Abstract:
A database modernization system and method is disclosed. One example embodiment comprises a method for receiving a record stored as a source physical data model, parsing the source physical data model into a source logical data model, wherein the source logical data model includes semantic information from the record, transforming the source logical data model to a destination logical data model independent of a source or a destination record physical implementation, wherein the destination logical data model includes at least a portion of the semantic information from the record, and storing the destination logical data model in a destination physical data model. In this manner, a destination physical data model may be stored in a relational database management system on a per record basis.

Description:
TECHNICAL FIELD 
     The present invention relates generally to business systems and software, and more particularly to legacy data system modernization. 
     BACKGROUND 
     One of the greatest infrastructure challenges in organizations today is the reliance on database systems created and maintained over a period of time much longer than their anticipated lifespan. Many of these systems were created with numerous limitations and restrictions due to technological restraints of the time period. Over time, technology has rapidly improved and many of these systems have become outdated and inefficient. As a result, many organizations are looking for a viable approach to modernize their legacy database systems. 
     Past attempts at legacy database modernization have generally included direct software updates and/or conversions. One approach to legacy database modernization involves creating a new data store and uploading an entire legacy database into the new store in a single modernization attempt. One problem with this approach is that undetected flaws in the modernization software may result in unacceptable amounts of lost and/or destroyed data. 
     Another approach to legacy database modernization involves performing a record by record conversion of legacy source data into a new data store format. Although the occurrence of lost and/or destroyed data may be reduced, this approach may be both time-consuming and cost-prohibitive. 
     SUMMARY 
     According to one aspect of the invention, a database modernization system is provided that may include a data migration workbench transformer (DMWT) that transforms data records in a legacy source database and migrates them to a destination database. The DMWT may be configured with a rule set that describes the formats of both the source and destination database and ensures that records transformed/migrated to a destination database are of a specified format and content. 
     According to one embodiment, a database modernization method may include receiving a record stored as a source physical data model, parsing the source physical data model into a source logical data model, wherein the source logical data model includes semantic information from the record, transforming the source logical data model to a destination logical data model independent of a source or a destination record physical implementation, wherein the destination logical data model includes at least a portion of the semantic information from the record, and storing the destination logical data model in a destination physical data model. In this manner, a destination physical data model may be stored in a relational database management system on a per record basis. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The disclosure is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which the like references indicate similar elements and in which: 
         FIG. 1  is a schematic diagram of an exemplary embodiment of a data modernization system. 
         FIG. 2  is a schematic diagram of the data modernization system of  FIG. 1  that depicts present case analysis and future case analysis repositories. 
         FIG. 3  is a schematic diagram of an example data migration/transformation through the data modernization system of  FIG. 1 . 
         FIG. 4  shows a flow chart depicting an example data referential integrity validation/correction/reporting process for the modernization system of  FIG. 1 . 
         FIG. 5  shows a flow chart depicting the processing of records through the modernization system of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     One exemplary embodiment of a data modernization system is schematically illustrated in  FIG. 1 . Data Modernization system  100  may include a legacy data source file  106  that encompasses a source physical data model  108  format. As a non-limiting example, legacy data source file  106  may be configured as a flat file export of a proprietary binary format. Source physical data model  108  may be configured as a lower-level representation of the physical data layout of legacy data source file  106 , for example a physical data model  108  may be defined as a character with a length of 1 or as a string of length 20. 
     Data modernization system  100  may also include conversion engine  102 . Conversion engine  102  may include loader  110 , data migration workbench transformer (DMWT)  104 , GUI  130  storer  112 , and destination physical data model  132 . Loader  110  may interface with legacy data source file  106  to parse the legacy data into an internal representation such as a document object model (DOM). In some embodiments, the legacy data may be a record that is parsed into an internal representation that conforms to a source logical data model  122  format. A logical data model provides semantic information more readily understood by a human user and therefore is not as implementation specific as the physical data model. Additionally, loader  110  may also validate source data  106  against source physical data model  108 . Values that are not in conformance with the source physical data model may be deemed violations by DMWT  104  and subsequently logged as violations by audit trail  114 . For example, dates might be stored in the source data as eight digit text strings such as YYYYMMDD. Thus, a non-eight digit string or a string that included a non-number symbol may be logged as one or more violations by audit trail  114 . Audit trail  114  may be configured to log sets of distinct audit units created during a data modernization run. Each audit unit may contain a unique identifier that may be used to identify the source data record related to each audit unit. Storer  112  may be configured to translate data records received from DMWT  104  conforming to destination logical data model  126  and then pass them on to RDBMS  128 . Like source physical data model  108 , destination physical data model  132  may be configured as a low-level representation of the physical data layout of the target database (RDBMS  128 ). 
