Patent Publication Number: US-10769169-B2

Title: System for automatically creating and associating data conversion programs with source and target data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation to U.S. patent application Ser. No. 14/846,910, titled “SYSTEM FOR AUTOMATICALLY TRACKING DATA THROUGH A PLURALITY OF DATA SOURCES AND PLURALITY OF CONVERSIONS” filed on Sep. 7, 2015, which claims the benefit of, and priority to U.S. Provisional Patent Application No. 62/152,865, titled “SYSTEM FOR AUTOMATICALLY TRACKING DATA THROUGH A PLURALITY OF DATA SOURCES AND PLURALITY OF CONVERSIONS AT ROW AND ELEMENT LEVEL” filed on Apr. 25, 2015, the entire specifications of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Art 
     This patent specification relates to the field of systems configured to track data through a plurality of data sources. More specifically, this patent specification relates to systems for tracking data through a plurality of data sources and plurality of conversions such as at the row and element level. 
     Discussion of the State of the Art 
     Computing systems are used to store and retrieve data. Due to the proliferation of data sources and the connected nature of computing systems, tracking where data originates and how it was processed has become extremely difficult. The data generated by computing systems is typically stored in a database in a format defined by the processing application. For example, a computing system used to process health records will store the data in a format that allows a health practitioner to retrieve and process patient data. There are a multitude of computing systems that are designed to process health records but each computing system may not store the health records in the same format. When data is collected from each source system, it is often critical to the collector that the data abide by format and business rules. For example, pharmaceutical data must comply with standards before it can be submitted to the FDA. Determining compliance not only requires verifying format but knowledge of how data was processed as well as data sources. Recording the data sources and processing steps for data as it moves through the data supply chain is called data tracking. 
     Because multiple systems may be involved in producing, processing and sharing data, data tracking has become a necessity. Sharing data requires that the storage and exchange format be standardized. Standards exist to expedite analysis or allow sharing. Standards also exist to protect privacy and ensure safety. Personal or financial data must be tracked to comply with privacy laws. Financial data must be tracked to comply with accounting rules and regulations. 
     Although standards are necessary for sharing data, they can create challenges for data tracking. Computing systems that need to share data are not necessarily produced by the same vendor. If multiple entities are involved in data sharing, each entity can create their own standard. Even when multiple entities agree on a standard, multiple revisions are necessary as the standard is refined. Furthermore, in computing systems designed for scientific research, the discovery nature of science necessitates creation of new domains to be added to the standard. Thus creating standardized data if often a multistep process with multiple versions of data at each step. As the number of steps increases, data tracking becomes increasingly difficult. 
     In order to track data, many approaches and tools have been utilized. Datasets can be manually converted and transferred to comply with standards and regulations. Data describing the source of data, and the type of processing and data standards utilized is called metadata. Metadata is critical to data tracking, but in most current systems, it is manually recorded. When metadata is manually entered, either before or after the data is transferred and converted, it can get out of sync with the datasets. In other words, the metadata may not actually reflect what was performed on the data. Accurate data tracking requires knowledge of what metadata corresponds to a particular dataset. 
     Extraction Transformation and Loading (ETL) programs are used to convert data. ETL programs are either manually written using a programming language or created using an ETL creation tool. An ETL creation tool is good for automatically creating ETL programs that conform to common conversion patterns. The ETL tool user selects pre-built conversion building blocks and manually fills in specific parameters. For example, a user may select a building block that writes data into a database. The user manually fills in the data source connection parameters and how data will be mapped from a source to target dataset. Although the ETL program is then created automatically, it is still up to the ETL programmer to manually record what ETL program was used on every resultant dataset. Datasets may go through a series of cascading conversions and validations. Each step of the series requires a different ETL program. For data tracking purposes, the ETL program must be recorded and related to the dataset. This is especially important if a dataset fails validation at a final step. In order to determine which conversion step introduced invalid data, a mechanism to retrieve the ETL program and the resultant datasets is necessary. For example, if a dataset has invalid data, the previous dataset and the latest conversion must be examined. This is problematic because each conversion step may have multiple ETL versions. The multiple versions may be due to data irregularities or variations of business rules implemented. In current practice, the ETL program associated to each step is manually recorded. Due to the complexity of manual recording processes, datasets can get out of sync with the ETL program. 
     Currently systems exist that allow a user to manually capture metadata describing data conversion activities. These systems are often referred to as metadata management or semantic management systems. These systems allow users to manually enter data describing how data sets are going to be converted or how they data sets were converted. When using these systems, it is up to the user to ensure that the conversion programs convert data according to the metadata that was entered. For instance, the metadata may indicate that a data element in a source data set be extracted, undergo a format conversion and then be loaded into a data element in a target dataset. It is up to the ETL programmer to create programs that insure the data element is extracted, converted and loaded according to the metadata entered into the metadata management system. 
     Systems exist to manage changes to computer programs. These systems are referred to as software revision control or software configuration management systems. These systems manage changes to programs by applying a new version number to a program if it is changed. Revision control systems can be used to track revisions of conversion programs. The problem is these systems are designed to track revisions in computer programs not computer data. These systems were not designed to associate a conversion program and its resultant data. 
     Methodologies exists that associate metadata to a dataset. Using a system that tracks data workflow, a workflow step could be created that automatically logs metadata information associated with a data set conversion. This methodology can&#39;t record metadata at a row or element level. For example, this methodology can store information regarding the processing of an entire dataset, but it can&#39;t record information regarding the processing of an individual data element such as a patient&#39;s blood pressure. Therefore, a system does not exist that automatically applies revision management to data conversion metadata and associated data down to the row and element level. 
     SUMMARY OF THE INVENTION 
     The system described herein documents a novel approach to tracking data through a plurality of data sources and data conversions using automated data conversion. Embodiments include a computing system that tracks data through a plurality of conversions by automatically creating conversion programs based on conversion metadata. The conversion metadata may be automatically revision managed by applying version numbers to each new revision of metadata. Embodiments of this system can automatically attach version information to the programs created based on the conversion metadata. The programs that convert data may attach the version information to the resultant data. Automatic versioning of processing metadata, conversion processes and resultant data keeps processing metadata tied to the resultant data through matching version IDs. The versioned metadata may be kept in a database. The system allows access to this database, so that a variety of data tracking operations are possible. 
