# Patent

## Title
Extracting data cell transformable to model object

## Abstract

Embodiments relate to systems and methods for extracting a data cell transformable to a model object. Aspects relate to object-based modeling using modeling objects that can be extracted from spreadsheet cells, database entries, or other sources. A modeling client can host modeling logic and an application programming interface (API) to create, access, manipulate, and import/export modeling objects used in modeling applications, such as engineering, medical, financial, and other modeling platforms. In aspects, the source data can be accepted into the modeling client from consumer or business-level applications, whose cell, database, or other data content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. Modeling operations can therefore be performed on or incorporate data that was not originally strictly configured for object-based modeling applications. The extracted model object can also be exchanged with other applications or platforms.

## Background
FIELD
The present teachings relate to systems and methods for extracting a data cell transformable to a model object, and more particularly to platforms and techniques for dedicated modeling of technical, medical, financial, and other systems which are configured to extract spreadsheet cells, database entries, or other data along with associated functions.
BACKGROUND OF RELATED ART
A spectrum of modeling platforms and options exist today for engineers, managers, developers and other professionals. In the case of engineering, medical, technical, financial, and other advanced modeling resources, a range of platforms are available for users interested in setting up, running and maintaining financial modeling systems. For example, organizations interested in relatively sophisticated modeling applications, such as geophysical models for detecting oil reserves or other geologic features or equity market analysis based on Black-Sholes option pricing models, a company or other organization may choose to install advanced modeling software on mainframe-class computers to run those classes of models and obtain various projections, reports, and other results. Such mainframe platform and related installations, however, can involve costs on the order of millions of dollars or more, and may require the full time attention of highly skilled professionals, including programmers and managers with advanced training. As a consequence, putting a mainframe-based ERP modeling operation into place may not be practical or possible for many organizations or users.
On the other end of the spectrum, managers, engineers and others may employ widely available entry-level applications to capture operational data and attempt to develop predictive models for engineering, financial, medial, and other applications. That class of applications can include, for example, consumer or business-level spreadsheet, database, or data visualization programs for technical, financial, and other purposes. For instance, a manager of a manufacturing facility may use a commercially available spreadsheet application to enter production numbers, schedules, and other details of that site. However, attempting to extract useful modeling outputs from those classes of applications can be difficult or impossible. For one, spreadsheet, database, and other widely available applications are typically built to produce reports based on already existing data, but not to generate modeling outputs or objects that represent predictive outputs or scenarios. For another, existing spreadsheet, database, and other applications typically involve limitations on cell size, number of dimensions, overall storage capacity, and other program parameters which, in the case of large-scale modeling operations, may be insufficient to operate on the data sets necessary to produce and run meaningful models.
For another, the data structures and outputs of existing spreadsheet, database and other entry-level or commonly available applications are typically arranged in proprietary format, rather than a widely interoperable object-based or other universal format. As still another drawback, the cells, rows, columns, and other data elements within commonly available spreadsheets, databases, and other entry-level programs can not be extracted as separate units and exported to other modeling or analytic tools. In short, the use of spreadsheet, database, and other consumer or business-level applications to conduct modeling operations involves significant shortcomings, due in part to the fact that those classes of platforms are not designed to reliable handle modeling functionality.
At present, therefore, a manager, developer engineer, or other professional or user with modeling requirements is faced with a choice between installing a large and expensive mainframe-based ERP solution with its attendant infrastructure, a spreadsheet or database-based entry level solution with its attendant limitations on power and data handling, or a combination of those two types of platforms. It may be desirable to provide object-based or object-compatible modeling platforms capable of generating modeling objects which encapsulate various modeling features, and which are exportable to other external modeling tools.



DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:
FIG. 1 illustrates an overall system for a modeling network including various hardware and connectivity resources that can be used in systems and methods for extracting a data cell transformable to a model object, according to various embodiments of the present teachings;
FIG. 2 illustrates an exemplary modeling network including a modeling server and connectivity resources, according to various embodiments;
FIG. 3 illustrates an exemplary hardware configuration for a modeling server that can be used in systems and methods for generating floating desktop representation of extracted model object, according to various embodiments; and
FIG. 4 illustrates a flow diagram of overall modeling processing for systems and methods for extracting a data cell transformable to a model object, according to various embodiments.