     Data modernization system  100  may also include logical data model interface  120 , which may be configured as a module that represents the logical, abstracted referencing of the names used in legacy data source file  106  and a relational database management system  128 . Logical data model interface  120  may in turn encompass source logical data model  122 , destination logical data model  126 , and data migration transform language (DMWTL)  124 . DMWT  104  may access logical data model interface  120  to create a destination logical data model via DMWTL  124  that may be based in part on source logical data model  122 . DMWTL  124  may utilize reference names included within source physical data model  108  and destination logical data model  126 . Furthermore, DMWT  104  may read both the source and destination logical models and use them to validate legacy source data file  106 . For example, entity and field names within data source  106  may be validated against both the source and destination logical models to ensure the accuracy of destination logical data model  126 . 
     Data modernization system  100  may further include data model transform language (DMTL) editor  118  and data workbench migration console (DMWC)  116 . DMWTL editor  118  may be a program accessed by a user via GUI  130  and logical data model interface  120  to update and modify a rule set (as described in further detail with regard to  FIG. 2 ) embodied by DMWT  104  so as to improve the accuracy of the transformation/migration process of DMWT  104 . DWMC  116  may provide a user real-time data via DMWT  104  and GUI  130  to assess the efficiency and performance of the data conversion process through data conversion engine  102  (e.g. number of records modernized per second, total violations, memory used, etc.). 
     Each data record that is successfully uploaded by loader  110  from legacy source data file  106  may then be processed by DMWT  104 . A rule set (as described in further detail with regard to  FIG. 2 ) within DMWT  104  may be applied to each data element (datum) to ensure that each data datum passed to storer  112  is of the data object format defined by source physical data model  108  and also concurs with destination physical data model  132  and destination logical data model  126 . Once encapsulated as an instantiated data object by storer  112 , data objects may then be stored in various relational database management systems  128 , such as an Oracle® database management system, as one example. 
       FIG. 2  is a schematic diagram of the data modernization system of  FIG. 1  that depicts present case analysis and future case analysis repositories. Another representation of a data migration flow is depicted in  FIG. 2 . In this example, legacy source data may flow from present case analysis repository (PCAR)  202  through conversion engine  102  to RDBMS  128  via loader  110 , DMWT  104 , and future case analysis repository (FCAR)  204 . PCAR  202  may include legacy source data file  106  which may be further defined by source physical data model  102 . PCAR  202  may also include source logical data model  122  and source conceptual domain model  206  which may be mapped by a system programmer. 
     FCAR  204  may include destination conceptual domain model  216 , destination logical data model  126 , and storer  130  which may be further defined by destination physical data model  130 . Destination conceptual domain model  218  and destination physical data model  132  may be mapped and defined by a system programmer. A relational database management system may access storer  112  within FCAR  204  to download data objects that have been stored within storer  112 . 
     Each data element (datum) that is successfully uploaded by loader  110  from legacy source data file  106  may then be processed by DMWT  104 . DMWT  104  may include rule set  208 . Rule set  208  may be applied to a legacy data object to transform the legacy data object into a modern data object. A legacy data object may be a field, a data record, an arbitrarily defined set of data records, an entire database, or other individually transformable data object. Rule set  208  may include record-level transformation rule(s)  210 , field level rule(s)  212 , source expression(s)  214 , and caster(s)  216  which may all be applied to the data records that are processed by DMWT  104 . For example, record-level transformation rules may be rules that are applied to an entire data record and may include a rule that commands DMWT  104  to run all or a subset of the mapping rules in the transformer on the legacy source data. An example of a field-level rule may be a mapping rule  214  that copies a source logical field name within source logical data model  122  into a destination logical field name within destination logical model  126 . A mapping rule may further include a source expression  214 . A source expression may be defined as a further refinement of a mapping rule. For example, in a license plate data record, the presence of certain letters may indicate specific automobile registration information such as a commercially-owned or government-owned vehicle. In this example, a “G” might be used at the end of a license plate number data entity to indicate that the vehicle is a government owned vehicle. A source expression  214  may determine the presence of the letter “G” at the end of a license plate number data datum and produce a Boolean value based on the presence of the letter (or lack thereof) that may be stored in storer  112 . 