     The platform includes a database capable of storing conversion metadata, multiple datasets and a central management module that provides access to conversion metadata as well as the location of the resultant datasets. Source dataset(s), conversion metadata and the resultant dataset will be referred to as a data build. The integrity of a data build is due to the fact that the metadata creates the conversion program which, when executed, converts the source data into the target data. Data builds are the building blocks used to create multistep data conversion flows. The system comprises a data build manager to store and access data build metadata as well as execution information. The metadata stored in the data build manager is used to categorize, group, sequence, and execute data builds. A sequence of data builds will be referred to as a data flow. Resultant datasets from each data build are archived and may be outputted. 
     The system supports operations on the target dataset of each data build. Operations on the target datasets may be guided by the data build&#39;s conversion metadata presented to the user via a Graphical User Interface (GUI). An embodiment of the system would allow comparing the target datasets from a group of data builds that share a common source dataset. An embodiment would allow merging datasets from data builds whose sources and conversion processing may be different but whose resultant datasets are in compliance with the same regulatory requirements. An embodiment would allow direct SQL access to a data build&#39;s datasets as well as mechanisms to export the datasets. 
     In some embodiments, a method of data tracking a source dataset comprising a plurality of data elements in a computer system which has a memory and a processor may comprise the steps of: storing data conversion instructions for one or more data elements as conversion metadata associated with the source dataset in the memory; creating a unique version number with the processor based on the conversion metadata; creating a conversion program from the conversion instructions of the conversion metadata; running the conversion program to perform a data conversion with the processor on one or more data elements of the source dataset to form a target dataset comprising one or more converted data elements; and associating and storing the unique version number with the conversion metadata, with the conversion program, and with the target dataset with the processor in the memory. 
     In further embodiments, a method of data tracking a source dataset comprising a plurality of data elements in a computer system which has a memory and a processor may comprise the steps of: storing data conversion instructions for one or more data elements as conversion metadata associated with the source dataset in the memory; creating a unique version number with the processor based on the conversion metadata; creating a conversion program from the conversion instructions of the conversion metadata; running the conversion program to perform a data conversion with the processor on one or more data elements of the source dataset to form a target dataset comprising one or more converted data elements; and associating and storing the unique version number with the conversion metadata, with the conversion program, and with each converted data element with the processor in the memory. 
     In still further embodiments, a unique version number may be created when the conversion metadata is modified. In still further embodiments, the unique version number created when the conversion metadata is modified may be associated and stored with the conversion metadata, with the conversion program, and with the target dataset with the processor in the memory. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       A more complete understanding of embodiments of the present invention and advantages thereof may be acquired by referring to the following description taken in conjunction with the accompanying drawings. Understanding that these drawings depict only typical embodiments and are not therefore to be considered to be limiting in scope. Embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIG. 1  an illustrative example of some of the components and computer implemented methods which may be found in a system according to various embodiments described herein. 
         FIG. 2  depicts an example of a block diagram of a server which may be used in the system or standalone according to various embodiments described herein. 
         FIG. 3  shows an example of a block diagram of a client device according to various embodiments described herein. 
         FIG. 4  illustrates an example of how a data build and a data flow can be tracked according to various embodiments described herein. 
         FIG. 5  illustrates an example of how plurality of data builds can be tracked according to various embodiments described herein. 
         FIG. 6  illustrates an example of how a plurality of data flows from different data sources can be tracked according to various embodiments described herein. 
         FIG. 7  shows an example of how data build and data flows can be executed via a data build manager engine according to various embodiments described herein. 
         FIG. 8  shows an example of how operations upon datasets can be executed via the data build manager according to various embodiments described herein. 
         FIG. 9  shows illustrates how conversion metadata is used to generate conversion programs and how conversion metadata is associated with resultant data at the element level according to various embodiments described herein. 
         FIG. 10  shows a block diagram illustrating a process for linking new conversion metadata to datasets by automating data conversion according to various embodiments described herein 
         FIG. 11  shows a block diagram illustrating a process for linking modified metadata to datasets by automating data conversion according to various embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well as the singular forms, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one having ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
     Definitions 
     As used herein, the term “computer” refers to a machine, apparatus, or device that is capable of accepting and performing logic operations from software code. The term “software”, “software code” or “computer software” refers to any set of instructions operable to cause a computer to perform an operation. Software code may be operated on by a “rules engine” or processor. Thus, the methods and systems of the present invention may be performed by a computer based on instructions received by computer software. 
     The term “client device” as used herein is a type of electronic device comprising circuitry and configured to generally perform functions such as recording audio, photos, and videos; displaying or reproducing audio, photos, and videos; storing, retrieving, or manipulation of electronic data; providing electrical communications and network connectivity; or any other similar function. Non-limiting examples of electronic devices include; personal computers (PCs), workstations, laptops, tablet PCs including the iPad, cell phones including iOS phones made by Apple Inc., Android OS phones, Microsoft OS phones, Blackberry phones, digital music players, or any electronic device capable of running computer software and displaying information to a user, memory cards, other memory storage devices, digital cameras, external battery packs, external charging devices, and the like. Certain types of electronic devices which are portable and easily carried by a person from one location to another may sometimes be referred to as a “portable electronic device” or “portable device”. Some non-limiting examples of portable devices include; cell phones, smart phones, tablet computers, laptop computers, wearable computers such as watches, Google Glasses, etc. and the like. 
     The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processor for execution. A computer readable medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks, such as the hard disk or the removable media drive. Volatile media includes dynamic memory, such as the main memory. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that make up the bus. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. 
     As used herein the term “data network” or “network” shall mean an infrastructure capable of connecting two or more computers such as client devices either using wires or wirelessly allowing them to transmit and receive data. Non-limiting examples of data networks may include the internet or wireless networks or (i.e. a “wireless network”) which may include wifi and cellular networks. 
     As used herein, the term “database” shall generally mean a digital collection of data or information. The present invention uses novel methods and processes to store, link, and modify information such digital images and videos and user profile information. For the purposes of the present disclosure, a database may be stored on a remote server and accessed by a client device through the internet (i.e., the database is in the cloud) or alternatively in some embodiments the database may be stored on the client device or remote computer itself (i.e., local storage). A “data store” as used herein may contain or comprise a database (i.e. information and data from a database may be recorded into a medium on a data store). 
     In describing the invention, it will be understood that a number of techniques and steps are disclosed. Each of these has individual benefit and each can also be used in conjunction with one or more, or in some cases all, of the other disclosed techniques. Accordingly, for the sake of clarity, this description will refrain from repeating every possible combination of the individual steps in an unnecessary fashion. Nevertheless, the specification and claims should be read with the understanding that such combinations are entirely within the scope of the invention and the claims. 