DESCRIPTION OF EMBODIMENTS
Embodiments of the present teachings relate to systems and methods for extracting a data cell transformable to a model object. More particularly, embodiments relate to platforms and techniques that can access, extract, and generate corresponding modeling objects in a native object-based or object-compatible format. The modeling objects produced via a modeling client or other modeling tool according to the present teachings can encapsulate both source data describing a physical, medical, technical, financial, or other process or phenomena, and modeling attributes that relate the source data to predictive scenarios, specific models, and other features. In embodiments, the modeling objects can be extracted or “lifted” from data sources such as spreadsheet or database programs, and in embodiments, an icon or other representation of those objects can be placed on the desktop or other user interface of a local modeling client. These and other embodiments described herein address the various noted shortcomings in known modeling technology, and provide a user or operator with enhanced modeling power on a desktop or other client, while allowing seamless extraction, generation, local storage, and communication of model objects to backend mainframe platforms, data centers, middleware servers, other modeling clients, and/or other local or remote modeling, storage, or data processing resources.
Reference will now be made in detail to exemplary embodiments of the present teachings, which are illustrated in the accompanying drawings. Where possible the same reference numbers will be used throughout the drawings to refer to the same or like parts.
FIG. 1 illustrates an overall network 100 in which systems and methods for extracting a data cell transformable to a model object can be implemented, consistent with various embodiments of the present teachings. In embodiments as shown, a modeling client 102 can communicate with a variety of local and remote resources, including an mainframe platform 202 via one or more network 112. Client 102 can be or include, for instance, a personal computer, a server, a dedicated workstation, a mobile device, or other machine, device, hardware, or resource. One or more network 112 can be or include, for example, the Internet, a virtual private network (VPN), a local area network such as an EthemetTM network, or other public or private network or networks. Mainframe platform 202 can be or include commercially available platforms or installations, such as, merely for example, mainframe or enterprise platforms available from SAP Inc. of Walldorf, Germany, and other sources.
Mainframe platform 202 can include modules, logic, and functionality to perform an array of computation and data storage tasks, including data warehousing, data mining, statistical analyses, financial planning, inventory management, customer resource management, engineering design, and other applications. In implementations as shown, mainframe platform 202 can host or communicate with a variety or resources including, merely illustratively, a mainframe data store 206, and logic or applications including an analytic module 204. Mainframe platform 202 can contain, host, support, or interface to other data processing hardware, software, and other resources. In embodiments, modeling client 102 can likewise communicate with other local or remote resources, such as a middleware server 208 hosting or interfacing to a set of data stores for online analytical processing (OLAP) or other functions. Modeling client 102 can also communicate or interface with other local or remote servers, services, data stores, or other resources.
In embodiments as shown, modeling client 102 can operate under an operating system 118, such as a distribution of the LinuxTM, UnixTM, or other open source or proprietary operating system. Modeling client 102 can present a user interface 130, such as a graphical user interface or command line interface, operating under operating system 118 to receive commands and inputs from a user, and operate modeling client 102. Modeling client 102 can communicate with storage resources including a modeling store 104, such as a local or remote database or data store. Modeling store 104 can store a set of modeling objects 106, in which data, functions, procedures, attributes, and/or other information related to one or more model object 110 can be encapsulated and stored. In embodiments, model object 110 can be encoded in extensible markup language (XML) format. In embodiments, model object 110 can be encoded in other object-based or object-compatible formats or data structures. Modeling client 102 can communicate with mainframe platform 202 via a modeling application programming interface (API) 108. Modeling application programming interface (API) 108 can include, for instance, defined function calls or calls to other routines, calculations, or features, as well as data structures and parameters associated with modeling operations. For example, modeling application programming interface (API) 108 can include a function call to invoke a Monte Carlo simulation model based on a set of supplied data, such as an identified set of dimensions extracted from a spreadsheet or database. Other functions, routines, resources, and features can be called, invoked, or instantiated via modeling application programming interface (API) 108. According to embodiments in various regards, one or more local or remote modeling packages, modules, or other supporting applications can be instantiated via modeling module 120 and modeling application programming interface (API) 108 to manipulate source data and resulting one or more model object 110.
