Abstract: Embodiments relate to systems and methods for generating an interpolation data template to normalize analytic runs. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. In aspects, an interpolation history database can stores results of a variety of interpolation studies or results, from which a set of interpolation templates can be derived. The templates can reflect trends or patterns discovered or displayed in the historical interpolation results, for instance, to produce expected values for different variables based on averages, thresholds, or other criteria applied to the historical results.

Abstract: Embodiments relate to systems and methods for generating interpolated data sets converging to optimized results using iterative overlapping inputs. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. In aspects, to attempt to drive the set of interpolated input data and/or other results to an optimized and/or steady-state value or values, the interpolation engine can generate a set of shifts or alterations to the set of combined input data as that data exists, to create a set of iterative overlapping input data.

Abstract: Embodiments relate to systems and methods for binding multiple interpolated data objects. A database management system can store operational data, such as financial, medical, or other information. A user can access a set of target data, representing an output desired to be generated from an interpolated set of input data. Thus, the average air temperature of a region may be known for ten years, along with other inputs including water temperature, wind speed, and other data. The interpolation engine can receive a target temperature for the current year, and generate water temperatures, wind speeds, or other inputs that will produce the target temperature. The interpolation engine can also be configured to access and combine two or more sets of interpolated data using a set of data bindings into one multiply-bound interpolation object. The combined or multiply-bound interpolation object can be operated on by the interpolation engine or accessing application(s) on a unified and updateable basis.

Abstract: Embodiments relate to systems and methods for filtering interpolated input data based on user-supplied or other approximation constraints. A database management system can store operational data, such as financial, medical, or other information. A user can access a set of target data, representing an output desired to be generated from an interpolated set of input data. Thus, the average air temperature of a region may be known for ten years, along with other inputs including water temperature, wind speed, and other data. The interpolation engine can receive a target temperature for the current year, and generate water temperatures, wind speeds, or other inputs that will produce the target temperature. The engine can also receive sets of approximation constraints supplied by a user, application, and/or other source to apply to the interpolated input values, and force those values to conform to an additional layer of desired criteria or constraints.

Abstract: Embodiments relate to systems and methods for generating interpolated input data sets using reduced input source objects. A database can store operational data, such as financial, climate or other information. A user can input or access a set of target data, representing output the user wishes to be generated from an interpolated set of input data based on an interpolation function. Thus, the average air temperature of a region may be known for the last ten years, along with other inputs such as water temperature, wind speed, etc. The target data can include an expected average temperature for the current year. The interpolation engine can receive the target temperature, and interpolate other climate inputs that will produce the target output temperature. The interpolation engine can also reduce the number of predetermined data objects or the dimensions of input data sets to generate interpolated inputs based on more compact inputs.

Abstract: Embodiments relate to systems and methods for embedding an interpolated data object in an application data file. A database management system can store operational data, such as financial, climate or other information. A user can input or access target data, representing an output desired to be generated from an interpolated set of input data. Thus, the average air temperature of a region may be known for several years, along with other inputs including water temperature, wind speed, and other data. The target data can include an expected average temperature for the current year. The interpolation engine can receive the current-year target temperature, and generate water temperatures, wind speeds, and other variables that produce the target temperature. In aspects, the interpolation engine can embed the interpolated data as an object in a local or remote spreadsheet or other local data file via dynamic data links, to permit automatic updating of the embedded interpolated data.

Abstract: Embodiments relate to systems and methods for tracking differential changes in conformal data input sets. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. The operator can access a view of known (or interpolated) input data to view one or more series of interpolated input data, and analyze the differential between those interpolated values. The operator can for instance apply a constraint or filter to view only those interpolated series whose maximum marginal difference for any variable is less that a given threshold, such as ten percent, and graphically navigate between different series.

Abstract: Embodiments relate to systems and methods for interpolating alternative input sets based on user-weighted variables. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. The operator can access a view or dialog on the set of known (or interpolated) input data to manually select different weights to be applied to one or more variables in the various input sets. By applying different groups of weights, the operator can study or simulate the effects of changing the relative importance of different inputs, and generate a series of different inputs and outputs based on those varying weights.

Abstract: Embodiments relate to systems and methods for interpolating conformal input sets based on a target output. A database management system can store sets of operational data, such as financial, medical, climate or other information. A user can input or access a set of target data, representing an output which a user wishes to be generated from an interpolated set of input data based on an interpolation function. Thus, the average air temperature of a region may be known for the last ten years, along with various inputs including water temperature, wind speed, and other climate data. The target data can include an expected average temperature for the current year. The interpolation engine can receive the target temperature for the current year, and generate those water temperatures, wind speeds, and other input variables that will produce the target output temperature.

Abstract: Embodiments relate to systems and methods for training a self-learning network using interpolated input sets based on a target output. A database management system can store sets of operational data, such as financial, medical, climate or other information. A user can input or access a set of target data, representing an output which a user wishes to be generated from an interpolated set of input data. The interpolation engine can generate a conformal interpolation function and input sets that map to the set of target output data. After interpolation, the interpolation engine can transmit the interpolated inputs, along with the set of target output data and other information, to a self-learning network such as a neural or fuzzy logic network. The self-learning network can be trained to converge to the target output based on the interpolated input results as generated by the interpolation engine, thus reproducing the desired interpolation function.