     New systems and methods for automatically tracking data through a plurality of data sources and plurality of conversions are discussed herein. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details. 
     The present disclosure is to be considered as an exemplification of the invention and is not intended to limit the invention to the specific embodiments illustrated by the figures or description below. 
     The present invention will now be described by example and through referencing the appended figures representing preferred and alternative embodiments. As perhaps best shown by  FIG. 1 , an illustrative example of some of the physical components which may comprise a system for automatically tracking data through a plurality of data sources and plurality of conversions, “the system”  100 , according to some embodiments is presented. The system  100  is configured to facilitate the transfer of data and information between one or more client devices  400  and servers  300  over a data network  105  with one or more databases on a data store  308  accessible by a server  300 . Each client device  400  may comprise a wired or wireless network connection  104  to an access point  103  which may provide access to the data network  105 . In further embodiments, data and information may be transferred between a data store  408  ( FIG. 3 ) of one or more client devices  400  and/or a data store  308  accessible by a server  300  to be operated on by one or more processors  402  ( FIG. 3 ) of one or more client devices  400  and/or to be operated on by one or more processors  302  ( FIG. 2 ) of a server  300 . 
     In this example, the system  100  comprises at least one client device  400  (but preferably more than two client devices  400 ) configured to be operated by one or more users  101 . Wireless client devices  400  can be mobile devices such as laptops, personal digital assistants, IP phones and other smart phones, or fixed devices such as desktops and workstations that are equipped with a wireless network interface capable of sending data to one or more servers  300  with access to one or more data stores  308  over a data network  105 . 
     Some embodiments of the system  100  described herein implement the ability to track data by automating data conversion through metadata. Data may be stored in a database on a memory accessible to a processor  302  ( FIG. 2 ),  402  ( FIG. 3 ), such as on one or more data stores  308  ( FIGS. 1 and 2 ),  408  ( FIG. 3 ). The data in a database may comprise a plurality of data elements. Data elements in a database may be organized in any format such as with one or more fields, rows, columns, or any other format. Metadata, comprising information on or about a database and/or with data elements in a database may be associated with the database and/or with data elements in the database. Conversion instructions may comprise instructions which may be used to cause or instruct a computer to perform a data conversion operation on one or more source databases and/or on one or more source data elements in a source database to form a converted or one or more target databases and/or on one or more target data elements in a target database. 
     Referring to  FIG. 2 , in an exemplary embodiment, a block diagram illustrates a server  300  which may be used in the system  100  or standalone. The server  300  may be a digital computer that, in terms of hardware architecture, generally includes a processor  302 , input/output (I/O) interfaces  304 , a network interface  306 , a data store  308 , and memory  310 . It should be appreciated by those of ordinary skill in the art that  FIG. 2  depicts the server  300  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 302 ,  304 ,  306 ,  308 , and  310 ) are communicatively coupled via a local interface  312 . The local interface  312  may be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  312  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  312  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  302  is a hardware device for executing software instructions. The processor  302  may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server  300 , a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the server  300  is in operation, the processor  302  is configured to execute software stored within the memory  310 , to communicate data to and from the memory  310 , and to generally control operations of the server  300  pursuant to the software instructions. The I/O interfaces  304  may be used to receive user input from and/or for providing system output to one or more devices or components. User input may be provided via, for example, a keyboard, touch pad, and/or a mouse. System output may be provided via a display device and a printer (not shown). I/O interfaces  304  may include, for example, a serial port, a parallel port, a small computer system interface (SCSI), a serial ATA (SATA), a fibre channel, Infiniband, iSCSI, a PCI Express interface (PCI-x), an infrared (IR) interface, a radio frequency (RF) interface, and/or a universal serial bus (USB) interface. 
     The network interface  306  may be used to enable the server  300  to communicate on a network, such as the Internet, the WAN  101 , the enterprise  200 , and the like, etc. The network interface  306  may include, for example, an Ethernet card or adapter (e.g., 10BaseT, Fast Ethernet, Gigabit Ethernet, 10 GbE) or a wireless local area network (WLAN) card or adapter (e.g., 802.11a/b/g/n). The network interface  306  may include address, control, and/or data connections to enable appropriate communications on the network. A data store  308  may be used to store data. The data store  308  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  308  may incorporate electronic, magnetic, optical, and/or other types of storage media. In one example, the data store  308  may be located internal to the server  300  such as, for example, an internal hard drive connected to the local interface  312  in the server  300 . Additionally, in another embodiment, the data store  308  may be located external to the server  300  such as, for example, an external hard drive connected to the I/O interfaces  304  (e.g., SCSI or USB connection). In a further embodiment, the data store  308  may be connected to the server  300  through a network, such as, for example, a network attached file server. 
     The memory  310  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory  310  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  310  may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor  302 . The software in memory  310  may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory  310  may include a suitable operating system (O/S)  314  and one or more programs  316 . The operating system  314  essentially controls the execution of other computer programs, such as the one or more programs  316 , and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The operating system  314  may be, for example Windows NT, Windows 2000, Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server 2003/2008 (all available from Microsoft, Corp. of Redmond, Wash.), Solaris (available from Sun Microsystems, Inc. of Palo Alto, Calif.), LINUX (or another UNIX variant) (available from Red Hat of Raleigh, N.C. and various other vendors), Android and variants thereof (available from Google, Inc. of Mountain View, Calif.), Apple OS X and variants thereof (available from Apple, Inc. of Cupertino, Calif.), or the like. The one or more programs  316  may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein. 
     Referring to  FIG. 3 , in an exemplary embodiment, a block diagram illustrates a client device  400 , which may be used in the system  100  or the like. The client device  400  can be a digital device that, in terms of hardware architecture, generally includes a processor  402 , input/output (I/O) interfaces  404 , a radio  406 , a data store  408 , and memory  410 . It should be appreciated by those of ordinary skill in the art that  FIG. 3  depicts the client device  400  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 402 ,  404 ,  406 ,  408 , and  410 ) are communicatively coupled via a local interface  412 . The local interface  412  can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  412  can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  412  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  402  is a hardware device for executing software instructions. The processor  402  can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the client device  400 , a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the client device  400  is in operation, the processor  402  is configured to execute software stored within the memory  410 , to communicate data to and from the memory  410 , and to generally control operations of the client device  400  pursuant to the software instructions. In an exemplary embodiment, the processor  402  may include a mobile optimized processor such as optimized for power consumption and mobile applications. The I/O interfaces  404  can be used to receive input from a user or other source and/or for providing system output. User input can be provided via, for example, a keypad, a touch screen, a scroll ball, a scroll bar, buttons, bar code scanner, voice recognition, eye gesture, and the like. System output can be provided via a display device such as a liquid crystal display (LCD), touch screen, and the like. The I/O interfaces  404  can also include, for example, a serial port, a parallel port, a small computer system interface (SCSI), an infrared (IR) interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, and the like. The I/O interfaces  404  can include a graphical user interface (GUI) that enables a user to interact with the client device  400 . Additionally, the I/O interfaces  404  may further include an imaging device, i.e. camera, video camera, etc. 