In embodiments, a user of modeling client 102 can access, modify, or add data modeling objects to a set of data modeling object 106 via a modeling module 120 hosted in modeling client 102. Set of data modeling objects 106 can include data objects that the user of modeling client 102 has directly entered, or, in aspects, which the user of modeling client has imported or extracted from sources such as consumer or business-level spreadsheet, database, and/or other applications or platforms. Modeling module 120 can itself be or include applications, software modules or hardware modules, or other logic or resources to operate on set of modeling objects 106. Modeling module 120 can, merely illustratively, include or access logic or modules for invoking and manipulating a variety of scientific, technical, engineering, medical, financial, manufacturing, or other modeling operations. For instance, modeling module 120 can be or include applications or logic for performing Monte Carlo simulations, finite element analyses, Black-Scholes option pricing or other market analyses, epidemiological projections, geophysical models or simulations, or other simulations, models, trend mappings, projections, or other predictive processes. In embodiments in one regard, after invoking modeling module 120 and performing any modeling task, the user of modeling client 102 can locally store and/or export one or more model object 110 to external platforms or resources.
In embodiments as shown, the user of modeling client 102 can for instance export or communicate one or more model object 110 to mainframe platform 202 via modeling application programming interface (API) 108, for storage and use at a local or remote location from within that platform. In aspects, mainframe platform 202 can receive model object 110 directly, without a necessity for translation, re-formatting, or invoking any spreadsheet, database, or other application from which data encapsulated in one or mode model object 110 originated. In aspects, mainframe platform 202 can operate on one or more model object 110, and transmit or return that data or other results to modeling client 102 via modeling application programming interface (API) 108. Thus, according to aspects of the present teachings, modeling objects can be exchanged directly and programmatically between modeling client 102, mainframe platform 202 or other larger-scale or remote platforms, including for instance middleware server 208 or other comparatively large-scale or higher-capacity modeling or analytic tools.
In terms of operating on source data and generating one or more model object 110 for local storage and/or exchange with mainframe platform 202 or other platforms, and as shown for instance in FIG. 2, according to various embodiments, a user of modeling client 102 can invoke modeling module 120 to manipulate a set of source data 114 to identify, configure, and/or extract the functional objects, attributes, or other features of a set of data to produce a modeling output. In embodiments as shown, modeling module 120 can access a set of source data 114, from which data, attributes, and/or other metadata can be extracted to generate one or more model object 110. In aspects, set of source data 114 can be generated, hosted, or stored by or in a local application 134, such as a spreadsheet, database, accounting, word processing, presentation, or other application or software. In aspects, set of source data 114 can comprise data previously, or newly generated in the form of an object-based modeling object, such as a modeling object entered, imported, or specified by the user of modeling client 102. In aspects, set of source data 114 can comprise data originally stored or generated in a consumer or business-level spreadsheet, database, and/or other application or software. In aspects, set of source data 114 can be initially formatted or encoded in a non-object oriented format, such as in a cellular array or in a relational database format. In aspects, set of source data 114 can be initially formatted or encoded in an object-oriented format, such as extensible markup language (XML) format. In aspects, a user of modeling client 102 can highlight, select, or otherwise specify all or a portion of set of source data 114 to generate one or more extracted functional object 116. For instance, a user can highlight a column of set of source data 114 to identify and extract data as well as functional relationships of interest, to the user, as a unified object. Thus, purely illustratively and as shown, a user may wish to obtain a view on a current month's sales figures including gross sales, tax, production or delivery costs, and cost basis, as well as other parameters related to sales activity. In aspects as shown, a user can, for instance, highlight those functional relationships by dragging a cursor or otherwise selecting a set of cells to group together, and form one or more extracted functional object 116. In aspects, selection can include the extraction of set of data elements 136, such as values stored in spreadsheet cells or database entries. In aspects, once a set of data elements 136 are selected, the functional, computational, or other modeling parameters associated with that data can be stored or associated with one or more extracted functional object 116. For instance, modeling module 120 can store associated routines, computations, processes, or other attributes or functional specifications for one or more extracted functional object 116 in set of attributes 122, which can be stored or associated with one or more extracted functional object 116. In aspects, set of attributes 122 can include the identification of or linkage to any routines, interfaces, or other functional or computational resources that will be associated with one or more extracted functional object.
According to various embodiments, analytic module 204 of mainframe platform 202, or other resource or platform receiving one or more extracted functional object 116 from modeling client 102 can thereby obtain both data values derived or obtained from set of source data 114, as well as functional or procedural resources and relationships associated with that data. One or more extracted functional object 116 along with any associated set of attributes 122 can be encoded or stored in one or more model object 110, which can thereby be transparently exported to mainframe platform 202, middleware server 208, or other platforms or destinations for further modeling operations. In embodiments, one or more model object 110 can be consumed or accessed by modeling client 102 itself, or other local or remote clients.
