Patent Publication Number: US-8990165-B2

Title: Methods, apparatus and articles of manufacture to archive data

Description:
BACKGROUND 
     Databases storing millions of data entries are commonplace. To improve database performance, database archiving techniques are used to reduce the size of an active database, also referred to herein as a production database. Unlike a backup procedure that copies the entire contents of a database to another storage location, database archiving involves moving one or more portions (e.g., such as less-used portions) of the database to an archive, also referred to herein as an archive database. Furthermore, in many database applications, an archived portion of the database needs to be complete such that the archived portion is not functionally dependent on data remaining in the active database, and vice versa. For example, when the archived portion of the database corresponds to a business transaction, the archived portion should include all functionally interdependent entries making up the archived business transaction to enable the archived business transaction to be recovered later from the archive. If the archived business transaction remains functionally dependent on data remaining in the active database, that remaining data may later be deleted or lost, making the archived business transaction unrecoverable. Conversely, if the active database remains functionally dependent on data in the data archive, the active database may become corrupted or otherwise function improperly when the archived portion is removed from the active database, which can be manifested in the form of “data not found” or similar errors. Accordingly, many database archiving techniques employ data modeling to model the functional dependencies in a database. 
     SUMMARY 
     Methods, apparatus and articles of manufacture to archive data are disclosed. A method to archive data disclosed herein comprises determining an initial data model representing functional dependencies among attributes of the data, the initial data model having fully interdependent functional dependencies among all attributes of the data, pruning one or more functional dependencies from the initial data model to determine a verified data model, and archiving a transaction included in the data to memory according to the verified data model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is block diagram of an example environment of use for an example data archiver to archive data according to a functional dependency data model as described herein. 
         FIG. 2  illustrates an example functional dependency between tables of a database. 
         FIG. 3A  illustrates example prior art functional dependency data modeling. 
         FIG. 3B  illustrates example operation of the data archiver of  FIG. 1  to perform functional dependency data modeling according to the methods and apparatus described herein. 
         FIG. 4  is a block diagram of an example data modeler that may be used to implement the data archiver of  FIG. 1 . 
         FIG. 5  is a flowchart representative of example machine readable instructions that may be executed to implement the data archiver of  FIG. 1 . 
         FIG. 6  is a flowchart representative of example machine readable instructions that may be executed to implement user interface processing in the data modeler of  FIG. 4  and/or the data archiver of  FIG. 1 . 
         FIG. 7  is a flowchart representative of example machine readable instructions that may be executed to implement candidate key verification in the data modeler of  FIG. 4  and/or the data archiver of  FIG. 1 . 
         FIG. 8  is a flowchart representative of example machine readable instructions that may be executed to implement functional dependency verification in the data modeler of  FIG. 4  and/or the data archiver of  FIG. 1 . 
         FIG. 9  is a flowchart representative of example machine readable instructions that may be executed to implement data model pruning in the data modeler of  FIG. 4  and/or the data archiver of  FIG. 1 . 
         FIG. 10  is a block diagram of an example processing system that may execute the example machine readable instructions of  FIGS. 5-9  to implement the data archiver of  FIG. 1  and/or the data modeler of  FIG. 4 . 
     
    
    
     DETAILED DESCRIPTION 
     Methods, apparatus and articles of manufacture to archive data are disclosed herein. Although the following discloses example methods and apparatus including, among other components, software executed on hardware, it should be noted that such methods and apparatus are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware and software components could be implemented exclusively in hardware, exclusively in software, exclusively in firmware, or in any combination of hardware, software, and/or firmware. Accordingly, while the following describes example methods and apparatus, it will readily be appreciated that the examples provided are not the only way to implement such methods and apparatus. Furthermore, wherever possible, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts, objects, features, functions, etc. 
     An example data archiving technique disclosed herein involves determining an initial data model representing functional dependencies among attributes of the data. The initial data model is assumed to have fully interdependent functional dependencies among all attributes of the data. Two attributes are functionally dependent when the values of the first attribute are, for example, dependent on, derivable from, determinable from, etc., the values of the second attribute. A functional dependency in the data model indicates that a first set of attributes of a first data set depends upon a second set of attributes of the first data set or a second data set. The first set of attributes is referred to herein as a dependent attribute set and can include, for example, one or more attributes associated with one or more columns of a first table in a database. The second set of attributes is referred to herein as a parent attribute set and can include, for example, one or more other attributes associated with one or more other columns of the first table or a second table in the database. Consistent with database vernacular, each parent attribute set is considered to make up a respective key specifying attribute values that can be used to uniquely describe the data entries in a database. 
     Additionally, the example disclosed data archiving technique involves pruning one or more functional dependencies from the initial data model to determine a verified data model. As noted above, the initial data model is assumed to have fully interdependent functional dependencies among all attributes of the data or, in other words, the initial data model assumes the attributes (e.g., columns) of the data (e.g., databases) are fully, or all-to-all, interdependent. In some examples, the disclosed data archiving prunes, or removes, functional dependencies by determining a set of candidate keys comprising respective parent attribute sets, and for each candidate key either pruning the particular candidate key from the set of candidate keys when samples of the data indicate the particular candidate key cannot be an actual key, or increasing a confidence value associated with the particular candidate key when the data samples indicate that the particular candidate key is likely an actual key. Additionally or alternatively, the disclosed data archiving prunes, or removes, functional dependencies by determining a set of functional dependencies for a set of candidate keys, and for each functional dependency either increasing a confidence value associated with the particular functional dependency when samples of the data indicate that the particular dependent attribute set corresponding to the particular functional dependency does depend upon an associated candidate key, or decreasing the confidence value associated with the particular functional dependency when the data samples indicate that the particular dependent attribute set corresponding to the particular functional dependency does not depend upon the associated candidate key. In some examples, after completion of a number of processing iterations, the verified data model is determined by pruning candidate keys (and associated functional dependencies) having confidence values lower than a threshold. Additionally or alternatively, individual functional dependencies having confidence values lower than the same or a different threshold can be pruned to determine the verified data model. 
     Furthermore, the example disclosed data archiving technique involves archiving a transaction included in the data to memory according to the verified data model. A transaction typically includes multiple data entries (e.g., database rows) across one or more data sets (e.g., one or more database tables), where each entry has one or more attributes (e.g., one or more database columns). In some examples, the verified data model can be used to ensure that all functionally dependent data entries making up the transaction are included in the archive (e.g., which would make the archived transaction complete and recoverable). Additionally, the verified data model can be used to ensure that the archive does not include any data entries upon which data entries in the actual (e.g., production) database are still functionally dependent (e.g., which avoids causing the actual database to become corrupted or otherwise function improperly). 
     The example disclosed data archiving technique also involves processing a user interface that accepts one or more commands input by a user. An example set of commands that can be accepted via the user interface include, but are not limited to: (1) a first command to force removal of a specified candidate key from the data model, (2) a second command to force inclusion of the specified candidate key in the data model and to cause a confidence value associated with the specified candidate key to be set to a maximum value (e.g., such as 100%), (3) a third command to force removal of a specified functional dependency from the data model, and (4) a fourth command to force inclusion of the specified functional dependency in the data model and to cause a confidence value associated with the specified functional dependency to be set to the maximum value (e.g., such as 100%). 
     Prior data archiving techniques that employ data modeling typically begin with the assumption that there are no functional dependencies in the data. These prior techniques then use various approaches to discover functional dependencies in the data. Such discovery techniques may take considerable time to discover all the functional dependencies and, if processing is terminated early, some functional dependencies may not be discovered. Additionally, some functional dependencies may not be discovered even if the prior discovery techniques are performed to completion. However, missing just one functional dependency can cause the resulting archived data to be unrecoverable, or may render the remaining unarchived data corrupt or incomplete. 
