Abstract: Disclosed herein is a process that involves retrieving a data model for a data source, establishing at least one filter operation or at least one clean operation that modifies an aspect of the retrieved data model, onboarding underlying data from the data source while applying the established at least one filter operation or at least one clean operation, defining at least one transformation operation to apply to a portion of the underlying data that has been onboarded, and applying the at least one transformation operation to the portion of the underlying data to thereby assign the data to a semantic network, the semantic network comprising conceptual data components and associative data components.

Abstract: Disclosed herein is a process that involves receiving an indication of an instance of a semantic network, the semantic network comprising conceptual data components and associative data components, receiving a selection of one or more of the conceptual data components and associative data components of the instance of the semantic network, the selection comprising an indication to block the selected one or more conceptual data components and associative data components or selectively filter the selected one or more conceptual data components and associative data components, and presenting a visualization of the semantic network, the visualization being configured to exclude data related to the selected one or more conceptual data components and associative data components or include data related to the selected one or more conceptual data components and associative data components and exclude data not related to the selected one or more conceptual data components and associative data components.

Abstract: Disclosed herein is a process that involves retrieving a data model for a data source, establishing at least one filter operation or at least one clean operation that modifies an aspect of the retrieved data model, onboarding underlying data from the data source while applying the established at least one filter operation or at least one clean operation, defining at least one transformation operation to apply to a portion of the underlying data that has been onboarded, and applying the at least one transformation operation to the portion of the underlying data to thereby assign the data to a semantic network, the semantic network comprising conceptual data components and associative data components.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving input defining a validation point, the validation point comprising at least two or more validation functions applicable to (i) raw data and (ii) other data stored within a semantic network comprising nodes and links, importing source data; applying one or more transformations to the source data, populating the source data into one or more of the nodes and links comprising the semantic network, executing the validation point with respect the source data, based on the executing, determining one or more rules associated with the validation point are not satisfied, and based on the determining, revising either the source data or the other data stored withing the semantic network.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving an input defining at least two master nodes and at least one master link, each master node having at least one or more respective data properties populated with master node data and the master link having at least one or more master link data, the master nodes and master link defining a master semantic network, importing source data into a second semantic network, comparing the source data to the master node data and making a first determination that the source data reflects a data relationship defined by the master node data, and based on the first determination, populating the source data into the second semantic network, wherein the source data populated within the second semantic network reflects the data relationship defined by the master node data and the master link data.

Abstract: Disclosed herein is a process that involves retrieving a data model for a data source, establishing at least one filter operation or at least one clean operation that modifies an aspect of the retrieved data model, onboarding underlying data from the data source while applying the established at least one filter operation or at least one clean operation, defining at least one transformation operation to apply to a portion of the underlying data that has been onboarded, and applying the at least one transformation operation to the portion of the underlying data to thereby assign the data to a semantic network, the semantic network comprising conceptual data components and associative data components.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving input defining a validation point, the validation point comprising at least two or more validation functions applicable to (i) raw data and (ii) other data stored within a semantic network comprising nodes and links, importing source data; applying one or more transformations to the source data, populating the source data into one or more of the nodes and links comprising the semantic network, executing the validation point with respect the source data, based on the executing, determining one or more rules associated with the validation point are not satisfied, and based on the determining, revising either the source data or the other data stored within the semantic network.

Abstract: Disclosed herein is a process that involves receiving an indication of an instance of a semantic network, the semantic network comprising conceptual data components and associative data components, receiving a selection of one or more of the conceptual data components and associative data components of the instance of the semantic network, the selection comprising an indication to block the selected one or more conceptual data components and associative data components or selectively filter the selected one or more conceptual data components and associative data components, and presenting a visualization of the semantic network, the visualization being configured to exclude data related to the selected one or more conceptual data components and associative data components or include data related to the selected one or more conceptual data components and associative data components and exclude data not related to the selected one or more conceptual data components and associative data components.

Abstract: Disclosed herein is a process that involves receiving an indication of an instance of a semantic network, the semantic network comprising conceptual data components and associative data components, receiving a selection of one or more of the conceptual data components and associative data components of the instance of the semantic network, the selection comprising an indication to block the selected one or more conceptual data components and associative data components or selectively filter the selected one or more conceptual data components and associative data components, and presenting a visualization of the semantic network, the visualization being configured to exclude data related to the selected one or more conceptual data components and associative data components or include data related to the selected one or more conceptual data components and associative data components and exclude data not related to the selected one or more conceptual data components and associative data components.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving an input defining at least two master nodes and at least one master link, each master node having at least one or more respective data properties populated with master node data and the master link having at least one or more master link data, the master nodes and master link defining a master semantic network, importing source data into a second semantic network, comparing the source data to the master node data and making a first determination that the source data reflects a data relationship defined by the master node data, and based on the first determination, populating the source data into the second semantic network, wherein the source data populated within the second semantic network reflects the data relationship defined by the master node data and the master link data.

