as class variables or as numeric (interval) variables. Some variables may be suitable for inclusion in the analysis even if may they contain many distinct levels. In addition, getting accurate frequency counts for a subset of levels can provide additional insight into the data set. 
     The patentable scope of the described subject matter may include other examples. Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein. Other implementations may also be used, however, such as firmware or even appropriately designed hardware configured to carry out the methods and systems described herein. 
     The systems&#39; and methods&#39; data (e.g., associations, mappings, data input, data output, intermediate data results, final data results, etc.) may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program. 
     The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand. 
     It should be understood that the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise. Finally, as used in the description herein and throughout the claims that follow, the meanings of “and” and “or” include both the conjunctive and disjunctive and may be used interchangeably unless the context expressly dictates otherwise; the phrase “exclusive or” may be used to indicate situation where only the disjunctive meaning may apply.