     Unlike such prior techniques, the example disclosed methods, apparatus and articles of manufacture to archive data employ functional dependency modeling that initially assumes that the data is characterized by fully interdependent functional dependencies. In other words, all attributes (e.g., columns) of the data (e.g., database) are initially assumed to be all-to-all functional dependent. Then, in some examples, each candidate key of each assumed functional dependency, as well as each assumed functional dependency itself, is verified against samples of the data and removed from the data model if the data suggests the functional dependency is not actually present in the data. The remaining, verified candidate keys and associated functional dependencies form the verified data model. By beginning with the assumption that the data has fully interdependent functional dependencies, and then not removing an assumed dependency unless that data itself indicates the assumed dependency is not valid, the resulting verified data model tends to ensure that all actual functional dependencies are included in the model. By ensuring that the verified data model includes all actual functional dependencies, even if additional assumed functional dependencies that are not actual dependencies are included, the verified data model can be used by the example disclosed methods, apparatus and articles of manufacture to perform data archiving in which the resulting archived data (e.g., such as the archived transaction) can be guaranteed to be complete and recoverable, and the remaining unarchived data (e.g., such as the active database) can also be guaranteed to be complete and not corrupt (e.g., assuming proper user configuration, such as proper setting of one or more of the thresholds described below, and proper user input during operation). 
     Furthermore, it is unlikely that many, if not all, prior data archiving techniques are able to discover all functional dependencies and still complete in a practical amount of time. This is because the problem of discovering functional dependencies from actual data (in contrast to functional dependency verification as performed by the example disclosed methods, apparatus and articles of manufacture) is known to be a computationally complex problem. In contrast, the example disclosed data modeling techniques based on functional dependency verification (instead of discovery) can be stopped at any time during execution and still provide a useable data model that can guarantee data integrity (e.g., through setting of appropriate thresholds, through input of appropriate user commands, etc., as described in greater detail below). 
     Turning to the figures, a block diagram of an example environment of use  100  for an example data archiver  105  to archive data according to a functional dependency data model as described herein is illustrated in  FIG. 1 . The environment  100  includes an example production database  110  that represents an active database used to, for example, organize and store data into one or more data sets, with each data set containing data entries (also referred to as data instances, data tuples, data records, etc.) having one or more attributes (e.g., also referred to as characteristics, properties, etc.). In some examples, the production database  110  implements a relational database in which each data set is organized as a table having a column for each attribute and a row for each data entry. Such an example tabular relationship  200  that may be implemented by the production database  110  is illustrated in  FIG. 2 . 
     Turning to  FIG. 2 , the example tabular relationship  200  stores two data sets in two respective tables, table  205  and table  210 . In the illustrated example, table  205  stores two data entries in two respective rows  215  and  220  of the table  205 . The data entries in table  205  have a single attribute, ‘C,’ which is represented by a single column  225  of the table  205 . For example, the data entry corresponding to row  215  has a value of ‘1’ in column  225  for attribute ‘C,’ whereas the data entry corresponding to row  220  has a value of ‘2’ in column  225  for attribute ‘C.’ In comparison, table  210  stores five data entries in five respective rows,  230 ,  235 ,  240 ,  245  and  250  of the table  210 . The data entries in table  210  have two attributes, ‘C’ and ‘Q,’ which are represented by two respective columns  255  and  260  of the table  210 . For example, the data entry corresponding to row  230  has a value of ‘1’ in column  255  for attribute ‘C’ and a value of ‘1’ in column  260  for attribute ‘Q,’ the data entry corresponding to row  235  has a value of ‘1’ in column  255  for attribute ‘C’ and a value of ‘2’ in column  260  for attribute ‘Q,’ the data entry corresponding to row  240  has a value of ‘1’ in column  255  for attribute ‘C’ and a value of ‘3’ in column  260  for attribute ‘Q,’ the data entry corresponding to row  245  has a value of ‘2’ in column  255  for attribute ‘C’ and a value of ‘4’ in column  260  for attribute ‘Q,’ and the data entry corresponding to row  250  has a value of ‘2’ in column  255  for attribute ‘C’ and a value of ‘5’ in column  260  for attribute ‘Q.’ 
     In general, an attribute provides meaning to a data value of a data entry. For example, at first glance the values stored in tables  205  and  210  may appear to be just sets of meaningless numbers. However, these values can become meaningful with knowledge of the meaning of the attributes ‘C’ and ‘Q’ represented by columns  225 ,  255  and  260 . For example, if ‘C’ corresponds to customer number, and ‘Q’ corresponds to purchase quantity, then it becomes more apparent that table  205  stores the customer numbers for two different customers, and table  210  stores quantities of items purchased by the customers whose customer numbers are stored in table  205 . Furthermore, data attributes can be used to group data entries into transactions (e.g., also referred to as business transactions, database transactions, etc.) that share one or more common attributes. For example, if ‘C’ corresponds to customer number, and ‘Q’ corresponds to purchase quantity in tables  205  and  210 , then the data entries corresponding to row  215  of table  1  and rows  230 - 240  of table  210  can be grouped into a first transaction  265  associated with the customer having a customer number of ‘1’ for the attribute ‘C’ of columns  225  and  255 . Similarly, the data entries corresponding to row  220  of table  205  and rows  245 - 250  of table  210  can be grouped into a second transaction  270  associated with the customer having a customer number of ‘2’ for the attribute ‘C’ of columns  225  and  255 . 
     Returning to  FIG. 1 , the data archiver  105  is to archive data from the production database  110  into an example archive  115 , such as an archive database  115 . The archive  115  can be separate from, co-located with, or implemented as a portion of the production database  110 , or any combination thereof. The production database  110  and the archive database  115  can be implemented using any types or combinations of storage and/or memory elements, such as the example volatile memory  1018  and/or the mass storage device  1030  of the example processing system  1000  of  FIG. 10 , which is discussed in greater detail below. 
     In the illustrated example, the data archiver  105  archives transactions stored in the production database  110  to the archive  115 . As described above, a database transaction is made up of a group of data entries (e.g., database rows) sharing one or more common attributes (e.g., database columns). The data attributes provide meaning to the data entries. Furthermore, the collection of data attributes of the data stored in the production database  110  and the functional dependencies among the collection of attributes form a data model describing the data entries, their functional dependencies and, therefore, how the data entries are groupable to form complete transactions. 
     For example, with reference to  FIG. 2 , an example data model indicates that the data entries of table  205  are characterized by the single attribute ‘C’ represented by the column  225  and the data entries of table  210  are characterized by the two attributes ‘C’ and ‘Q’ represented by the two column  255  and  260 . Additionally, the data model indicates that table  210  functionally depends on table  205  and, in particular, that column  255  of table  210  (representing the attribute ‘C’) functionally depends on column  225  of table  205  (also representing the attribute ‘C’). Such a functional dependency can be written as table  205 .C←table  210 .C to indicate that attribute ‘C’ represented by column  225  in table  205  is a parent attribute (or parent column) specifying the unique values that this attribute (or column) can have, and that attribute ‘C’ represented by column  255  in table  210  is a dependent attribute (or dependent column) containing one or more data entries having respective values of this attribute (or column) as specified by the attribute ‘C’ represented by column  225  of table  205 . 
     More generally, in a parent attribute and dependent attribute combination, the data set (e.g., table) associated with the parent attribute includes data entries specifying unique, permissible values that this attribute can have, whereas the data set (e.g., table) associated with the dependent attribute includes data entries having values for the dependent attribute that belong to the set of unique, permissible values specified by the parent attribute. Accordingly, the data entries in the data set (e.g., table) specifying the parent attribute are such that each data entry in the data set (e.g., table) has a unique (e.g., different) value of the attribute. For example, the attribute ‘C’ of column  225  in table  205  of  FIG. 2  could be a parent attribute because each data entry in table  205  has a unique value for this attribute. A parent attribute also defines a set of attribute values that can be used to identify transactions containing this attribute. For example, the first transaction  265  corresponding to row  215  of table  1  and rows  230 - 240  of table  210  have a unique value (‘1’) for the attribute ‘C,’ and the second transaction  270  corresponding to row  220  of table  1  and rows  245 - 250  of table  210  have a unique value (‘2’) for the attribute ‘C.’ In some examples, attributes that are not unique themselves can be combined to form a parent set of attributes specifying a combination of attribute values that are unique and can be used to uniquely associate data entries belonging to a particular transaction. For example, if a database includes a table of data containing a ‘name’ attribute (e.g., column) specifying a customer&#39;s name and an ‘address’ attribute (e.g., column) specifying a customer&#39;s address, each of these attributes by itself may not be unique because different customers may have the same name and different customers may reside at the same address. However, taken together, the set of attributes containing the ‘name’ and ‘address’ attributes can form a parent attribute set for which each data entry in the table has a unique value. In database vernacular, a parent attribute or parent set of attributes is referred to as a ‘key’ that uniquely describes the data. Also, as used herein, a set of attributes can include none, one or more attributes. 