FIG. 3 illustrates an exemplary diagram of hardware, software, connectivity, and other resources that can be incorporated in a modeling client 102 configured to communicate with one or more network 112, including to interface with mainframe platform 202, middleware server 208, and/or other local or remote resources. In embodiments as shown, modeling client 102 can comprise a processor 124 communicating with memory 126, such as electronic random access memory, operating under control of or in conjunction with operating system 118. Operating system 118 can be, for example, a distribution of the LinuxTM operating system, the UnixTM operating system, or other open-source or proprietary operating system or platform. Processor 124 also communicates with a model store 104, such as a database stored on a local hard drive, which may store or host set of modeling objects 106. Processor 124 further communicates with network interface 128, such as an Ethernet or wireless data connection, which in turn communicates with one or more networks 112, such as the Internet, or other public or private networks. Processor 124 also communicates with modeling module 120 along with modeling application programming interface (API) 108 and/or other resources or logic, to execute control and perform modeling calculation, translation, data exchange, and other processes described herein. Other configurations of the network modeling client 102, associated network connections, and other hardware and software resources are possible. While FIG. 3 illustrates modeling client 102 as a standalone system comprises a combination of hardware and software, modeling client 102 can also be implemented as a software application or program capable of being executed by a conventional computer platform. Likewise, modeling client 102 can also be implemented as a software module or program module capable of being incorporated in other software applications and programs. In either case, modeling client 102 can be implemented in any type of conventional proprietary or open-source computer language.
FIG. 4 illustrates a flow diagram of overall processing that can be used in systems and methods for extracting a data cell transformable to a model object, according to various embodiments. In 402, processing can begin. In 404, a user of modeling client 102 or other client or device can invoke or instantiate modeling module 120 or other logic, to perform modeling operations. In 406, modeling module 120 can access model store 104 and extract one or more model object 110 from set of modeling objects 106. In 408, modeling computations or other operations can be performed on one or more model object 110. For example, a modeling operation can be performed to project or predict the output of a factory based on various supply scenarios for parts, materials, energy costs, or other variables. In 410, the values, functions, linkages, or other attributes of one or more data model object 110 that were accessed, produced, or modified by the modeling operations can be captured, fixed, or locked down by modeling module 120. For instance, the resulting one or more model object 110 can be stored to set of modeling objects 106 in model store 104, or other databases or data stores.
In 412, modeling application programming interface (API) 108 can be invoked by modeling module 120, by mainframe platform 202, or other resources to transfer one or mode model object 110 to mainframe platform 202. In embodiments, one or more model object 110 can for instance be communicated to mainframe platform 202 via a secure connection or channel, such as a secure socket layer (SSL) connection, via a channel encrypted using a public/private key infrastructure, or other channel or connection. In 414, one or more model object 110 can be received in modeling module 120 from mainframe platform 202 or other resource, as appropriate. For example, an updated version of one or more model object 110 reflecting new data, new modeling results, or other information can be received in modeling module 120. In 416, the resulting new, updated, or modified one or more model object 110 can be stored to set of modeling objects 106 in model store 104, as appropriate. In embodiments, one or more model objects 110 can in addition or instead be stored to mainframe data store 206, to middleware server 208, to another modeling client or other client, data center, or other site or destination. In 418, modeling module 120 can convert one or more model objects 110 to spreadsheet, database, or other format, and export any converted data as a set of cell-formatted information, or data encoded in other formats. For instance, modeling module 120 can convert or translate one or more model objects to cell data values or database entries, and export that data to client-level applications on modeling client 102 or other local or remote devices or storage. In 420, processing can repeat, return to a prior processing point, jump to a further processing point, or end.
The foregoing description is illustrative, and variations in configuration and implementation may occur to persons skilled in the art. For example, while embodiments have been described wherein one or more model object 110 is accessed and manipulated via one modeling client 102, in embodiments, one or more users can use multiple modeling clients, or networks including modeling clients or other resources, to operate on model object data. For further example, while embodiments have been described in which modeling client 102 may interact with one mainframe platform 202 and/or one middleware server 208, in embodiments, one or more modeling client 102 can interact with multiple mainframe platforms, data centers, middleware servers, and/or other resources, in various combinations. Yet further, while embodiments have been described in which a modeling client 102 interacts with a mainframe platform 202 and/or middleware server 208, in embodiments, rather than interact with large-scale mainframe platforms, data centers, or middleware servers, modeling client 102 can interact with other local or remote modeling clients, networks of those clients, or, in embodiments, can operate to perform modeling operations on a stand-alone basis, without necessarily communicating with other modeling platforms. Other resources described as singular or integrated can in embodiments be plural or distributed, and resources described as multiple or distributed can in embodiments be combined. The scope of the present teachings is accordingly intended to be limited only by the following claims.