     The radio  406  enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the radio  406 , including, without limitation: RF; IrDA (infrared); Bluetooth; ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11 (any variation); IEEE 802.16 (WiMAX or any other variation); Direct Sequence Spread Spectrum; Near-Field Communication (NFC); Frequency Hopping Spread Spectrum; Long Term Evolution (LTE); cellular/wireless/cordless telecommunication protocols (e.g. 3G/4G, etc.); VHF spectrum, AM spectrum, wireless home network communication protocols; paging network protocols; magnetic induction; satellite data communication protocols; wireless hospital or health care facility network protocols such as those operating in the WMTS bands; GPRS; proprietary wireless data communication protocols such as variants of Wireless USB; and any other protocols for wireless communication. The data store  408  may be used to store data. The data store  408  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  408  may incorporate electronic, magnetic, optical, and/or other types of storage media. 
     In some preferred embodiments, the client device  400  includes a global positioning system sensor configured to receive latitude and longitude coordinates from satellites (i.e. a GPS signal). 
     In some other preferred embodiments, the client device  400  includes an accelerometer configured to receive user-initiated actions (e.g. shaking the device, moving the device in a pattern, etc.). 
     The memory  410  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory  410  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  410  may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor  402 . The software in memory  410  can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of  FIG. 3 , the software in the memory system  410  includes a suitable operating system (O/S)  414  and programs  416 . The operating system  414  essentially controls the execution of other computer programs, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The operating system  414  may be, for example, LINUX (or another UNIX variant), Android (available from Google), Symbian OS, Microsoft Windows CE, Microsoft Windows 7 Mobile, iOS (available from Apple, Inc.), webOS (available from Hewlett Packard), Blackberry OS (Available from Research in Motion), and the like. The programs  416  may include various applications, add-ons, etc. configured to provide end user functionality with the client device  400 . For example, exemplary programs  416  may include, but not limited to, a web browser, social networking applications, streaming media applications, games, mapping and location applications, electronic mail applications, financial applications, and the like. In a typical example, the end user typically uses one or more of the programs  416  along with a network such as the system  100 . 
     Referring now to  FIGS. 4-10  and in some embodiments, one or more conversion instructions for a source database, optionally down to the row and element level, may captured in the metadata as conversion metadata. Conversion programs may be automatically created by the system  100  based on the conversion metadata. Conversion metadata may include data validation information such as links to regulatory standard references. The conversion metadata may also contain information about the source data and information about the data that results from running a conversion program. 
     The dataset resulting from the conversion activities of a conversion program may be in many forms such as a table in a database or a file on a distributed file system. Regardless of the form, the resultant dataset will be referred to as the target dataset. Conversion programs created from the conversion metadata may be executed on a single computing node, such as by a client device  400  ( FIGS. 1 and 3 ) or server  300  ( FIGS. 1 and 2 ), or in parallel on a plurality of computing nodes, such as on one or more client devices  400  and/or servers  300 . If a conversion program is run in parallel, the conversion program may have plurality of steps such as a step run on the plurality of nodes and a step to collate the results from each node. When a conversion program is executed, conversion runtime information, such as time of execution and execution status, may be created by the conversion program. Conversion runtime information, such as time of execution, execution duration, and execution status, will be referred to as execution attributes. In some embodiments, conversion runtime information or execution attributes may comprise time of execution, execution duration, execution status, and/or any other data related to conversion execution. Conversion metadata, execution attributes, and source and target datasets will be referred to as a data build. The data build&#39;s conversion metadata and execution attributes may be stored in a data build database. A data build manager engine  180  may be used to group data builds into data flows. The data build manager may also execute data processing operations as well as record execution times and run time parameters. Furthermore, the data build manager may archive and perform operations on target datasets from any data build. 
     A data build may contain processing metadata as well as source and target data which may be contained in source datasets and target datasets, respectively. Accurate data tracking requires knowledge of the source of data as well as the processing that was performed on the data. In some embodiments, instructions for processing and source of data are recorded in conversion metadata. Conversion metadata and resultant datasets are kept in sync by attaching the same version ID to the metadata and resultant data sets. Version IDs are kept in sync automatically through the automated creation of processing programs. This will be referred to as automated versioning. 
       FIG. 4  illustrates an example of how a data build  121  and a data flow  120  can be tracked according to various embodiments described herein. In some embodiments, the source dataset  210 , conversion program  150  comprising processing metadata and execution attributes, and resultant target dataset  220  may be referred to as a data build  121 . The data build information may be kept in a data build database  185  in one or more data build tables  186  which may be managed by a data build manager engine  180 . Interaction with the data build manager engine  180  may be performed through a graphical user interface (GUI)  187  which may be provided by an I/O Interface  304 ,  404  of a server  300  or client device  400 . 
     In some embodiments, a GUI  187  may be used to input data into the data build manager engine  180  which can be used to create or view one or more data flows  120  based on conversion metadata which may be used to create a conversion program  150 . Each data flow  120  may comprise one or more data builds  121 . In some embodiments, a first data build  121  and a second data build  122  and any number of other data builds  123  may be stored as a data flow  120 . Additionally, each data flow  120  may comprise lineage data describing the relationship between a first data build  121  and the second data build  122  and any number of other data builds  123 . A conversion program  150  may convert a source dataset  210  to a target dataset  220  based on conversion instructions contained in conversion metadata. Storage and management of conversion metadata at the data build level allows decomposition and construction of complex and multistep data flows  120 . In further embodiments, a GUI  187  may present summary information, such as name and type of the data builds  121 ,  122 ,  123 , in a data flow  120 . By using the GUI  187  to select an item of summary information regarding a data build, the conversion metadata associated with the summary information of that data build may then be presented to the user by the GUI  187 . 
     Turning now to  FIG. 5 , all target datasets  220 , such as a first target dataset  220   a , second target dataset  220   b , final target dataset  220   z , may be archived and their location and access information may be stored in the Data Build Database  185 . This allows operations on the final resultant target dataset  220   z  or any intermediate target datasets  220  such as a first target dataset  220   a , second target dataset  220   b , and the like. The data build manager engine  180  may also generate data tracking or lineage reports based on conversion metadata from related data builds  121 ,  122 ,  123 . 