     Relative to a particular parent attribute or parent set of attributes, a dependent attribute or dependent set of attributes does not specify the unique values of this attribute or set of attributes. Instead, the dependent attribute or dependent set of attributes takes on permissible values as specified by its associated parent attribute or parent set of attributes. For example, the attribute ‘C’ of column  255  in table  210  of  FIG. 2  is not a parent attribute because each data entry in table  210  does not have a unique value for this attribute. However, the attribute ‘C’ of column  255  in table  210  could be a dependent attribute of the parent attribute ‘C’ of column  225  in table  205  because the attribute ‘C’ of column  255  takes on values as specified by the parent attribute ‘C.’ 
     Although functionally dependent attributes (e.g., such as a parent attribute and its associated dependent attribute) often have the same name, the data model may specify functionally dependent attributes having different names. For example, a data model could specify that ‘customer number’ is a parent attribute specified by one table, and ‘customer identifier’ is a dependent attribute in a second table, where the ‘customer identifier’ attribute is to take on values of the ‘customer number’ attribute. Also, as used herein, the terms “attribute” and “column” are interchangeable, the terms “data entry” and “row” are interchangeable, and the terms “data set” and “table” are interchangeable, unless noted otherwise. 
     Returning to  FIG. 1 , to enable determination of the groups of data entries forming complete transactions in the production database, the data archiver  105  includes an example data modeler  120 . The data modeler  120  operates to determine the data model describing the functional dependencies between the attributes of the data stored in the production database  110  from the data entries and attributes stored therein. In other words, the data modeler  120  uses data entries and attributes stored in the production database  110  to determine or reverse engineer the data model describing the functional dependencies between attributes and, thus, between data entries. The data model determined by the data modeler  120  is stored in an example data model storage  125 , which can be implemented using any types or combinations of storage and/or memory elements, such as the example volatile memory  1018  and/or the mass storage device  1030  of the example processing system  1000  of  FIG. 10 , which is discussed in greater detail below. 
     An example transaction archiver  130  included in the data archiver  105  uses the data model stored in the data model storage  125  to archive data (e.g., transactions) from the production database  110  to the archive  115  according to the determined (e.g., reverse engineered) data model. In particular, the transaction archiver  130  uses the functional dependencies specified by the determined data model to identify related data entries belonging to a transaction to be archived. For example, a particular transaction to be archived may be specified (e.g., via an example terminal  135  described in greater detail below) using a value of a parent attribute or values of a parent set of attributes. The transaction archiver  130  then examines the functional dependencies between attributes as specified by the data model and, using any appropriate functional dependency-based archiving technique, traverses the dependencies to identify data entries having attributes that functionally depend on the parent attribute value(s) identifying the transaction to be archived. The transaction archiver  130  can also examine other attributes of the identified data entries to identify respective parent attribute(s) of these other attributes. The transaction archiver  130  can then continue this process to iteratively determine all data entries that are functionally dependent on the originally specified parent attribute value(s) for the transaction to be archived. After all functionally dependent data entries are identified, the transaction archiver  130  archives the identified data entries forming specified transaction to the archive  115 . For example, such archiving can involve copying the data entries (e.g., retaining their tabular relationships) to the archive  115  and then deleting these entries from the production database  110 . 
     As noted above, in many applications, transactions (or, more generally, data portions) archived by the data archiver  105  should be complete or, in other words, contain all functionally dependent data entries, to enable the archived transaction to be recovered from the archive  115  at a later time. Additionally, to avoid corruption of other incorrect operation, the archived transaction should be accurate and not contain data entries upon which other data entries remaining in the production database  110  are functionally dependent. As such, successful (e.g., complete and accurate) data archiving depends upon determining a data model specifying all functional dependencies in the data stored in the production database  110 . As noted above, prior art reverse engineering data modeling techniques typically begin with the assumption that there are no functional dependencies in the data, and then use various approaches to discover functional dependencies in the data. However, such prior data modeling techniques can take considerable time to complete and, if stopped prior to completion, can yield a data model that does not include all the functional dependencies. Unlike such prior techniques, the data modeling performed by the data modeler  120  initially assumes that the data is characterized by fully (e.g., all-to-all) interdependent functional dependencies between data attributes. Assumed functional dependencies are then removed, or pruned, by verifying the assumed functional dependency against the actual data stored in the production database. Such an approach tends to ensure that the determined data model includes all the actual functional dependencies exhibited by the data. The data model determined by the data modeler  120  may also include additional assumed functional dependencies that are not actual functional dependencies, but such over-inclusion of functional dependencies is usually of less concern than not including an actual functional dependency, which can occur with prior data modeling techniques. 
     To further illustrate a distinction between data modeling performed by the data modeler  120  relative to prior modeling techniques,  FIG. 3A  illustrates an example modeling operation  300  of a prior art data modeling technique to determine a data model specifying functional dependencies between data attributes. In the example of  FIG. 3A , the prior art data modeling technique is to determine a data model for a database that includes four tables (represented in the figure as a group of four circles). As described above, the modeling operation  300  of the prior art data modeling technique begins by assuming that there are no functional dependencies between the tables. In the example of  FIG. 3A , this assumption of no functional dependencies is represented by a table grouping  305  in which there are no relationships depicted between tables. Then, the prior art data modeling technique is performed (represented by a directed arrow  310 ) to determine the functional dependencies between the data tables and, more specifically, between attributes (or columns) of the data tables. In the example of  FIG. 3A , the resulting data model specifying the collection of functional dependencies is represented by a table grouping  315  in which the functional dependencies between tables are represented by directed arrows. 
     In contrast to the prior art modeling technique illustrated in  FIG. 3A ,  FIG. 3B  illustrates an example modeling operation  350  of the data modeler  120  to determine a data model specifying functional dependencies between data attributes. In the example of  FIG. 3B , the data modeler  120  is to determine a data model for the same database containing four tables that was the subject of the prior art modeling operation  300  of  FIG. 3A . However, unlike the prior art modeling operation  300 , the data modeler  120  begins the modeling operation  350  by assuming that there are fully interdependent (e.g., all-to-all) functional dependencies between the tables. In the example of  FIG. 3B , this assumption of fully interdependent (e.g., all-to-all) functional dependencies is represented by a table grouping  355  in which there are functional dependencies (represented by directed arrows) in both directions between each pair of tables and, more specifically, between each pair of attributes or sets of attributes (or columns) of each pair of tables. Then, the data modeler  120  performs its data modeling procedure (represented by a directed arrow  360 ) to determine the functional dependencies between the data tables and, more specifically, between attributes (or columns) of the data tables. In the example of  FIG. 3B , the resulting data model specifying the collection of functional dependencies is represented by a table grouping  365  in which the functional dependencies between tables are represented by directed arrows. The resulting table grouping  365  illustrates the possibility of over-inclusion of functional dependencies relative to the table grouping  315  of  FIG. 3A . However, as noted above, the over-inclusion of functional dependencies is usually of less concern than not including an actual functional dependency, which can occur with the prior data modeling technique whose operation is illustrated in  FIG. 3A , especially if the prior technique is terminated early. For example, because of the potentially high computational complexity for functional dependency determination in at least some example database applications, a user may wish to stop data modeling at some intermediate processing stage and still obtain a usable data model. Prior data modeling techniques based on functional dependency discovery are generally unable to provide such an early-termination feature, whereas the example disclosed data modeling techniques can support such early termination. 
     Returning to  FIG. 1 , the data archiver  105  interfaces with an example terminal  135 . The example terminal  135  can be implemented using any type of terminal, computer, device, etc., capable of receiving information from and presenting information to a user. In the illustrated example, the terminal  135  enables the data modeler  120  to accept input commands from a user at various stages of its data modeling procedure. The data modeler  120  is also able to present initial, intermediate and final modeling results to the user via the terminal  135 . Additionally, in some examples the terminal  135  can be used by a user to control the transaction archiver  130  by specifying one or more transactions (or, more generally, one or more portions) of the production database  110  to be archived to the archive  115 . 