## Claims

1. A method comprising:
invoking an instance of a relational database, the relational database comprising a plurality of data elements;
receiving input indicative of a selection of a portion of the relational database from a user via the instance of the relational database, the selection comprising a first data element of the plurality of data elements having a first value and a second data element of the plurality of data elements having a second value;
identifying a functional relationship between the first value and the second value;
encapsulating the functional relationship between the first value and the second value in a functional object;
encapsulating, by a processor, the first value, the second value, and the functional object in a predictive database model object;
invoking a modeling application to perform a modeling operation on the predictive database model object to predict an output and create a first modified first value and a first modified second value;
locking the first modified first value, the first modified second value, and the functional object in the predictive database model object in view of performing the modeling operation on the predictive database model object;
sending the predictive database model object, comprising the portion of the relational database, to a mainframe platform via a secure channel over a network, wherein the mainframe platform performs a modeling operation using the first modified first value, the first modified second value, and the functional object in the predictive database model object to create a second modified first value, a second modified second value, or a changed functional object in the predictive database model object;
receiving a modified predictive database model object from the mainframe platform via the secure channel over the network, wherein the modified predictive database model object comprises the second modified first value, the modified changed second value, or the changed functional object; and
converting the modified predictive database model object to a non-object oriented format, wherein the relational database performs an operation using the second modified first value, the second modified second value, or the changed functional object from the converted modified predictive database model object.

2. The method of claim 1, wherein the portion comprises at least one of spreadsheet data or database data.

3. The method of claim 1, further comprising storing the predictive database model object in storage of the client machine.

4. The method of claim 1, wherein the predictive database model object is encapsulated in extensible markup language (XML) format.

5. The method of claim 1, wherein the functional relationship comprises at least one of a function call parameter or a linkage parameter.