     A sequential grouping of data builds  121 ,  122 ,  123 , may be referred to as a data flow  120 , and each data flow  120  may comprise any number of data builds  121 ,  122 ,  123 . In some embodiments, the data build database  185  may comprise data, such as a table containing data flow attributes  184 , which may serve as a parent table to a table containing data build attributes  186 . Data flow information may include data build sequence and run time information. Data builds  121 ,  122 ,  123  may be grouped and sequenced into a data flow  120 . Data flow information may be stored in dataflow tables  184  in the data build database  185 . Thus, data build and data flow information  195  from the database  185  can be used to automatically create one or more data conversion processing steps  190   a ,  190   b.    
       FIG. 5  illustrates an example of how plurality of data builds  121 ,  122 ,  123 , can be tracked according to various embodiments described herein. In some embodiments, data builds  121 ,  122 ,  123 , may be used to create multistep data processing operations. As illustrated in  FIG. 5 , a data flow  120  can be broken down into a series of data builds  121 ,  122 ,  123 , and each data build  121 ,  122 ,  123 , may comprise a source dataset  210  and a target dataset  220 . A target dataset  220  may be used as the source dataset  210  of a subsequent data build  121 ,  122 ,  123 . For example, a first target dataset  220   a  of a first data build  121  may be used as a second source dataset  210   b  of a second data build  122  and this process may be repeated any number of times, creating a series of data builds, until a final resultant target dataset  220   z  is produced. Multiple data builds  121 ,  122 ,  123 , can be sequenced together resulting in a final data build  123  with a final resultant target dataset  220   z . In some embodiments, the target dataset  220   a  of a first data build  121  is used as a second source dataset  210   b  to create a second data build  122  comprising the second source dataset  210   b , a second conversion metadata with a second associated unique version number, and a second target dataset  220   b  with the second associated unique version number. Information regarding all conversion activities in the data flow  120  may be maintained in the data build database  185 . Since the conversion metadata in the data build database  185  is recorded and used to create the conversion programs  141   a ,  141   b , of the data builds  121 ,  122 ,  123 , for each data flow  120 , the conversion metadata information is guaranteed to be consistent with respect to all datasets  210 ,  131 , involved in a data flow  120 . In some embodiments, lineage of resultant target datasets  220  can be traced back through an involved second data build  122  and a first data build  121  through conversion metadata stored in the data build database  185 . In further embodiments, the lineage data can be viewed or reported through interaction with the data build manager engine  180 , such as through a GUI  187 . 
     In some embodiments, a first data flow  120   a  can be created that has a first source data set  210   a . One or more second data flows  120  could also be created that has the same said first source data set  210   a . Data flow conversion information  195  could be selected for viewing or reporting by filtering data that has a specific source dataset  210  such as a first source data set  210   a . Filtering data based on a specific source data set may be accomplished through the data build manager engine  180  GUI  187 . Data flow conversion information  195  for a data flow  120  may contain conversion details of a first data build  121 , a second data build  122 , a third data build  123 , and/or any number of other related data builds. In further embodiments, data flow conversion information  195  may comprise data build execution attribute data  130 . Data selected for viewing or reporting may be at data set row or at the data element level. In further embodiments, a GUI  187  may present summary information, of the data builds  121 ,  122 ,  123 , in a data flow  120 . By using the GUI  187  to select an item of summary information regarding a data build, the conversion metadata associated with the summary information of that data build may then be presented to the user by the GUI  187  at data set row or at the data element level. 
     In some embodiments, multiple data tracking systems  100  could be networked together. In further embodiments, a master data tracking system  100  could have access to the database  185  of a plurality of monitored subordinate data tracking systems  100 . The master data tracking system  100  may use conversion metadata from a plurality of data tracking systems  100  to create data tracking and data lineage reports that span a plurality of data tracking systems  100 . 
       FIG. 6  illustrates an example of how a plurality of data flows  120   a ,  120   b , from different data sources can be tracked according to various embodiments described herein. In some embodiments, one or more data flows, such as a first data flow  120   a  and a second data flow  120   b , may be created that have different source data sets but ultimately load data into the same final target dataset  220   z . Each data build may comprise a conversion program  141 . In some embodiments, a first conversion program  141   a  and a second conversion program  141   b  from different data builds  123 ,  126 , may be configured to deposit data to the same final target dataset  220   z . For example, a first source dataset  220   a  may be converted to a first target dataset  220   a  in a first data build  121 . The first target data set  220   a  may be used as a second source dataset  210   b  and converted to a second target dataset  220   b  in a second data build  122 . The second target data set  220   b  may be used as a third source dataset  210   c  and converted to a final target dataset  220   z  in a third data build  123 . Likewise, a fourth source dataset  220   d  may be converted to a fourth target dataset  220   d  in a fourth data build  124 . The fourth target data set  220   d  may be used as a fifth source dataset  210   e  and converted to a fifth target dataset  220   e  in a fifth data build  125 . The fifth target data set  220   e  may be used as a sixth source dataset  210   f  and converted to a final target dataset  220   z  in a sixth data build  126 , with both source datasets  210   c  and  210   f  converted and then deposited into the same final target dataset  220   z.    
     Conversion information from multiple data flows  120   a ,  120   b , may be selected for viewing or reporting by filtering data that has a specific target dataset  220 . An example would be finding all source datasets  210   a ,  210   b ,  210   c ,  210   d ,  210   e ,  210   f , for a given target dataset  220   z . Filtering data for a specific target dataset  220  may be accomplished through the data build manager  180  such as through user input provided through a GUI  187 . 
     As illustrated in  FIG. 6 , in some embodiments, one or more data flows  120   a ,  120   b , may be created that share the same data processing step  190   b . Furthermore, the system  100  may be capable of storing references to global semantic identifiers in the conversion metadata. A global semantic identifier is an identifier associated with a specific semantic meaning. They are generally used to harmonize data from two different data standards. For example, in data standard A, there could be an element named “ethnicity detail” and in data standard B, there could be an element named“ethnic code”. The conversion metadata may include references to a common global semantic identifier to associate these elements from different data standards. Conversion information from multiple data flows  120   a ,  120   b , may be selected for viewing or reporting by filtering data that has one or more specific data conversion processing steps  190   a ,  190   b ,  190   c . A data conversion processing step  190  may be of interest due to a semantic identifier contained in the conversion metadata. Filtering data for a specific conversion processing step  190  or reference to a global semantic identifier may be accomplished through the data build manager  180  such as through user input provided through a GUI  187 . 
