Abstract: A fluidic device holder configured to orient a fluidic device. The device holder includes a support structure configured to receive a fluidic device. The support structure includes a base surface that faces in a direction along the Z-axis and is configured to have the fluidic device positioned thereon. The device holder also includes a plurality of reference surfaces facing in respective directions along an XY-plane. The device holder also includes an alignment assembly having an actuator and a movable locator arm that is operatively coupled to the actuator. The locator arm has an engagement end. The actuator moves the locator arm between retracted and biased positions to move the engagement end away from and toward the reference surfaces. The locator arm is configured to hold the fluidic device against the reference surfaces when the locator arm is in the biased position.

Abstract: Embodiments relate to systems and methods for object-based modeling using modeling objects exportable to external modeling tools. 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 accepted into the modeling client can be consumer or business-level applications, whose cell, database, or other data content can be extracted and encapsulated in an object-oriented format, such as extensible markup language (XML) format. The modeling client can exchange one or more resulting modeling object directly with external platforms, such as mainframe platforms, middleware databases, and others, via the application programming interface (API) on a programmatic basis.

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 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 extracting a model object from a multi-dimensional source database. 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 accepted into the modeling client can include consumer or business-level applications, whose database or other content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. The resulting model object can be pivoted along selected dimensions, or otherwise manipulated. The modeling client can exchange one or more modeling object directly with external platforms, such as mainframe modeling platforms, via the application programming interface (API) on a programmatic basis.

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 aggregating data from data sources according to a hierarchical mapping generated from dimensions of the data sources. A set of applications such as online analytical processing (OLAP) applications can access the combined data of a set of multi-dimensional data sources via a transform engine. The set of data sources can be configured with diverse dimensions and associated data, which in general do not reflect a strictly hierarchical structure. In embodiments, the transform engine can combine or aggregate the set of data sources using common dimensions or data points, and build an index into a transform table reflecting the hierarchical level of dimension from each data source in a combined hierarchical mapping.

Abstract: Embodiments relate to systems and methods for analyzing data extracted from a set of data sources. A set of applications such as online analytical processing (OLAP) applications can access the combined data of a set of data sources via an aggregation engine. The set of data sources can be configured with diverse dimensions and associated data. In general the data sources may not be expected to reflect a strictly consistent structure. In embodiments, the aggregation engine can combine or aggregate the set of data sources using common dimensions or data points, and build an index into a transform table reflecting a combined mapping. An OLAP or other application can then perform statistical computations, searches, sorts, and/or other operations on the combined mapping, even when the original data sources do not contain identical dimensions or other formats.

Abstract: Embodiments relate to systems and methods for object-based modeling using hierarchical 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. In aspects, 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 extensible markup language (XML) or other object-oriented format. Various model objects can be extracted from source data, and aggregated into a hierarchical model object encapsulating and preserving the underlying object layers. In aspects, one or more of the model object layers can functionally combine at least two other layers, to relate levels of data in hierarchical fashion.

Abstract: A computing system generates a model object from source data, the model object based on an extracted data element of the source data and an attribute of the extracted data element and generates a management interface representing the model object. The computing system provides at least one of a communications status or a security status of the model object via the management interface. The management interface is used to control how the model objects and other resources can exchange outputs, share internal data, and otherwise operate.

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 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 generating sets of model objects having data messaging pipes. 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 set of modeling objects can be connected via data messaging pipes on a selective basis, so that individual objects can transmit or receive data outputs generated by other objects. The model objects can share, exchange, or combine computational outputs, internal data, and other information from other model object(s). Multiple pipes can be established at various levels or dimensions between objects or groups of objects.

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 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 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: A fluidic device holder configured to orient a fluidic device. The device holder includes a support structure configured to receive a fluidic device. The support structure includes a base surface that faces in a direction along the Z-axis and is configured to have the fluidic device positioned thereon. The device holder also includes a plurality of reference surfaces facing in respective directions along an XY-plane. The device holder also includes an alignment assembly having an actuator and a movable locator arm that is operatively coupled to the actuator. The locator arm has an engagement end. The actuator moves the locator arm between retracted and biased positions to move the engagement end away from and toward the reference surfaces. The locator arm is configured to hold the fluidic device against the reference surfaces when the locator arm is in the biased position.

Abstract: Embodiments relate to systems and methods for validating interpolation results using Monte Carlo simulations on interpolated data 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, in order to test the interpolated input data, the operator can initiate a Monte Carlo or other variational analysis using access a validation dialog. The Monte Carlo process can apply randomized perturbations to the values of the interpolated input variables, and track the results of that perturbation on the other interpolated inputs.

Abstract: Embodiments relate to systems and methods for validating interpolation results using Monte Carlo simulations on interpolated data 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, in order to test the interpolated input data, the operator can initiate a Monte Carlo or other variational analysis using access a validation dialog. The Monte Carlo process can apply randomized perturbations to the values of the interpolated input variables, and track the results of that perturbation on the other interpolated inputs.

Abstract: Embodiments relate to systems and methods for generating portable interpolated data using object-based encoding of interpolation results. 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, the interpolation engine can interact with an object encoder module to access, extract, and encode the interpolated inputs and/or other data in an object-based format or schema, such as the extensible markup language (XML). The object encoder can generate an object-oriented interpolated data container to transmit to a local or remote database, and expose that container to sets of applications for access and use by those applications.