     Another example of DMWT  104  utilizing mapping rules  210 , is the creation of surrogate keys within storer  112 , each of which indirectly references a natural key of a single data datum in legacy source data file  106 . Furthermore, a mapping rule may create a foreign key that is a reference between two related data objects within storer  112 . Another example of a DMWT mapping rule may be a rule that establishes a unique key for the natural key of each data datum in legacy source data file  106 . A unique key may require that each natural key of a data datum be a singular key unto itself. In other words, the creation of a unique key ensures that duplicate legacy source data file datums will not be passed on to storer  112  and duplicates may be recorded in audit trail  114  as violations. 
     Additionally, rule set  208  may include a caster(s), which may be a script or piece of compiled code that may validate and transform a single typed datum to an output field. For example, a default caster may simply validate that the datum can represent a number (e.g. a caster of this type would be utilized when transforming a number in legacy data source file  106  to a number in storer  112 ). A more complex caster may do project-specific work such as extract the “year” component from a complex binary field that was used to store sequence numbers for assigning numbers to, for example, birth and death certificates. 
     After the transforming and migration of a pre-determined number of source data records through DMWT  104  (by applying rule set  208  to each data record) is complete, a number of destination records may have been formed and passed on to storer  112 . Consequently, RDBMS  128  may then be populated with data object records. Unique keys and surrogate keys are valid for all data object records at this point; however, some foreign keys generated by DMWT  104  from natural key relationships in legacy source data file  106  may be invalid. DMWT  104  may then perform a referential integrity validation between legacy source data file  106  and the target database stored within RDBMS  128  (as described in further detail with regard to  FIG. 4 ). 
     Turning now to  FIG. 3 , which illustrates a schematic diagram of an example data migration through the data modernization system of  FIG. 1 , a data datum  302  that represents a six-digit value is stored within legacy data source file  106  along with the physical data model  108  that represents the specific data datum as a date to DMWT  104 . After the data datum is uploaded and processed by loader  110  and subsequently transformed by DMWT  104  within conversion engine  102 , a data object  304  may be instantiated with the transformed data datum. The instantiated data object  304 , stored by storer  112 , may now be accessed by a relational database management system (RDBMS)  128  and instantiated with other data, such as additional data datums contained within the original data record that was transformed and migrated, if desired. 
       FIG. 4  shows a flow chart depicting a data referential integrity validation/correction/reporting process for the modernization system of  FIG. 1 . After the transforming and migration of a pre-determined number of source data records through DMWT  104  (by applying rule set  208  to each data record) is complete, a number of destination data object records may have been formed and passed on to storer  112 . At this point, RDBMS  128  is populated with destination data object records. Primary and unique keys are valid for all source data object records at this point, however, some foreign key field values generated by DMWT  104  from natural key relationships in legacy source data file  106  may be invalid. 
     At  402 , transforming violation logs may be identified by DMWT  104 . An example of a violation log type may include a null field violation log where a source field in legacy data source file  106  is empty and a constraint in DMWT  104  and/or destination logical data model  126  is violated. At  404 , this type of violation log will be entered into audit trail  114 . 
     Another example of a violation log type may include a typecast error violation log where a field in legacy data source file  106  has failed to satisfy the logical constraints of a caster  216  within DMWT  104  that was utilized in a failed attempt to transform the corresponding data datum. At  404 , this type of violation log will be entered into audit trail  114 . Another example of a violation log type may include a parse error where loader  110  has failed to parse a particular data datum from legacy data source file  106 . At  404 , this type of violation log may be entered into audit trail  114 . 
     Another example of a violation log type may include a duplicate natural key where a natural key used to generate primary surrogate keys occurs more than once in the legacy data source file. When DMWT  104  first encounters a natural key it will successfully generate a surrogate key. Subsequent occurrences of a specific natural key may generate a violation log of this type that may be entered into audit trail  114 . 
     Another example of a violation log type may include a foreign key violation where surrogate keys were generated by DMWT  104  from natural key relationships in the legacy source data file  106 . In other words, a foreign key may have been generated that does not point to an existing data datum in the legacy source data file. This is often referred to as a “dangling” foreign key. At  404 , DMWT  104  may run queries on target RDBMS  128  to find dangling foreign key relationships. Additionally, at  404 , DMWT  104  may produce dangling foreign key violation log entries and enter them into audit trail  114 . The foreign key violation log entries may include the unique key of the specific data datum, the present case analysis repository natural key value of the specific data datum, and the surrogate key of the destination data object record, for example. 