     Although the example disclosed methods, apparatus and articles of manufacture are described in the context of data archiver  105  archiving database transactions stored in the production database  110 , the disclosed methods, apparatus and articles of manufacture are not limited thereto. For example, the disclosed methods, apparatus and articles of manufacture can be used to archive any type of data having attributes that exhibit functional dependencies. 
     A block diagram of an example implementation of the data modeler  120  of  FIG. 1  is illustrated in  FIG. 4 . In the illustrated example of  FIG. 4 , the data modeler  120  includes an example initializer  405  to initialize the data modeling procedure performed by the data modeler  120 . For example, the initializer  405  obtains the configuration data specifying the data sets (e.g., tables) and associated attributes (e.g., columns of the tables) for the data stored in the presentation database  110 . The initializer  405  then determines an initial data model in which functional dependencies are assumed to exist between all attributes of all data sets (e.g., the initial model assumes fully interdependent functional dependencies). In some examples, the configuration data is received via an example user interface  410  implemented using any appropriate interface technology for interfacing with the terminal  135 . Additionally or alternatively, the configuration data can be obtained from configuration files/information used to implement the production database  110 . 
     The data modeler  120  also includes an example candidate key selector  415  to determine a set of candidate keys for the functional dependencies included in the data model. As noted above, a key corresponds to a parent attribute or, more generally, a parent set of attributes for one or more functional dependencies between attributes, or sets of attributes. The set of keys determined by the candidate key selector  415  are referred to as candidate keys because during initial and intermediate processing, the keys are considered to be candidates that are to be verified against the data stored in the production database  110 . In some examples, the candidate key selector  415  initially assumes all attributes or sets of attributes are candidate keys. Additionally, the candidate key selector  415  can obtain configuration information received from the terminal  135  via the user interface  410  to select or discard one or more attributes or attribute sets from consideration as candidate keys. Furthermore, in some examples the candidate key selector  415  associates an initial confidence level (also referred to as a confidence value) with each candidate key that represents a confidence that the candidate key is an actual key for the data stored in the production database  110 . 
     Using the set of candidate keys selected by the candidate key selector  415 , an example candidate key verifier  420  included in the data modeler  120  verifies each candidate key against the data, or at least samples of the data, stored in the production database  110 . For example, the candidate key verifier  420  can determine whether a sample of data indicates that a particular candidate key violates one or more criteria for being a key. If the particular candidate key does violate one or more key criteria, the candidate key verifier  420  can remove, or prune, the particular candidate key from the set of candidate keys. However, if the data sample indicates that the particular candidate key does not violate the key criteria, then the candidate key verifier  420  can keep the particular candidate key in the set of candidate keys and also increase its associated confidence level. In some examples, the candidate key verifier  420  can iteratively compare each particular candidate key against different samples of the data, and at each iteration either prune the particular candidate key from the set or increase the particular candidate key&#39;s confidence level. Furthermore, in some examples the candidate key verifier  420  can obtain configuration information received from the terminal  135  via the user interface  410  to force one or more candidate keys to be pruned and/or force one or more other candidate keys to be accepted as actual keys (e.g., with a maximum confidence level of 100%). 
     The data modeler  120  further includes an example functional dependency selector  425  to determine a set of functional dependencies for each candidate key included in the current set of candidate keys. As noted above, a functional dependency is specified by a dependent attribute or dependent set of attributes (compactly referred to as a dependent attribute set) that depends upon a key made up of a parent attribute or a parent set of attributes (compactly referred to as a parent attribute set). Accordingly, for each particular candidate key included in the current set of candidate keys, the functional dependency selector  425  determines the functional dependencies for the particular candidate key by determining the dependent attribute sets for the particular candidate key. In some examples, the candidate key selector  415  initially assumes that, for a particular candidate key, all attributes or sets of attributes not included in the parent attribute set making up the candidate key are dependent attributes or dependent attribute sets of the particular candidate key (an assumption that follows from assuming fully interdependent functional dependencies in the initial data model). Additionally, the functional dependency selector  425  can obtain configuration information received from the terminal  135  via the user interface  410  to select or discard one or more attributes or attribute sets from consideration as a dependent attribute set for a particular candidate key. Furthermore, in some examples the functional dependency selector  425  associates an initial confidence level with each functional dependency that represents a confidence that the functional dependency is an actually dependent upon its associated candidate key. 
     Using the set of functional dependencies selected for each candidate key by the functional dependency selector  425 , an example functional dependency verifier  430  included in the data modeler  120  verifies each functional dependency against the data, or at least samples of the data, stored in the production database  110 . For example, the functional dependency verifier  430  can determine whether a sample of data indicates that, for a particular assumed functional dependency, the dependent attribute set does not appear to depend upon the associated candidate key (e.g., the associated parent attribute set). If the data sample indicates that the dependent attribute set does not appear to depend upon the associated candidate key, the functional dependency verifier  430  reduces the confidence level associated with that functional dependency. However, if the data sample indicates that the dependent attribute set does appear to depend upon the associated candidate key, the functional dependency verifier  430  increases the confidence level associated with that functional dependency. In some examples, the functional dependency verifier  430  can iteratively compare each particular functional dependency for each particular candidate key against different samples of the data, and at each iteration either decrease or increase the particular functional dependency&#39;s confidence level. Furthermore, in some examples the functional dependency verifier  430  can obtain configuration information received from the terminal  135  via the user interface  410  to force one or more functional dependencies to be pruned and/or force one or more other functional dependencies to be accepted as actual keys (e.g., with a maximum confidence level of 100%). 
     The data modeler  120  also includes an example data model processor  435  to determine a resulting verified data model from the set of candidate keys and associated confidence levels determined by the candidate key verifier  420  and the set of functional dependencies and associated confidence levels determined by the functional dependency verifier  430 . For example, the data model processor  435  can determine the verified data model by pruning candidate keys (and associated functional dependencies) having confidence values lower than a threshold (e.g., specified as a configuration parameter via the terminal  135 , hardcoded, etc.). Furthermore, the data model processor  435  can prune individual functional dependencies having confidence values lower than the same or a different threshold (e.g., specified as a configuration parameter via the terminal  135 , hardcoded, etc.) to determine the verified data model. The data model processor  435  then stores the verified data model to the data model storage  125 . 
     The operation and functionality of the data modeler  120  and its constituent elements are described in greater detail below in connection with the descriptions of  FIGS. 5-9 . 
     While example manners of implementing the data archiver  105  and the data modeler  120  have been illustrated in  FIGS. 1 and 4 , one or more of the elements, processes and/or devices illustrated in  FIGS. 1  and/or  4  may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example production database  110 , the example archive  115 , the example data model storage  125 , the example transaction archiver  130 , the example terminal  135 , the example initializer  405 , the example user interface  410 , the example candidate key selector  415 , the example candidate key verifier  420 , the example functional dependency selector  425 , the example functional dependency verifier  430 , the example data model processor  435  and/or, more generally, the example data modeler  120  of  FIGS. 1 and 4 , and/or the example data archiver  105  of  FIG. 1  may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example production database  110 , the example archive  115 , the example data model storage  125 , the example transaction archiver  130 , the example terminal  135 , the example initializer  405 , the example user interface  410 , the example candidate key selector  415 , the example candidate key verifier  420 , the example functional dependency selector  425 , the example functional dependency verifier  430 , the example data model processor  435  and/or, more generally, the example data modeler  120  and/or the example data archiver  105  could be implemented by one or more circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the appended apparatus claims are read to cover a purely software and/or firmware implementation, at least one of the example data archiver  105 , the example production database  110 , the example archive  115 , the example data modeler  120 , the example data model storage  125 , the example transaction archiver  130 , the example terminal  135 , the example initializer  405 , the example user interface  410 , the example candidate key selector  415 , the example candidate key verifier  420 , the example functional dependency selector  425 , the example functional dependency verifier  430  and/or the example data model processor  435  are hereby expressly defined to include a computer readable medium such as a memory, digital versatile disk (DVD), compact disk (CD), etc., storing such software and/or firmware. Further still, the example data archiver  105  of  FIG. 1  and/or the example data modeler  120  of  FIGS. 1  and/or  4  may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIGS. 1  and/or  4 , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowcharts representative of example machine readable instructions that may be executed to implement the example data archiver  105 , the example production database  110 , the example archive  115 , the example data modeler  120 , the example data model storage  125 , the example transaction archiver  130 , the example terminal  135 , the example initializer  405 , the example user interface  410 , the example candidate key selector  415 , the example candidate key verifier  420 , the example functional dependency selector  425 , the example functional dependency verifier  430  and/or the example data model processor  435  are shown in  FIGS. 5-9 . In these examples, the machine readable instructions represented by each flowchart may comprise one or more programs for execution by a processor, such as the processor  1012  shown in the example processing system  1000  discussed below in connection with  FIG. 10 . Alternatively, the entire program or programs and/or portions thereof implementing one or more of the processes represented by the flowcharts of  FIGS. 5-9  could be executed by a device other than the processor  1012  (e.g., such as a controller and/or any other suitable device) and/or embodied in firmware or dedicated hardware (e.g., implemented by an ASIC, a PLD, an FPLD, discrete logic, etc.). Also, one or more of the machine readable instructions represented by the flowchart of  FIGS. 5-9  may be implemented manually. Further, although the example machine readable instructions are described with reference to the flowcharts illustrated in  FIGS. 5-9 , many other techniques for implementing the example methods and apparatus described herein may alternatively be used. For example, with reference to the flowcharts illustrated in  FIGS. 5-9 , the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, combined and/or subdivided into multiple blocks. 