6. The method of claim 1, wherein the predictive database model object is extensible via an appending of at least one of a third value of a third data element or an additional functional relationship to the predictive database model object.

7. The method of claim 1, wherein the predictive database model object is importable into at least one application.

8. The method of claim 1, where the relational database is hosted in at least one of local storage coupled to the processor or remote storage.

9. A system comprising:
a memory to store a relational database; and
a processor, operatively coupled to the memory, to:
invoke an instance of the relational database, the relational database comprising a plurality of data elements;
receive input indicative of a selection of a portion of the relational database from a user via the instance of the relational database, the selection comprising a first data element of the plurality of data elements having a first value and a second data element of the plurality of data elements having a second value;
identify a functional relationship between the first value and the second value;
encapsulate the functional relationship between the first value and the second value in a functional object;
encapsulate the first value, the second value, and the functional object in a predictive database model object;
invoke a modeling application to perform a modeling operation on the predictive database model object to predict an output and create a first modified first value and a first modified second value;
lock the first modified first value, the first modified second value, and the functional object in the predictive database model object in view of performing the modeling operation on the predictive database model object;
send the predictive database model object, comprising the portion of the relational database, to a mainframe platform via a secure channel over a network, wherein the mainframe platform performs a modeling operation using the first modified first value, the first modified second value, and the functional object in the predictive database model object to create a second modified first value, a second modified second value, or a changed functional object in the predictive database model object;
receive a modified predictive database model object from the mainframe platform via the secure channel over the network, wherein the modified predictive database model object comprises the second modified first value, the second modified second value, or the changed functional object; and
convert the modified predictive database model object to a non-object oriented format, wherein the relational database performs an operation using the second modified first value, the second modified second value, or the changed functional object from the converted modified predictive database model object.

10. The system of claim 9, wherein the portion comprises at least one of spreadsheet data or database data.

11. The system of claim 9, wherein the memory is further to store the predictive database model object.

12. The system of claim 9, wherein the predictive database model object is encapsulated in extensible markup language (XML) format.

13. The system of claim 9, wherein the functional relationship comprises at least one of a function call parameter or a linkage parameter.

14. The system of claim 9, wherein the predictive database model object is extensible via an appending of at least one of a third value of a third data element or an additional functional relationship to the predictive model object.

15. The system of claim 9, wherein the predictive database model object is importable into at least one application.

16. The system of claim 9, where the memory comprises at least one of local storage or remote storage.

17. A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processor, cause the processor to:
invoke an instance of a relational database, the relational database comprising a plurality of data elements;
input indicative of a selection of a portion of the relational database from a user via the instance of the relational database, the selection comprising a first data element of the plurality of data elements having a first value and a second data element of the plurality of data elements having a second value;
identify a functional relationship between the first value and the second value;
encapsulate the functional relationship between the first value and the second value in a functional object;
encapsulate, by the processor, the first value, the second value, and the functional object in a predictive model object;
encapsulate the functional relationship between the first value and the second value in a functional object;
encapsulate, by the processor, the first value, the second value, and the functional object in a predictive database model object;
invoke a modeling application to perform a modeling operation on the predictive database model object to predict an output and create a first modified first value and a first changed modified value;
lock the first modified first value, the first modified second value, and the functional object in the predictive database model object in view of performing the modeling operation on the predictive database model object;
send the predictive database model object, comprising the portion of the relational database, to a mainframe platform via a secure channel over a network, wherein the mainframe platform performs a modeling operation using the first modified first value, the first modified second value, and the functional object in the predictive database model object to create a second modified first value, a second modified second value, or a changed functional object in the predictive database model object;
receive a modified predictive database model object from the mainframe platform via the secure channel over the network, wherein the modified predictive database model object comprises the second modified first value, the second modified second value, or the changed functional object; and
convert the modified predictive database model object to a non-object oriented format, wherein the relational database performs an operation using the second modified first value, the second modified second value, or the changed functional object from the converted modified predictive database model object.

18. The computer system readable medium of claim 17, wherein the predictive database model object is importable into at least one application.