     Since modern database systems may require referential integrity constraints to be present and active, referential integrity violations such as dangling foreign keys may need to be corrected by DMWT  104  at  406 . By requiring loader  110  to only load complete records to DMWT  104  and requiring DMWT  104  to remove or rewrite source data datums related to dangling foreign keys in legacy data source file  106 , conversion engine  102  ensures overall record atomicity between a legacy database and a destination database. In other words, the DMWT only transforms/migrates complete source data datums to a destination database whereas incomplete or defective source data datums are not transformed/migrated to the destination database. Thus, only complete source data records that agree with both its corresponding source and destination logical data models are allowed to be migrated/transformed to the destination database. Furthermore, any given source data datum has either been transformed/migrated to storer  112  with a corresponding full set of log entries reported to audit trail  114  that indicate which rules within DMWT  104  were ran by DMWT  104  or there is a full set of log entries reported to audit trail  114  that indicate why specific data records were rejected for transformation/migration. 
     At  408 , summary data for auditing may be calculated. For example, queries against audit trail  114  may be executed and the results may be stored in a metadata table for retrieval by a DMWT user. At  410 , remaining constraints such as primary key, foreign key, and unique key constraints are enabled by DMWT  104 . At this point, however, the referential integrity validation, logging, and correction step has already been run and thus unique natural keys have been verified by the unique constraint(s) enforced by DMWT  104 . Therefore, any constraint validation errors at this point typically are fatal and are reported to audit trail  114  as such. 
     At  412 , several runtime statistics may be reported to audit trail  114  and data workbench migration console  116 . Some of these runtime statistics may include, key index performance (cache hits, disk reads, disk writes, for example), loader efficiency (blocked time, for example), log messages (info, warning, error, for example), referential integrity results (dangling foreign keys, source data records removed, foreign keys replaced by null, for example). These statistics may be used by a DMWT user to diagnose problems associated with and assess the efficiency and quality of a data migration/transformation run. 
     By using the statistics to evaluate the overall performance of DMWT  104 , a DMWT user may continuously integrate improvements into conversion engine  102 . For example, modifications may be made to rule set  208  and/or the destination logical data model  126  of future case analysis repository  204  to improve the efficiency or accuracy of the migration and transformation of data from PCAR  202  to FCAR  204 . More specifically, a mapping rule  220 , caster  216 , or source expression  214  may be rewritten to be more constrained or more lenient so as to produce a desired migration/transformation outcome. 
     This rule-by-rule cleansing allows DMWT  104  to be continuously and incrementally improved in an iterative manner that allows for small portions of legacy source data to be processed by the DMWT prior to a migration/transformation of the entire legacy source data file  106 . This results in a user of conversion engine  102  to have the ability to have demonstrated successful data migration/transformation prior to subjecting an entire legacy data source to a modernization process that might result in the loss or destruction of source data. 
     The portion of legacy source data that was processed during the previous data transformation/migration run performed by DMWT  104  (and produced unique runtime statistics which formed the basis for the aforementioned modifications/improvements to conversion engine  102 ) may then be re-processed by DMWT  104 . An iterative conversion engine improvement process may thus be instituted by a user of DMWT  104  that results in an iterative cycle of data transformation/conversion engine improvement until the performance of the data migration/transformation process is deemed to be at a satisfactory level by a DMWT user. A full data migration/transformation of the entire legacy source data file may then be executed. 
       FIG. 5  shows a flow chart depicting the processing of records through the modernization system of  FIG. 1 . After logical records are processed by DMWT  104  and stored by storer  112 , they may be generically processed at  502 . This generic processing may include preparing physical records that will be sent on to a generic relational database management system. Certain processing actions may be performed at  502  that prepare the physical records for any number of relational database management systems. In other words, the processing done at  502  is not for a specific RDBMS, but instead includes processing steps that may prepare physical records for specific processing at  504 . At  504 , record processing steps specific to an individual RDBMS, such as Oracle®, may be performed. The physical records may then be accessed by multiple relational database management systems  128  or another type of storage implementation such as comma separated text files. This may be accomplished, for example, by multiplexing these various storage implementations. 
     It should be understood that the described steps may graphically represent code to be programmed into a non-transitory computer readable storage medium. It should be further understood that the embodiments herein are illustrative and not restrictive, since the scope of the invention is defined by the appended claims rather than by the description preceding them, and all changes that fall within metes and bounds of the claims, or equivalence of such metes and bounds thereof are therefore intended to be embraced by the claims.