     Referring to  FIG. 6 , in some embodiments, one or more data flows  120   a ,  120   b , may be created that may have different source data sets  210   a ,  210   d , but may ultimately load data into the same final target dataset  220   z . Conversion information  195  from one or more data flows  120   a ,  120   b , may be selected for viewing or reporting by filtering data that has a specific row or element in final target dataset  220   z . Origin of a specific data row or element of a source  210  and/or target  220  dataset may come into question during analysis or publication. In such cases, filtering conversion data  195  for a specific row or element in a source  210  and/or target  220  dataset may be accomplished through the data build manager  180  such as through user input provided through a GUI  187 . 
       FIG. 7  shows an example of how data builds  121 ,  122 ,  123 , and data flows  120  can be executed via a data build manager engine  180  according to various embodiments described herein. In some embodiments, the data build manager  180  may, based on commands issued by a user through the data build manager GUI  187 , and information relating parent data flows, such as may be found in data flow tables  184 , to child data builds, such as may be found in data build tables  186 , the data build execution controller  170 , execute one or more data builds  121 ,  122 ,  123 , and/or entire data flows  120 . Data which describes the relationship between parent data flows and child data flows, data which describes the relationship between parent or source data builds and child or target data builds, and data which describes the relationship between parent or source datasets and child or target datasets may be referred to as lineage data. In some embodiments, data build execution attribute data  130  may comprise lineage data. Since conversion metadata for all data builds may contain source and destination table information, lineage data may be easily derived from the stored conversion metadata. For instance, the target data for data build  121  is the source data for data build  122 . In further embodiments, a GUI  187  can be used to display data flow execution attribute data  130  and data flow conversion metadata. For instance, the GUI may display all data build steps for a data flow and the execution status of each data build step. As data builds  121 ,  122 ,  123 , are executed, data build execution attribute data  130  may be created and sent back to the data build manager  180 , by one or more conversion programs  141  ( FIGS. 4-6 ) and stored in the data build manager database  185 . For example and also referring to  FIG. 5 , a first conversion program  141   a  of a first data build  121  may send first data build execution attribute data  130   a  describing the data conversion performed by the first conversion program  141   a  to the data build manager database  185 . Similarly, a second conversion program  141   b  of a second data build  122  may send second data build execution attribute data  130   b  describing the data conversion performed by the second conversion program  141   b  to the data build manager database  185 . In some embodiments, the GUI  187  can be used to see if a data build in dataflow  120  failed execution. The GUI  187  could then be used to display any portion of the conversion metadata of the specific data build that failed execution. 
       FIG. 8  shows an example of how operations upon datasets  220  can be executed via the data build manager  180  according to various embodiments described herein. In some embodiments, operations on all target datasets  220  ( FIGS. 4-6 ) may be performed by the data build manager  180 . Operations may be based on data build information stored in the data build database  185 . In  FIG. 8 , the data build database  185  may comprise information for the data builds  121 ,  122 ,  123 ,  124 . Furthermore, the data build manager  180  may store information regarding the data flows  120   a ,  120   b , that the data builds  121 ,  122 , and  123 ,  124 , respectively, may be grouped into. Thus, the data build manager  180  can perform operations, through the conversion programs  141  ( FIGS. 4-6 ) on the source  210  ( FIGS. 4-6 ) and/or target  220  data sets according to data conversion information stored in its data build database  185 . The data conversion information may be entered as conversion metadata which may be associated with a source data set  210 , a target data set  220 , a data flow  120 , and/or a data build  121 ,  122 ,  123 ,  124 . The data build manager engine  180  may use the conversion metadata to create a conversion program  141  in which the conversion program  141  instructions are retrieved or supplied by the respective conversion metadata. The data build manager engine  180  may be configured to perform a plurality of data build manager operations  160  or processes. Examples of operations or processes the data build manager  180  may perform include but are not limited to the following: 
     1. A data access process  161  may be performed allowing direct data access to any data build&#39;s  121 ,  122 ,  123 ,  124 , source  210  or target  220  dataset and conversion metadata. For example, using the data build manager  180 , a user can determine what datasets  210 ,  220 , have been processed by a specific conversion program  141  through the conversion metadata. The user can then use a third party data analysis application  150  or tools to access those data sets  210 ,  220 , and perform analysis. 
     2. A data merger process  162  may be performed allowing data from any data set  210 ,  220 , can be merged. For example, using the data build manager  180 , a user can determine what data sets  210 ,  220 , have been processed to conform to a specific data standard through the conversion metadata. The user can then direct the data build manager  180  to merge the identified datasets  210 ,  220 , to create one or more new datasets, secure in the knowledge that all the merged new data sets conform to a specific regulatory standard. 
     3. A data compare process  163  may be performed allowing data from any dataset  210 ,  220 , can be compared. For example, using the data build manager  180 , a user can determine what datasets  210 ,  220 , have been processed with conversion metadata that share similar attributes. The user can then compare the identified datasets  210 ,  220 , to determine if and how differences in conversion metadata relate to differences in data. 
     4. A data export process  164  may be performed allowing datasets  210 ,  220 , and data artifacts from any data build  121 ,  122 ,  123 ,  124 , can be exported. For example, using the data build manager  180 , a user may determine what datasets  210 ,  220 , have been processed by using conversion metadata that converts data to the same data standard. The user can then export those datasets  210 ,  220 , and artifacts for submittal to a regulatory agency, such as through a reporting and submission application  155  or any other source capable of receiving data. 
       FIG. 9  shows illustrates how conversion metadata  135  may be used to generate conversion programs  141  and how conversion metadata  135  may be associated with resultant data target  220  at the element level according to various embodiments described herein. In the some embodiments, automated versioning may keep conversion metadata versions in sync with datasets  210 ,  220 , down to the data row and data element level of each dataset  210 ,  220 . In further embodiments, one or more conversion instructions  140  may be entered into or as conversion metadata  135 , such as through the GUI  187  of a data build manager  180 . Conversion metadata information  196  may be sent and received by the data build manager  180  to be stored in a data build database  185 . The data build manager  180  may then create a conversion program  141  which may convert a source dataset  210  into a target dataset  220  based on the conversion instructions  140 . The conversion program  141  may send and receive data flow conversion information  195 , including execution attribute data, to the data build manager  180 . 
