Patent Application
20110054854
The present teachings relate to systems and methods for generating dimensionally altered model objects, and more particularly to platforms and techniques for accessing model objects outputted by dedicated modeling of technical, medical, financial, and other systems, and expanding or reducing the row, column, or other dimension of those objects, for instance to conform the data structure of the altered version to a new data object destination.
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, data center 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 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 further regards, besides lacking an ability to extract data objects from desired locations, available platforms do not permit a user to adjust the row, column, depth, or other dimension of a source object to match or conform to a destination data object, such as a database or spreadsheet.
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 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 a set of modeling objects which encapsulate various modeling features, and which objects can be scaled or dimensionally manipulated to ensure compatibility with target data stores.
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:
Embodiments of the present teachings relate to systems and methods for generating dimensionally altered model objects. More particularly, embodiments relate to platforms and techniques that can access, extract, and generate 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 database programs or others, and stored to local storage of a local modeling client.
The resulting set or sets of model objects can be accessed and manipulated by a modeling module or other logic in the modeling client to scale, resize, or otherwise alter the dimensions and/or related attributes of one or more of those objects. In aspects, data from the model object(s) can be expanded or, generally speaking, “stretched” in its dimensions, for instance to create or add additional rows, columns, depth planes, and/or other dimensions. The expanded dimensions can, in embodiments, involve the copying of data into duplicate cells or patterns of cells. In aspects, data from the model object(s) can be reduced or, generally speaking, “compressed” in its dimensions, for instance to reduce the number rows, columns, depth planes, and/or other dimensions. The reduced dimensions can, for instance, be generated or determined by combining data from different cells or entries, deleting cells or entries, or performing other operations on the constituent data content to make the number of rows, columns, depth planes, and/or other dimensions or associated attributes smaller.
According to aspects, the set of dimensionally altered model objects can be stored to a new or destination database, spreadsheet, data store, and/or other data object or data schema. In embodiments, the dimensionally altered data object(s) can have their dimensions scaled up or down to conform to the data schema of the destination data object, for example, to fit into a relational database or other structure. It may be noted that in embodiments, the modeling module can leave or retain the original or source data in the source object or other location, in its pre-altered state. In aspects, each dimensionally altered data object can be duplicated, inserted, and/or stored to a destination multiple times, for example to different entries in a database, or to different databases or other destinations 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, allowing the flexible configuration and scaling of model objects to insert and store model objects into desired data object containers, formats, and/or other destinations. Systems and methods according to the present teachings also allow seamless generation, local storage, and communication of model objects and their interconnections 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.
Mainframe platform 102 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 102 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 102 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 Linux™, Unix™, 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 modeling object 110 can be encapsulated and stored. In embodiments, modeling object 110 can be encoded in extensible markup language (XML) format. In embodiments, modeling object 110 can be encoded in other object-based or object-compatible formats or data structures. Modeling client 102 can communicate with mainframe platform 102 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 modeling 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 modeling 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 modeling object 110 to mainframe platform 102 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 102 can receive modeling 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 modeling object 110 originated. In aspects, mainframe platform 102 can operate on one or more modeling 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 102 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 modeling object 110 for local storage and/or exchange with mainframe platform 102 or other platforms, and as shown for instance in
In 412, modeling application programming interface (API) 108 can be invoked by modeling module 120, by mainframe platform 102, or other resources to transfer one or mode modeling object 110 to mainframe platform 102. In embodiments, one or more modeling object 110 can for instance be communicated to mainframe platform 102 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 102 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, 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.
According to various embodiments of the present teachings, and as for example generally illustrated in
In aspects, as noted modeling module 120 can a perform configuration and management of set of model objects 140 In aspects, modeling module 120 can comprise a module, logic, or layer that is separate from, or can be integrated with, operating system 118 of modeling client 102. According to aspects in various regards, modeling module 120 can permit a user to highlight, click, select by radio button or link, or otherwise select or identify one or more objects in set of model objects 140 upon which re-sizing or other actions to alter the dimensional attributes of one or more model objects may be taken.
In aspects as shown, an extracted model object 146 that is highlighted or otherwise selected from set of model objects 140 can be operated on by modeling module 120 to generate a scaled model object 142. In embodiments as shown, the original extracted model object 146 can have a set of dimensional attributes and/or values, such as, for instance, a row by column size of 2×2, such as in a matrix, database entry, spreadsheet cell, or other data format. In embodiments as likewise shown, the modeling module 120 can determine the original dimensional attributes of extracted model object 146, and expand those dimensions to generate additional row values, column values, depth plane values, and/or other attributes or features. For instance, modeling module can generate a scaled model object having a row by column size of 36×36, in which the smaller 2×2 sized object is replicated around the center block of the extracted model object 146. In aspects, modeling module 120 can, as shown, place the original data values from the entries or cells of the 2×2 object into the corners of scaled model object 142, preserving the corner values of the originating object in corresponding corners of the expanded object. In embodiments, other techniques for translation or importation of data values to scaled model object 142 can be used. For instance, the cells of extracted model object 146 can be filled with multiple copies of the data values hosted in extracted model object 146, or, in embodiments, the cells or other nodes of scaled model object 142 can be filled with zero or other default values. In aspects, scaled model object 142 can be scaled to greater than three dimensions. In aspects, it may be noted that in addition to expanding the dimensions of scaled model object 142, the dimensions of extracted model object 146 can be reduced, for instance, to eliminate or or more rows and/or columns. Other types of dimensional alterations can be performed on extracted model object 146 to generate scaled model object 142.
In embodiments, it may be noted that scaled model object 142 can be stored, copied, and/or otherwise inserted into destination model object 144. In aspects, scaled model object 142 can be scaled by modeling module 130 to conform to the dimensions or other configuration requirements of destination model object 144, such as, for example, to conform the number of rows and columns of scaled model object 142 to those of destination model object 144. Other types of conforming, fitting, or other scaling relationships between extracted model object 146 and destination model object 144 can be used. It may be likewise noted that in aspects, scaled model object 142 can be stored, hosted, or otherwise communicated or inserted to multiple destination objects, such as other local or remote databases, spreadsheets, and/or other data objects or stores.
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 102 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 102 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. Still further, while embodiments have been described in which one or more scaled model object 142 is stored to one destination model object 144, in embodiments, scaled model object 142 can be stored or copies to multiple targets or destinations. 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.