Abstract: Disclosed herein are techniques and tools for verifying data for semantic correctness and/or verifying data for network correctness. In one respect, a method includes receiving input defining a validation point, the validation point comprising at least two or more validation functions applicable to (i) raw data and (ii) other data stored within a semantic network comprising nodes and links, importing source data; applying one or more transformations to the source data, populating the source data into one or more of the nodes and links comprising the semantic network, executing the validation point with respect the source data, based on the executing, determining one or more rules associated with the validation point are not satisfied, and based on the determining, revising either the source data or the other data stored withing the semantic network.

Abstract: Disclosed herein is new approach for structuring an organization's data, involving at a high level establishing a digital context and populating the digital context with digital content to thereby form what is referred to herein as a digital duplicate. In one aspect, the disclosed approach may be embodied in a computer-implemented method that involves: establishing a data structure comprising (i) a structural context that has at least one data component, where each component of the structural context has associated therewith one or more respective data properties (ii) a semantic context that has at least two data types that further describe individual data properties and; and populating underlying data into an instance of the data structure such that underlying data populated into each respective property of the at least one data component has each of the at least two data types of the semantic context.

Abstract: Disclosed herein is a process that involves receiving an indication of an instance of a semantic network, the semantic network comprising conceptual data components and associative data components, receiving a selection of one or more of the conceptual data components and associative data components of the instance of the semantic network, the selection comprising an indication to block the selected one or more conceptual data components and associative data components or selectively filter the selected one or more conceptual data components and associative data components, and presenting a visualization of the semantic network, the visualization being configured to exclude data related to the selected one or more conceptual data components and associative data components or include data related to the selected one or more conceptual data components and associative data components and exclude data not related to the selected one or more conceptual data components and associative data components.

Abstract: Disclosed herein is a process that involves retrieving a data model for a data source, establishing at least one filter operation or at least one clean operation that modifies an aspect of the retrieved data model, onboarding underlying data from the data source while applying the established at least one filter operation or at least one clean operation, defining at least one transformation operation to apply to a portion of the underlying data that has been onboarded, and applying the at least one transformation operation to the portion of the underlying data to thereby assign the data to a semantic network, the semantic network comprising conceptual data components and associative data components.

Abstract: Disclosed herein is new approach for structuring an organization's data, involving at a high level establishing a digital context and populating the digital context with digital content to thereby form what is referred to herein as a digital duplicate. In one aspect, the disclosed approach may be embodied in a computer-implemented method that involves: establishing a data structure comprising (i) a structural context that has at least one data component, where each component of the structural context has associated therewith one or more respective data properties (ii) a semantic context that has at least two data types that further describe individual data properties and; and populating underlying data into an instance of the data structure such that underlying data populated into each respective property of the at least one data component has each of the at least two data types of the semantic context.

Abstract: Disclosed herein is new approach for structuring an organization's data, involving at a high level establishing a digital context and populating the digital context with digital content to thereby form what is referred to herein as a digital duplicate. In one aspect, the disclosed approach may be embodied in a computer-implemented method that involves: establishing a data structure comprising (i) a structural context that has at least one data component, where each component of the structural context has associated therewith one or more respective data properties (ii) a semantic context that has at least two data types that further describe individual data properties and; and populating underlying data into an instance of the data structure such that underlying data populated into each respective property of the at least one data component has each of the at least two data types of the semantic context.

Abstract: Described herein is a control system for a vehicle having an infinitely variable transmission (WT) having a ball planetary variator (CVP), providing a smooth and controlled operation. In some embodiments, the vehicle is a fork lift truck. An operator commands a brake pedal, an accelerator pedal, and a direction switch (or gear selector), which are evaluated by the control system to determine a current operating state of the vehicle. Some operating states include, forward drive, reverse drive, vehicle braking, automatic deceleration, inching, power reversal, vehicle hold, and park, among others.

Abstract: The system and method of the present invention enable a task performance system made up from two or more components to be readily modified, in response to one or more predetermined criteria, to adapt to new functional and/or operational requirements. This modification may be automatic manual, or a combination thereof. Likewise, the predetermined criteria may range from failure of one or more task performance system components, to an instruction received from an authorized operator. The inventive system and method accomplish these objectives by causing one or more of the components of the task performance system to change functionality (e.g., to change the functions being performed by one or more components, to add one or more functions to one or more components, etc.) to implement the desired modification of the entire task performance system.