     As mentioned above, the example processes of  FIGS. 5-9  may be implemented using coded instructions (e.g., computer readable instructions) stored on a tangible computer readable medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a CD, a DVD, a cache, a random-access memory (RAM) and/or any other storage media in which information is stored for any duration (e.g., for extended time periods, permanently, brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable medium is expressly defined to include any type of computer readable storage and to exclude propagating signals. Additionally or alternatively, the example processes of  FIGS. 5-9  may be implemented using coded instructions (e.g., computer readable instructions) stored on a non-transitory computer readable medium, such as a flash memory, a ROM, a CD, a DVD, a cache, a random-access memory (RAM) and/or any other storage media in which information is stored for any duration (e.g., for extended time periods, permanently, brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable medium and to exclude propagating signals. Also, as used herein, the terms “computer readable” and “machine readable” are considered interchangeable unless indicated otherwise. 
     Example machine readable instructions  500  that may be executed to implement the data archiver  105  of  FIG. 1  are represented by the flowchart shown in  FIG. 5 . With reference to the data archiver  105  illustrated in  FIG. 1  and the data modeler  120  illustrated in  FIG. 4 , the machine readable instructions  500  of  FIG. 5  begin execution at block  505  at which the initializer  405  included in the data modeler  120  of the data archiver  105  obtains configuration information specifying the data sets (e.g., tables) and associated attributes (e.g., columns of the tables) for the data stored in the presentation database  110 . The initializer  405  then determines an initial data model in which there are fully interdependent (e.g., all-to-all) functional dependencies assumed to exist between all attributes of all data sets. Additionally, the initializer  405  can obtain configuration information to configure one or more confidence thresholds used to determine whether to prune candidate keys and/or functional dependencies from the initial data model to determine a verified data model. The configuration information may be obtained via one or more input commands entered by a user of the user terminal  135 , by accessing configuration data stored in the production database  110 , etc., or any combination thereof. 
     At block  510 A, the data modeler  120  obtains user input to exclude (e.g., mask out) attributes (e.g., table columns) from being considered as candidate keys prior to beginning model verification. An example procedure that may be used to implement the processing at block  510 A is illustrated in  FIG. 6  and described in greater detail below. 
     At block  515 , the data modeler  120  determines a set of candidate keys (e.g., excluding those attributes masked-out at block  510 ) for the functional dependencies included in the data model and performs candidate key verification to prune candidate keys from the data model. For example, the data modeler  120  uses one or more samples of the data stored in the production database  110  to verify whether each candidate key satisfies the criteria for being a key. An example procedure that may be used to implement the processing at block  515  is illustrated in  FIG. 7  and described in greater detail below. 
     At block  510 B, the data modeler  120  obtains user input to, for example, force pruning of one or more candidate keys still remaining after processing at block  515 . Additionally or alternatively, at block  510 B the data modeler  120  obtains user input to, for example, force inclusion of one or more candidate keys in the data model (e.g., to force the one or more candidate keys to be considered to be actual keys with maximum (e.g., 100%) confidence). Further discussion of the processing performed at block  510 B is provided below in connection with the description of  FIG. 6 . 
     At block  525 , the data modeler  120  determines a set of functional dependencies for each candidate key remaining in the set of candidate keys after processing at blocks  515  and  520 . At block  525  the data modeler  120  also performs functional dependency verification to prune functional dependencies from each remaining candidate key in the data model. For example, the data modeler  120  uses one or more samples of the data stored in the production database  110  to verify whether a particular dependent attribute set of a particular functional dependency appears (at least based on a data sample) to depend from the particular candidate key of the particular functional dependency. An example procedure that may be used to implement the processing at block  525  is illustrated in  FIG. 8  and described in greater detail below. 
     At block  510 C, the data modeler  120  obtains user input to, for example, force pruning of one or more functional dependencies still remaining after processing at block  525 . Additionally or alternatively, at block  510 C the data modeler  120  obtains user input to, for example, force inclusion of one or more functional dependencies in the data model (e.g., with maximum (e.g. 100%) confidence). 
     Additionally, at block  510 C the data modeler  120  obtains user input to, for example, determine whether data modeling is complete. For example, the data modeler  120  may present the current data model to a user (e.g., via the user interface  410  and the terminal  135 ) by presenting the current set of candidate keys, associated functional dependencies and associated confidence levels in a form, such as a tabular form listing candidate keys in a first column and associated functional dependencies in a second and subsequent columns, or graphically in a form similar to  FIG. 3B , etc. The data modeler  120  also prompts for an input from the user indicating whether another iteration of the data modeling procedure is to be performed. Additionally or alternatively, the data modeler  120  may present a summary of the confidence associated with the current model (e.g., by presenting the lowest or highest confidence level among all candidate keys and functional dependencies, by presenting an average or mean of the confidence levels, etc.) and prompts for an input from the user indicating whether another iteration of the data modeling procedure is to be performed. Alternatively, the decision whether to proceed with another processing iteration may be automatic based on the data modeler  120  evaluating the confidence levels of the candidate keys and functional dependencies and comparing them to one or more thresholds. Further discussion of the processing performed at block  510 C is provided below in connection with the description of  FIG. 6 . 
     Also, although the processing at each of blocks  510 A,  510 B and  510 AC is depicted as being performed in the context of (e.g., and synchronously with) the other data archiving processing illustrated by the flowchart of  FIG. 5 , in some examples the processing at any, some or all of blocks  510 A-C can be performed outside the context of (e.g., asynchronously relative to) the other data archiving processing. For example, in an asynchronous example, the processing at any, some or all of blocks  510 A-C can be performed to obtain user input at any time (asynchronously) relative to other data archiving processing. The obtained user input can then be buffered or otherwise stored for later use when appropriate in the data archiving process. Additionally or alternatively, at any, some or all of blocks  510 A-C, the data modeler  120  can allow the user to select one or more candidate keys and/or one or more functional dependencies for more extensive validation against larger data samples, or even all of the data stored in the production database  110 . 
     At block  535 , the data modeler  120  determines whether another data modeling processing iteration is to be performed. For example, the decision to perform another iteration may be based on a user input. For example, a user may examine the current model and determine that another processing iteration should be performed when the data model&#39;s confidence levels have not yet converged to steady-state levels and/or do not exceed some specified or preconfigured threshold or thresholds. Alternatively, the decision to perform another iteration may be performed automatically by the data modeler  120  based on, for example, evaluating whether the data model&#39;s confidence levels have converged to steady-state and/or exceed some specified or preconfigured threshold or thresholds. 
     If another data modeling processing iteration is to be performed (block  535 ), the processing returns to block  510 A and blocks subsequent thereto. However, if another data modeling processing iteration is not to be performed (block  535 ), then at block  540  the data modeler  120  performs data model pruning to determine a resulting verified data model. For example, at block  540  the data modeler  120  can determine the verified data model by pruning candidate keys (and associated functional dependencies) having confidence values lower than a threshold, and can also prune individual functional dependencies having confidence values lower than the same or a different threshold. The data modeler  120  then stores the verified data model to the data model storage  125 . An example procedure that may be used to implement the processing at block  540  is illustrated in  FIG. 9  and described in greater detail below. 