Abstract: Embodiments relate to systems and methods for the automatic propagation of data changes in distribution operations in a hierarchical database. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes in the store, such as quarters. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can access or host sequence logic to ensure that data is spread in an order that takes dependencies, or other factors into account. In aspects, the spreading tool can be configured to generate a propagation trace to identify other nodes in the data store that may be connected to or depend on the data content of the recipient child node(s). A set of propagated data changes, such as updates to values, formatting, or other data, can be automatically transmitted to those other nodes upon completion of the original data spreading operations.

Abstract: Embodiments relate to systems and methods for the distribution of data in a lattice-based database via placeholder nodes. A data store can store data in a lattice-based database or other three-dimensional hierarchical format. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes at different levels or points in the lattice structure to lower nodes or locations, such as quarters. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can insert child nodes representing quarters, months, or other insertion point(s) represented or encoded by a set of placeholder nodes, dividing, combining, or otherwise distributing quantities appropriately. In aspects, data can be spread from multiple parents and/or multiple target child nodes, at once. In aspects, the spreading tool can access or host sequence logic to ensure data spreading in an order that takes dependencies, formatting, or other factors into account.

Abstract: Embodiments relate to systems and methods for the conditioned distribution of data in a lattice-based database using spreading rules. A data store can store data in a lattice-based database or other three-dimensional hierarchical format. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes at different levels or points in the lattice structure to lower nodes or locations, such as quarters. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can insert child nodes representing quarters, months, or other insertion point(s) represented or encoded by a set of placeholder nodes, dividing, combining, or otherwise distributing quantities appropriately. In aspects, data can be spread from multiple parents and/or multiple target child nodes, at once. In aspects, the spreading tool can access or host sequence logic to ensure data spreading in an order that takes dependencies, formatting, or other factors into account.

Abstract: Embodiments relate to systems and methods for generating a push-up alert of fault conditions in the distribution of data in a hierarchical database. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as quarters, in a hierarchical data store. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can insert child nodes representing quarters, months, or other insertion point(s) encoded by a set of placeholder nodes, dividing quantities appropriately. The spreading tool can access or host logic to ensure that data is spread in an order that takes dependencies, formatting, or other factors into account. The spreading tool can also be configured to detect numerical errors, logical errors, or other faults and attempt to identify conflicting rules or other sources of error. Those rules can be automatically removed and/or removed via user selection.

Abstract: Embodiments relate to systems and methods for generating an optimized output range for a data distribution in a hierarchical database. A data store can store data in a hierarchical format, for instance, in a tree. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as nodes representing quarters. A spreading tool can insert child nodes representing quarters, months, or other at insertion point(s) represented or encoded by a set of placeholder nodes, dividing quantities appropriately. In aspects, the spreading tool can access multiple sets of spreading rules which govern the distribution of data from higher level nodes to lower level nodes. In aspects, the spreading tool can conduct multiple passes of data distribution using different sets of spreading rules, capturing the outputs expressed in the child nodes and selecting rule sets which produce a desired deviation, range, or other characteristics.

Abstract: Embodiments relate to systems and methods for generating iterated distributions of data in a hierarchical database. A data store can store data in a hierarchical format, for instance, in a tree. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as nodes representing quarters. A spreading tool can insert child nodes representing quarters, months, or other at insertion point(s) represented or encoded by a set of placeholder nodes, dividing quantities appropriately. In aspects, the spreading tool can access or host sequence logic including multiple sets of spreading rules which each ensure that data is spread in an order that takes dependencies, formatting, outputs, or other factors into account. In aspects, the spreading tool can receive or generate a prioritized ordering of the sets of spreading rules, to ensure that data distribution passes are iterated in a desired or error-free order.

Abstract: Embodiments relate to systems and methods for the distribution of data in a hierarchical database via placeholder nodes. A data store can store data in a hierarchical schema. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as quarters. Parent nodes can be associated with a set of programmatic placeholder nodes. The placeholder nodes can encode inheritance classes for lower nodes for spreading or other operations, such as division of profit into quarters, months, or other periods. A spreading tool can insert child nodes representing quarters, months, or other periods at the insertion point(s) represented by the placeholder nodes, dividing quantities appropriately. In aspects, data can be spread from multiple parents at once. In aspects, the spreading tool can host or access spreading rules to ensure that data is spread in an order taking dependencies or other factors into account.

Abstract: Embodiments relate to systems and methods for conditioning the distribution of data in a hierarchical database. A data store can store data in a hierarchical format. Higher-level data, such as yearly profit, may be desired to be spread from parent nodes to lower nodes, such as quarters. Parent nodes can be associated with a set of programmatic placeholder nodes. A spreading tool can insert child nodes representing quarters, months, or other at insertion point(s) represented or encoded by a set of placeholder nodes, dividing quantities appropriately. In aspects, data can be spread from multiple parents at once. In aspects, the spreading tool can access or host sequence logic to ensure that data is spread in an order that takes dependencies, formatting, output connections, or other factors into account.

Abstract: A device designed to separate fats waters and other contaminants from used vegetable oil.

Abstract: Embodiments relate to a unified management interface and related resources for generating dimensionally altered model objects. 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. The source data accepted into the modeling client can include consumer or business-level applications, whose spreadsheet, database or other content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. A modeling module can access the modeling objects, decode their row, column, depth, and/or other dimensional attributes, and scale those objects using scaling rules or other logic.