     The data build manager  180  may also create a unique version number which may be associated and stored with the conversion metadata  135 , with the conversion program  141 , and with the target dataset  220  with the processor in the memory, such as in the data build database  185 . In further embodiments, the data build manager  180  may associate and store the unique version number with one or more data rows, fields, and data elements of each target dataset  220 . In still further embodiments, the data build manager  180  may associate and store the unique version number with one or more, including every, converted data element in a target dataset  220 . A unique version number may comprise any set of characters or data, such as a string of characters, so that each version number is unique from any other version number of the system  100 , thereby allowing the unique version number to be used to identify the processing metadata and resultant target data. Each unique version number may be a unique identifier that comprises of one or more sequences of numbers or letters generally assigned in increasing order. In some embodiments, a unique version number may be created to correspond to new development or changes in the conversion metadata. 
     As an example, the conversion metadata  135  may include a first conversion instruction  140   a , such as to extract an element A from a source data set  210 . The conversion metadata  135  may further include a second conversion instruction  140   b , such as to convert said element A to an element B. The conversion metadata  135  may further include a third conversion instruction  140   c , such as to load said element B into a target dataset  220  element C. In some embodiments, a unique version number or other identification (ID) may be associated with the conversion metadata  135 . In further embodiments, a unique version number may be associated with each converted data row and/or each data element of a target data set  220 . The data build manager  180  may use the conversion instructions  140   a ,  140   b ,  140   c , in the conversion metadata  135  to create a conversion program  141 . The conversion program  141  may then extract said element A from a source dataset  210 , convert said element A to said element B, and load said element B into said element C. The unique version number of the conversion metadata  135  may be associated or embedded in the instructions of the conversion program  141 . When a conversion program  141  is run, the unique version number may be attached to the data that is written to target data set  310 . In alternative embodiments, the data build manager  180  may create and associate one or more different unique version numbers to the conversion metadata  135 , the conversion program  141 , the target dataset  220  one or more data rows, fields, and data elements of each target dataset  220 , and/or one or more, including every, converted data element in a target dataset  220 . 
       FIG. 10  shows a block diagram illustrating a process for linking new conversion metadata to datasets with automated versioning (“the method  600 ”) by automating data conversion according to various embodiments described herein. In some embodiments, the method  600  may begin  610  and a user may input data conversion metadata  620  into a database, such as in a data build database  185 . The user may input conversion metadata into a data build manager engine  180  through GUI  187  of a computer comprising a processor and memory. The processor may be configured to execute instructions, such as rules engines, of a data build manager engine  180  and a conversion program  141  with data stored in the memory. A unique version number may be created in step  630  by the data build manager engine  180 . In further embodiments, the unique version number may be associated and stored with the conversion metadata by the data build manager engine  180 . In some embodiments, the conversion metadata may be read from a database, such as a data build database  185 , and used to generate a conversion program  141  in step  640  comprising conversion program instructions  140  derived from the conversion metadata. In other embodiments, the conversion metadata may be read from a database, such as a data build database  185 , and sent to a conversion program  141  as program instructions  140  derived from the conversion metadata. 
     In some embodiments, the data build manager engine  180  may associate the unique version number with the conversion program  141  in step  650 . Next, the conversion program  141  may be run in  660  to convert one or more source datasets  210  into one or more target datasets  220 . In some embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with conversion program instructions  140  in memory, such as in a data build database  185 . In further embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with the target data  220  in memory, such as in a data build database  185  in step  670 . In still further embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with one or more data elements, data rows, or fields of the target data  220  in memory, such as in a data build database  185 . After the unique version number is associated and stored with the target data  220  in memory, the method  600  may finish  680 . 
     In some embodiments, the user may issue commands to run the conversion program  141  and any other processes or operations of the data build manager engine  180  through the GUI  187 . In further embodiments, running the conversion program  141  may automatically attach the unique version number to the resultant or converted target data  220 . Thus the conversion metadata is always completely in sync with each target dataset  220  and internal integrity of a data build  121 ,  122 ,  123 ,  124  is ensured. 
       FIG. 11  shows a block diagram illustrating a process for linking modified metadata to datasets by automating data conversion (“the method  700 ”) to perform automated versioning to keep subsequent conversion metadata revisions in sync with resultant target datasets. In some embodiments, the method  700  may begin  710  and a user may modify data conversion metadata  720  into a database, such as in a data build database  185 . The user may modify conversion metadata into a data build manager engine  180  through GUI  187  of a computer comprising a processor and memory. The processor may be configured to execute instructions, such as rules engines, of a data build manager engine  180  and a conversion program  141  with data stored in the memory. When the conversion metadata is modified, a unique version number may then be created in step  730  by the data build manager engine  180 . In further embodiments, the unique version number may be associated and stored with the modified conversion metadata by the data build manager engine  180 . In some embodiments, the modified conversion metadata may be read from a database, such as a data build database  185 , and used to generate a conversion program  141  in step  740  comprising conversion program instructions  140  derived from the modified conversion metadata. In other embodiments, the modified conversion metadata may be read from a database, such as a data build database  185 , and sent to a conversion program  141  as program instructions  140  derived from the modified conversion metadata. 
     In some embodiments, the data build manager engine  180  may associate the unique version number with the conversion program  141  in step  750 . In further embodiments, the unique version number created when the conversion metadata is modified may be associated and stored with the conversion metadata, with the conversion program, and with each converted data element with the processor in the memory. Next, the conversion program  141  may be run in  760  to convert one or more source datasets  210  into one or more target datasets  220 . In some embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with conversion program instructions  140  in memory, such as in a data build database  185 . In further embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with the target data or dataset  220  in memory, such as in a data build database  185  in step  770 . In still further embodiments, the data build manager engine  180  and/or the conversion program  141  may associate and store the unique version number with one or more data elements, data rows, or fields of the target data  220  in memory, such as in a data build database  185 . In still further embodiments, the source dataset, conversion metadata with the associated unique version number, and the target dataset may be stored with the associated unique version number as a data build. After the unique version number is associated and stored with the target data  220  in memory, the method  700  may finish  780 . 
     In some embodiments, the target dataset of a first data build may be used as a second source dataset to create a second data build comprising the second source dataset, a second conversion metadata with a second associated unique version number, and a second converted dataset with the second associated unique version number. In some embodiments, the user may issue commands to run the conversion program  141  and any other processes or operations of the data build manager engine  180  through the GUI  187 . In further embodiments, running the conversion program  141  may automatically attach the unique version number to the resultant or converted target data  220 . Thus, the modified conversion metadata is always completely in sync with each target dataset  220  and internal integrity of a data build  121 ,  122 ,  123 ,  124  is ensured. 