     After the verified data model is determined by the data modeler  120 , at block  545  the transaction archiver  130  included in the data archiver  105  uses the verified data model stored in the data model storage  125  to archive data (e.g., transactions) from the production database  110  to the archive  115  according to the verified data model, as described above. Execution of the example machine readable instructions  500  then ends. 
     Example machine readable instructions  510 A-C that may be executed to implement the processing at blocks  510 A,  510 B and  510 C of  FIG. 5  are represented by the flowchart shown in  FIG. 6 . With reference to the data archiver  105  illustrated in  FIG. 1  and the data modeler  120  illustrated in  FIG. 4 , the machine readable instructions  510 A-C of  FIG. 6  begin execution at block  605  at which the data modeler  120  determines whether an input command received via the user interface  410  is for data model initialization. If so, at block  610  the data modeler  120  processes the user input, which in the illustrated example is a user command indicated that one or more data attributes (e.g., columns) are to be excluded (e.g., masked out) from consideration as candidate keys in the verified data model. For example, at block  610  the data modeler  120  may cause an initial data model to be presented (e.g., in tabular form, graphical form, etc.), via the user interface  410 , to a user of the terminal  135 . Because the initial data model determined by the data modeler  120  assumes fully interdependent functional dependencies, in the initial data model each attribute (column) or set of attributes (columns) are assumed to be candidate keys. Through processing at block  610 , the user can exclude (mask-out) attributes that would not uniquely identify data transactions and, thus, do not qualify as keys. For example, in a customer sales database, the attribute ‘purchase quantity’ may not qualify as a key because different customers can purchase the same quantity of an item. Accordingly, at block  610  the user can exclude such an attribute from being a candidate key in the data model during initialization (e.g., to improve processing efficiency). 
     Assuming no input commands are received for candidate key verification or functional dependency verification, then at block  615  of the illustrated example the data modeler  120  processes another input from the user indicating whether to continue or terminate the data modeling processing currently being performed. If, for example, data model initialization has just completed, the user can issue a command to terminate data modeling, thereby causing the initial data model (after any candidate key exclusion) to become the verified data model. Execution of the example machine readable instructions  510 A-C then ends. As indicated in  FIG. 6 , the processing at blocks  605 ,  610  and  615  corresponds to the processing performed at block  510 A of  FIG. 5 . 
     At block  620 , the data modeler  120  determines whether an input command received via the user interface  410  is for candidate key verification. If so, then at block  625  the data modeler  120  obtains any user input commands to prune one or more candidate keys from the set of candidate keys. For example, the data modeler  120  can cause the parent attribute set forming each candidate key to be presented (e.g., in tabular form, graphical form, etc.), via the user interface  410 , to a user of the terminal  135 . The data modeler  120  can cause the confidence level associated with each candidate key to also be presented. The data modeler  120  can then prompt the user to select one or more candidate keys to be pruned (e.g., removed) from the set of candidate keys and, thus, from the data model. Pruning of a candidate key also causes any functional dependencies associated with the candidate key to be removed from the data model. 
     At block  630 , the data modeler  120  obtains any user input commands to force inclusion of one or more candidate keys in the data model. For example, after presenting the set of candidate keys and their respective confidence levels, the data modeler  120  can then prompt the user to select one or more candidate keys to be included in the data model without requiring any further verification. In some examples, forcing inclusion of a candidate key in the data model also causes its associated confidence level to be set to a specific (e.g., maximum) value, such as 100%. 
     Assuming no input commands are received for functional dependency verification, then at block  615  of the illustrated example the data modeler  120  processes another input from the user indicating whether to continue or terminate the data modeling processing currently being performed. If, for example, the current iteration of candidate key verification has just completed, the user can issue a command to terminate data modeling, thereby causing the current data model having the current set of candidate keys (and associated functional dependencies) to become the verified data model. Execution of the example machine readable instructions  510 A-C then ends. Example reasons for a user to terminate the data modeling process include, but are not limited to, the user being satisfied with the currently determined (e.g., currently verified) data model, an allotted time window having expired, the user plans to resume processing at a later time, etc. As indicated in  FIG. 6 , the processing at blocks  620 ,  625 ,  630  and  615  corresponds to the processing performed at block  510 B of  FIG. 5 . 
     At block  635 , the data modeler  120  determines whether an input command received via the user interface  410  is for functional dependency verification. If so, then at block  640  the data modeler  120  obtains any user input commands to prune one or more functional dependencies from the data model. For example, the data modeler  120  can cause the parent attribute set for each candidate key (or some selected subset) to be presented (e.g., in tabular form, graphical form, etc.) along with the dependent attribute set(s) functionally depending from the candidate key to a user of the terminal  135  via the user interface  410 . The data modeler  120  can cause the confidence level associated with each functional dependency to also be presented. The data modeler  120  can then prompt the user to select one or more functional dependencies to be pruned (e.g., removed) from the set of functional dependencies associated with a particular candidate and, thus, from the data model. In some examples, pruning of all functional dependencies associated with a particular candidate key also causes the candidate key itself to be removed from the data model. 
     At block  645 , the data modeler  120  obtains any user input commands to force inclusion of one or more functional dependencies in the data model. For example, after presenting a candidate key, its associated functional dependencies (e.g., the associated dependent attribute sets) and their respective confidence levels, the data modeler  120  can then prompt the user to select one or more functional dependencies to be included in the data model without requiring any further verification. In some examples, forcing inclusion of a functional dependency in the data model also causes its associated confidence level to be set to a specific (e.g., maximum) value, such as 100%. 
     Then, at block  615  of the illustrated example, the data modeler  120  processes another input from the user indicating whether to continue or terminate the data modeling processing currently being performed. If, for example, the current iteration of functional dependency verification has just completed, the user can issue a command to terminate data modeling, thereby causing the current data model having the current set of candidate keys and associated functional dependencies to become the verified data model. Execution of the example machine readable instructions  510 A-C then ends. As noted above, example reasons for user to terminate the data modeling process include, but are not limited to, the user being satisfied with the currently determined (e.g., currently verified) data model, an allotted time window having expired, the user plans to resume processing at a later time, etc. As indicated in  FIG. 6 , the processing at blocks  635 ,  640 ,  645  and  615  corresponds to the processing performed at block  510 B of  FIG. 5 . 
     At block  615 , the data modeler  120  can also process an input command from the user received via the user interface  410  indicating whether to continue or terminate data modeling processing altogether (e.g., without receiving any other input commands related to data model initialization, candidate key verification or functional dependency verification). For example, the data modeler  120  can be interrupted to cause the current data model, along with the confidence levels of the candidate keys and associated functional dependencies, to be presented (e.g., in tabular form, graphical form, etc.) to the user of the terminal  135  via the user interface  410 . The data modeler  120  can then prompt the user to indicate whether data modeling processing should continue (e.g., if one or more confidence levels have not converged or have not reached one or more particular thresholds) or can be terminated. Execution of the example machine readable instructions  510 A-C then ends. 
     Example machine readable instructions  515  that may be executed to implement the processing at block  515  of  FIG. 5  are represented by the flowchart shown in  FIG. 7 . With reference to the data archiver  105  illustrated in  FIG. 1  and the data modeler  120  illustrated in  FIG. 4 , the machine readable instructions  515  of  FIG. 7  begin execution at block  705  at which the candidate key selector  415  included in the data modeler  120  determines a set of non-trivial candidate keys for the data model. As noted above, a candidate key can correspond to a particular parent attribute or parent attribute set. In the case of a candidate key corresponding to a parent attribute set, the candidate key is non-trivial if the parent attribute set does not include a particular attribute or subset of attributes that is also a candidate key. For example, with reference to  FIG. 2 , at block  705  the candidate key selector  415  could include the attribute ‘C’ of column  225 , the attribute ‘C’ of column  255  and the attribute ‘Q’ of column  260  in the set of non-trivial candidate keys. In some examples, for a first iteration of the machine readable instructions  515  of  FIG. 7 , the set of candidate keys correspond to the set of candidate keys of the initial data model that assumes fully interdependent functional dependencies. Accordingly, the set of candidate keys for this first iteration include a key for each attribute (columns) in each data set (table), except for those that are expressly excluded by the user from the initial model. Then, for each subsequent iteration, the set of candidate keys includes those keys remaining after pruning, as well as parent attributes or parent attribute sets forced to be keys based on one or more user inputs. 
     Next, at block  710  the candidate key selector  415  assigns a first confidence level (e.g., such as 0% or some other minimum value) to each candidate key that was determined automatically or, in other words, was not specified by a user. Conversely, for each candidate key that is specified by a user via the terminal  135  and user interface  410 , at block  715  the candidate key selector  415  assigns a second confidence level (e.g., such as 100% or some other maximum value) to that candidate key. The first confidence level and/or the second confidence level can be specified as configuration parameters, hard-coded, etc. 
     Next, at block  720  the candidate key verifier  420  included in the data modeler  120  generates a sample of the data stored in the production database  110  for use in verifying the set of candidate keys. Modern databases can include millions of data entries. Accordingly, verifying each candidate key over the entire database can be computationally prohibitive. Instead, the candidate key verifier  420  generates a data sample containing a subset of the data stored in the production database. For example, the data sample can contain N groups of samples each containing M data entries, where N and M are configuration parameters specified by the user via the terminal  135  and user interface  410 . The N groups may be taken from the same data set (e.g., same database table) or taken from two up to N different data sets (e.g., different tables) included in the database. The same or different numbers M of data entries can be sampled from each data set. Additionally or alternatively, all the data entries included in one or more data set (e.g., tables) may be specified for inclusion in the data sample. In some examples, the candidate key verifier  420  maintains the association of a data sample to the particular data sets (e.g., tables) from which the data sample was taken. For example, with reference to  FIG. 2 , the candidate key verifier  420  could generate a data sample including N=2 groups, with M≦2 data entries taken from table  205  and M≦5 data entries taken from table  210 . 
     After generating the data sample, the candidate key verifier  420  verifies each candidate key against the data sample (blocks  725  and  730 ). For example, for a particular candidate key, the candidate key verifier  420  can determine whether the data sample indicates that data set (e.g., table) associated with the parent attribute set for the particular candidate key has unique entry values and, thus, may be specifying the unique, permissible values for this attribute in data transactions stored in the production database  110  (block  735 ). For example, with reference to  FIG. 2 , the attribute ‘C’ of column  225  has unique values across all data entries in its respective table  205  and, thus, could be a parent attribute specifying the permissible, unique values for this attribute, which can be used to uniquely identify different data transactions stored in the production database  110 . Accordingly, the attribute ‘C’ of column  225  could be a candidate key. Conversely, the attribute ‘C’ of column  255  and the attribute ‘Q’ of column  260  do not have unique values across all data entries in their respective table  210  and, thus, these attributes do not qualify as candidate keys. For processing efficiency, the candidate key verifier  420  can skip verification of candidate keys forced by the user for inclusion in the data model. Additionally or alternatively, the candidate key verifier  420  can skip verification of candidate keys whose associated confidence levels have reached a maximum value (e.g., such as 100%). 
     If a particular candidate key has unique values for its respective data entries included in the data sample (block  735 ), then at block  740  the candidate key verifier  420  increases the confidence level of the particular candidate key (e.g., by some specified incremental amount). In the illustrated example, the candidate key verifier  420  increments the confidence level at block  740  rather than just indicating that the particular candidate key has been verified because the data sample may contain only a subset of all data. As such, another data sample could indicate that the particular candidate key is actually not a key if, for example, that data sample included the same values for the particular candidate key in at least two data entries. 
     If, however, the particular candidate key does not have unique values for its respective data entries included in the data sample (block  735 ), then at block  745  the candidate key verifier  420  prunes the particular candidate key from the set of candidate keys. Then, after all candidate keys have been processed (block  750 ), the user input processing associated with block  510 B of  FIGS. 5-6  is performed. If an obtained user input indicates that another iteration of candidate key verification is to be performed (block  755 ) processing returns to block  720  at which another (e.g., different) data sample is generated for verifying the set of remaining candidate keys. For example, the user may determine that another iteration is warranted when the confidence levels for all candidate keys have not yet converged to steady-state values. However, if the user input indicates that another iteration of candidate key verification is not to be performed (block  755 ), execution of the example machine readable instructions  515  then ends. 
     Example machine readable instructions  525  that may be executed to implement the processing at block  525  of  FIG. 5  are represented by the flowchart shown in  FIG. 8 . With reference to the data archiver  105  illustrated in  FIG. 1  and the data modeler  120  illustrated in  FIG. 4 , the machine readable instructions  525  of  FIG. 8  begin execution at block  805  at which the functional dependency selector  425  included in the data modeler  120  determines a set of atomic functional dependencies for each candidate key in the set of candidate keys in the data model. As noted above, a functional dependency for a particular candidate key corresponds to a dependent attribute set that functional depends from a parent attribute set forming the candidate key. A dependent attribute set yields an atomic functional dependency if there is no complete combination of subsets of the dependent attribute set that are each by themselves functionally dependent on the particular candidate key. For example, with reference to  FIG. 2 , the attribute ‘C’ of column  225  is a candidate key, and attribute ‘C’ of column  255  functionally depends on this candidate key because, in this example, the attributes have the same name and attribute ‘C’ of column  255  takes on the values specified by attribute ‘C’ of column  225 . In contrast, attribute ‘Q’ of column  260  takes on values different from the values of the candidate key (e.g., attribute ‘C’ of column  225 ) and, thus, does not itself functionally depend on the candidate key. Accordingly, the dependent attribute set made up of attribute ‘C’ of column  255  and attribute ‘Q’ of column  260  would form an atomic functional dependency of the candidate key (e.g., attribute ‘C’ of column  225 ). 
     In some examples, for a first iteration of the machine readable instructions  525  of  FIG. 8 , the set of functional dependencies corresponds to the initial data model that assumes fully interdependent functional dependencies. Accordingly, for this first iteration, the set of functional dependencies for a particular candidate key include a functional dependency for each attribute (columns) in each data set (table), except for the attribute forming the candidate key itself and those that are excluded by the user from the initial model (e.g., by excluding the candidate key itself). Then, for each subsequent iteration, the set of functional dependencies for a particular candidate key includes those dependent attributes not pruned by the user, as well as dependent attributes or dependent attribute sets forced to be included as functional dependencies based on one or more user inputs. 
     Next, at block  810  the functional dependency selector  425  assigns a first confidence level (e.g., such as 0% or some other minimum value) to each functional dependency that was determined automatically or, in other words, was not specified by a user. Conversely, for each functional dependency that is specified by a user via the terminal  135  and user interface  410 , at block  815  the functional dependency selector  425  assigns a second confidence level (e.g., such as 100% or some other maximum value) to that functional dependency. The first confidence level and/or the second confidence level can be specified as configuration parameters, hard-coded, etc. 
     Next, at block  820  the functional dependency verifier  430  included in the data modeler  120  generates a sample of the data stored in the production database  110  for use in verifying the set of functional dependencies. For example, the procedure for generating data samples at block  820  may the same or similar to the procedure of generating samples at block  720  of  FIG. 7 . 
     After generating the data sample, the functional dependency verifier  430  verifies each functional dependency for each candidate key against the data sample (blocks  825  and  830 ). In the illustrated example, for a particular functional dependency of a particular candidate key, the functional dependency verifier  430  determines whether the data sample tends to confirm that the dependent attribute set does functionally depend from the parent attribute set for the particular candidate key (block  835 ). For example, the data sample tends to confirm that the dependent attribute set does functionally depend from the parent attribute set for the particular candidate key when the values of a dependent attribute that supposedly depends from a parent attribute does not take on values that are different from the parent attribute. For example, with reference to  FIG. 2 , the attribute ‘C’ of column  255  takes on the same values as the attribute ‘C’ of column  225  and, thus, a data sample drawn from data entries of table  205  and table  210  would tend to confirm that attribute ‘C’ of column  255  functionally depends on attribute ‘C’ of column  225 . Conversely, the attribute ‘Q’ of column  260  takes on different values than the attribute ‘C’ of column  225  and, thus, a data sample drawn from data entries of table  205  and table  210  would tend to confirm that attribute ‘Q’ of column  260  does not functionally depend on attribute ‘C’ of column  225 . For processing efficiency, the functional dependency verifier  430  can skip verification of functional dependencies forced by the user for inclusion in the data model. Additionally or alternatively, the functional dependency verifier  430  can skip verification of functional dependencies whose associated confidence levels have reached a maximum value (e.g., such as 100%). 