     It will be appreciated that some exemplary embodiments described herein may include one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches may be used. Moreover, some exemplary embodiments may be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer, server, appliance, device, etc. each of which may include a processor to perform methods as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a Flash memory, and the like. 
     Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible program carrier for execution by, or to control the operation of, data processing apparatus. The tangible program carrier can be a propagated signal or a computer readable medium. The propagated signal is an artificially generated signal, e.g., a machine generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a computer. The computer readable medium can be a machine-readable storage device, a machine readable storage substrate, a memory device, a composition of matter effecting a machine readable propagated signal, or a combination of one or more of them. 
     A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     Additionally, the logic flows and structure block diagrams described in this patent document, which describe particular methods and/or corresponding acts in support of steps and corresponding functions in support of disclosed structural means, may also be utilized to implement corresponding software structures and algorithms, and equivalents thereof. The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, solid state drives, or optical disks. However, a computer need not have such devices. 
     Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described is this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet. 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network or the cloud. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client server relationship to each other. 
     Further, many embodiments are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that various actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs)), by program instructions being executed by one or more processors, or by a combination of both. Additionally, these sequences of actions described herein can be considered to be embodied entirely within any form of computer readable storage medium having stored therein a corresponding set of computer instructions that upon execution would cause an associated processor to perform the functionality described herein. Thus, the various aspects of the invention may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the embodiments described herein, the corresponding form of any such embodiments may be described herein as, for example, “logic configured to” perform the described action. 
     The computer system may also include a main memory, such as a random-access memory (RAM) or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM (SRAM), and synchronous DRAM (SDRAM)), coupled to the bus for storing information and instructions to be executed by processor. In addition, the main memory may be used for storing temporary variables or other intermediate information during the execution of instructions by the processor. The computer system may further include a read only memory (ROM) or other static storage device (e.g., programmable ROM (PROM), erasable PROM (EPROM), and electrically erasable PROM (EEPROM)) coupled to the bus for storing static information and instructions for the processor. 
     The computer system may also include a disk controller coupled to the bus to control one or more storage devices for storing information and instructions, such as a magnetic hard disk, and a removable media drive (e.g., floppy disk drive, read-only compact disc drive, read/write compact disc drive, compact disc jukebox, tape drive, and removable magneto-optical drive). The storage devices may be added to the computer system using an appropriate device interface (e.g., small computer system interface (SCSI), integrated device electronics (IDE), enhanced-IDE (E-IDE), direct memory access (DMA), or ultra-DMA). 
     The computer system may also include special purpose logic devices (e.g., application specific integrated circuits (ASICs)) or configurable logic devices (e.g., simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)). 
     The computer system may also include a display controller coupled to the bus to control a display, such as a cathode ray tube (CRT), liquid crystal display (LCD) or any other type of display, for displaying information to a computer user. The computer system may also include input devices, such as a keyboard and a pointing device, for interacting with a computer user and providing information to the processor. Additionally, a touch screen could be employed in conjunction with display. The pointing device, for example, may be a mouse, a trackball, or a pointing stick for communicating direction information and command selections to the processor and for controlling cursor movement on the display. In addition, a printer may provide printed listings of data stored and/or generated by the computer system. 
     The computer system performs a portion or all of the processing steps of the invention in response to the processor executing one or more sequences of one or more instructions contained in a memory, such as the main memory. Such instructions may be read into the main memory from another computer readable medium, such as a hard disk or a removable media drive. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software. 
     As stated above, the computer system includes at least one computer readable medium or memory for holding instructions programmed according to the teachings of the invention and for containing data structures, tables, records, or other data described herein. Examples of computer readable media are compact discs, hard disks, floppy disks, tape, magneto-optical disks, PROMs (EPROM, EEPROM, flash EPROM), DRAM, SRAM, SDRAM, or any other magnetic medium, compact discs (e.g., CD-ROM), or any other optical medium, punch cards, paper tape, or other physical medium with patterns of holes, a carrier wave (described below), or any other medium from which a computer can read. 
     Stored on any one or on a combination of computer readable media, the present invention includes software for controlling the computer system, for driving a device or devices for implementing the invention, and for enabling the computer system to interact with a human user. Such software may include, but is not limited to, device drivers, operating systems, development tools, and applications software. Such computer readable media further includes the computer program product of the present invention for performing all or a portion (if processing is distributed) of the processing performed in implementing the invention. 
     The computer code or software code of the present invention may be any interpretable or executable code mechanism, including but not limited to scripts, interpretable programs, dynamic link libraries (DLLs), Java classes, and complete executable programs. Moreover, parts of the processing of the present invention may be distributed for better performance, reliability, and/or cost. 
     Various forms of computer readable media may be involved in carrying out one or more sequences of one or more instructions to processor for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions for implementing all or a portion of the present invention remotely into a dynamic memory and send the instructions over the air (e.g. through a wireless cellular network or wifi network). A modem local to the computer system may receive the data over the air and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus can receive the data carried in the infrared signal and place the data on the bus. The bus carries the data to the main memory, from which the processor retrieves and executes the instructions. The instructions received by the main memory may optionally be stored on storage device either before or after execution by processor. 
     The computer system also includes a communication interface coupled to the bus. The communication interface provides a two-way data communication coupling to a network link that is connected to, for example, a local area network (LAN), or to another communications network such as the Internet. For example, the communication interface may be a network interface card to attach to any packet switched LAN. As another example, the communication interface may be an asymmetrical digital subscriber line (ADSL) card, an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of communications line. Wireless links may also be implemented. In any such implementation, the communication interface sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. 
     The network link typically provides data communication to the cloud through one or more networks to other data devices. For example, the network link may provide a connection to another computer or remotely located presentation device through a local network (e.g., a LAN) or through equipment operated by a service provider, which provides communication services through a communications network. In preferred embodiments, the local network and the communications network preferably use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link and through the communication interface, which carry the digital data to and from the computer system, are exemplary forms of carrier waves transporting the information. The computer system can transmit and receive data, including program code, through the network(s) and, the network link and the communication interface. Moreover, the network link may provide a connection through a LAN to a client device such as a personal digital assistant (PDA), laptop computer, or cellular telephone. The LAN communications network and the other communications networks such as cellular wireless and wifi networks may use electrical, electromagnetic or optical signals that carry digital data streams. The processor system can transmit notifications and receive data, including program code, through the network(s), the network link and the communication interface. 
     Although the present invention has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present invention, are contemplated thereby, and are intended to be covered by the following claims.