     If a particular functional dependency is confirmed by the data sample (block  735 ), then at block  840  the functional dependency verifier  430  increases the confidence level of the particular functional dependency (e.g., by some specified incremental amount). In the illustrated example, the functional dependency verifier  430  increments the confidence level at block  840  rather than just indicating that the particular functional dependency has been verified because the data sample may contain only a subset of all data. As such, another data sample could indicate that the particular functional dependency is actually not a dependency of the associated candidate key if, for example, that data sample included different values for associated dependent and parent attributes making up the functional dependency. 
     If, however, the particular functional dependency is not confirmed by the data sample (block  835 ), then at block  845  the functional dependency verifier  430  decreases the confidence level of the particular functional dependency (e.g., by some specified decremental amount). In the illustrated example, the functional dependency verifier  430  decrements the confidence level at block  845  rather than just pruning the particular functional dependency because the data sample may contain only a subset of all data. As such, another data sample could indicate that the particular functional dependency is a dependency of the associated candidate key if, for example, that data sample included the same values for associated dependent and parent attributes making up the functional dependency. 
     Then, after all functional dependencies of all candidate keys have been processed (block  850 ), the user input processing associated with block  510 C of  FIGS. 5-6  is performed. If an obtained user input indicates that another iteration of functional dependency verification is to be performed (block  855 ) processing returns to block  820  at which another (e.g., different) data sample is generated for verifying the set of remaining functional dependencies for each remaining candidate key. For example, the user may determine that another iteration is warranted when the confidence levels for all functional dependencies have not yet converged to steady-state values. However, if the user input indicates that another iteration of functional dependency verification is not to be performed (block  855 ), execution of the example machine readable instructions  525  then ends. 
     Example machine readable instructions  540  that may be executed to implement the processing at block  540  of  FIG. 5  are represented by the flowchart shown in  FIG. 9 . With reference to the data archiver  105  illustrated in  FIG. 1  and the data modeler  120  illustrated in  FIG. 4 , the machine readable instructions  540  of  FIG. 9  begin execution at block  905  at which, for each candidate key remaining in the set of candidate keys (e.g., such as hose candidate keys that have not been pruned automatically or forced to be pruned by the user), the data model processor  435  included in the data modeler  120  determines whether to prune the particular candidate key from the data model. In particular, at block  910  the data model processor  435  determines whether the confidence level for a particular remaining candidate key is below a first threshold (which may be specified via configuration information, hard-coded, etc.). If the confidence level is below the first threshold (block  910 ), then at block  915  the data model processor  435  prunes the particular candidate key and its associated functional dependencies from the data model. 
     For example, with reference to  FIG. 2 , assume that attribute ‘Q’ of column  260  is a remaining candidate key with two functional dependencies, one corresponding to attribute ‘C’ of column  255  being a dependent attribute and the other corresponding to attribute ‘C’ of column  225  being another dependent attribute. If, after the processing of  FIGS. 5-8 , the confidence level associated with the attribute ‘Q’ of column  260  being a candidate key is below the first threshold, then the candidate key corresponding to attribute ‘Q’ of column  260  would be pruned at block  915 , as well as its associated functional dependencies corresponding to attribute ‘C’ of column  255  being a dependent attribute and attribute ‘C’ of column  225  being another dependent attribute. 
     At block  920 , the data model processor  435  continues processing all remaining candidate keys. Of course, for processing efficiency, the processing at blocks  905 - 920  can be skipped for any candidate keys forced by the user to be included in the data model. 
     Continuing to block  925 , for each functional dependency remaining for each remaining candidate key (e.g., such as those functional dependencies that have not been forced to be pruned by the user), the data model processor  435  included in the data modeler  120  determines whether to prune the particular functional dependency from the data model. In particular, at block  930  the data model processor  435  determines whether the confidence level for a particular remaining functional dependency for a particular remaining candidate key is below a second threshold (which may be specified via configuration information, hard-coded, etc., and which may be the same as, or different from, the first threshold used at block  910 ). If the confidence level is below the second threshold (block  930 ), then at block  935  the data model processor  435  prunes the particular functional dependency from the data model. 
     For example, with reference to  FIG. 2 , assume that attribute ‘C’ of column  225  is a remaining candidate key with two functional dependencies, one corresponding to attribute ‘C’ of column  255  being a dependent attribute and the other corresponding to attribute ‘Q’ of column  260  being another dependent attribute. If, after the processing of  FIGS. 5-8 , the confidence level associated with the attribute ‘Q’ of column  260  being a functional dependency of the candidate key (e.g., attribute ‘C’ of column  225 ) is below the second threshold, then the functional dependency corresponding to attribute ‘Q’ of column  260  would be pruned at block  935 , leaving to attribute ‘C’ of column  255  as a remaining functional dependency for the candidate key (e.g., attribute ‘C’ of column  225 ). 
     At block  940 , the data model processor  435  continues processing all remaining functional dependencies for all remaining candidate keys. Of course, for processing efficiency, the processing at blocks  925 - 940  can be skipped for any functional dependencies forced by the user to be included in the data model. 
     Next, at block  945  the data model processor  435  stores the remaining candidate keys and associated functional dependencies in the data model storage  125  as the verified data model for use by the transaction archiver  130  for data archiving. Execution of the example machine readable instructions  540  then ends. 
       FIG. 10  is a block diagram of an example processing system  1000  capable of implementing the apparatus and methods disclosed herein. The processing system  1000  can be, for example, a server, a personal computer, a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a personal video recorder, a set top box, or any other type of computing device. 
     The system  1000  of the instant example includes a processor  1012  such as a general purpose programmable processor. The processor  1012  includes a local memory  1014 , and executes coded instructions  1016  present in the local memory  1014  and/or in another memory device. The processor  1012  may execute, among other things, the machine readable instructions represented in  FIGS. 5-9  The processor  1012  may be any type of processing unit, such as one or more Intel® microprocessors from the Pentium® family, the Itanium® family and/or the XScale® family, one or more microcontrollers from the ARM® and/or PICO families of microcontrollers, etc. Of course, other processors from other families are also appropriate. 
     The processor  1012  is in communication with a main memory including a volatile memory  1018  and a non-volatile memory  1020  via a bus  1022 . The volatile memory  1018  may be implemented by Static Random Access Memory (SRAM), Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  1020  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1018 ,  1020  is typically controlled by a memory controller (not shown). 
     The processing system  1000  also includes an interface circuit  1024 . The interface circuit  1024  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a third generation input/output (3GIO) interface. 
     One or more input devices  1026  are connected to the interface circuit  1024 . The input device(s)  1026  permit a user to enter data and commands into the processor  1012 . The input device(s) can be implemented by, for example, a keyboard, a mouse, a touchscreen, a track-pad, a trackball, an isopoint and/or a voice recognition system. 
     One or more output devices  1028  are also connected to the interface circuit  1024 . The output devices  1028  can be implemented, for example, by display devices (e.g., a liquid crystal display, a cathode ray tube display (CRT)), by a printer and/or by speakers. The interface circuit  1024 , thus, typically includes a graphics driver card. 
     The interface circuit  1024  also includes a communication device such as a modem or network interface card to facilitate exchange of data with external computers via a network (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processing system  1000  also includes one or more mass storage devices  1030  for storing software and data. Examples of such mass storage devices  1030  include floppy disk drives, hard drive disks, compact disk drives and digital versatile disk (DVD) drives. The mass storage device  1030  may implement the production database  110  and/or the archive  115 . Alternatively, the volatile memory  1018  may implement the production database  110  and/or the archive  115 . 
     As an alternative to implementing the methods and/or apparatus described herein in a system such as the processing system of  FIG. 10 , the methods and or apparatus described herein may be embedded in a structure such as a processor and/or an ASIC (application specific integrated circuit). 
     Finally, although certain example methods, apparatus and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the appended claims either literally or under the doctrine of equivalents.