Abstract: A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.

Abstract: A method of automatically generating baggage driver staffing recommendations including receiving a first set of flight parameters for a first flight, creating a first predictive staffing model for the first flight by simulating missed bag quantities for a range of driver quantities using a first computer-implemented machine learning model and the first set of flight parameters, and automatically generating a first recommended driver quantity predicted to result in a quantity of missed bags using the first predictive staffing model and a threshold quantity of missed bags. The missed bag quantities are simulated using a simulator, the first computer-implemented machine learning model is configured to relate driver quantities and flight parameters to expected missed bag quantities, and the first predictive staffing model relates predicted quantities of missed bags to quantities of staffed drivers.

Abstract: Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

Abstract: A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

Abstract: Apparatus and associated methods relate to dynamically configuring a User Interface (UI) dashboard corresponding to an Internet of Things (IoT) device. The UI dashboard is dynamically configured so as to render data that is appropriate to its specific use or application in the UI dashboard. A processor dynamically configuring the UI dashboard receives a device-data definition of data that the IoT device can provide. The processor captures a specific one of a plurality of selectable use cases of the IoT device. The processor configures the UI dashboard based, at least in part, on the specific one of the plurality of selectable use cases captured. The processor receives a data stream provided by the IoT device via the internet, the data stream conforming to the device-data definition. The processor renders the data stream received in the UI dashboard configured. The processor then sends the UI dashboard with the data stream rendered therein to a display device for display.

Abstract: Apparatus and associated methods relate to dynamically configuring an Internet of Things (IoT) device and a processor interfacing the IoT device. The processor captures a specific one or more of a plurality of deployment configuration templates for the IoT. The IoT device is then configured by the processor based, at least in part, on the one or more of the plurality of deployment configuration templates captured. The processor then deploys software for use by the processor to interface with the IoT device configured. The software deployed is determined based on the one or more of the plurality of deployment configuration templates captured.

Abstract: A product count of a number of physical products within a physical grouping of a plurality of the physical products is determined based on an image of the physical grouping. A cycle counter generates image coordinates for visible product surfaces within the image. The cycle counter generates three-dimensional virtual base coordinates for the expected locations of the plurality of the physical products within the physical grouping. The cycle counter determines the actual locations of the visible product surfaces within three-dimensional space based on a comparison of the image coordinates and the virtual base coordinates. The cycle counter determines the product count of the number of physical products within the physical grouping based on the actual locations.

Abstract: Apparatus and associated methods relate to transformations of data provided by an Internet of Things (IoT) device. A processor defines a data transformation corresponding to data transmitted by the IoT device in an IoT system. The data transformation includes a definition of a transformation of data from a first format to a second format and an identification (ID) of the IoT device. The processor then compiles the data transformation to produce compiled executable code for performing the data transformation. The processor registers the compiled executable code for the data transformation as an available transformation for various components of the IoT system. The processor facilitates execution of the compiled executable code so as to perform the data transformation upon a data stream provided by the IoT device.

Abstract: A method of processing first video data from incoming video data includes receiving the incoming video data; preprocessing the incoming video data, by a first computer processor, according to preprocessing parameters, wherein the preprocessing parameters include formatting the incoming video data to create first video data; publishing the first video data to an endpoint; subscribing, by a second computer processor, to the endpoint; and processing, by a second computer processor, the first video data to determine at least one output that is indicative of a first inference dependent upon the first video data. The preprocessing parameters that format the incoming video data to create the first video data are dependent upon the processing to be performed on the first video data.

Abstract: A method of processing first video data of a region of interest from incoming video data includes preprocessing, according to preprocessing parameters defined within a runtime configuration file, incoming video data to create the first video data of the first region of interest and processing, by a computer processor, the first video data to determine at least one output that is indicative of a first inference dependent upon the first video data. The preprocessing parameters that format incoming video data to create the first video data are dependent upon the processing to be performed on the first video data.

Abstract: A method of processing first video data of a region of interest from incoming video data includes receiving, by a computer processor, the first video data that is preprocessed according to preprocessing parameters defined within a runtime configuration file, the preprocessing includes formatting the incoming video data to create the first video data of the first region of interest and processing, by the computer processor, the first video data to determine at least one output that is indicative of a first inference dependent upon the first video data. The preprocessing parameters are dependent upon the processing to be performed on the first video data.

Abstract: A method of database recovery includes starting a first database server, starting a second database server, starting a third database server, and starting an application server after starting the first database server, the second database server, and the third. The first, second, and third database servers are configured to store data according to first, second, and third database management systems, respectively, and the application server is configured to run application modules of a set of application modules. The method of database recovery further includes starting a search module, starting a product catalog management module, starting a web connectivity module after starting the search module, starting a graphical user interface module after starting the web connectivity module, and starting at least one business operations module.

Abstract: Apparatus and associated methods relate to dynamically generating a rule for determining an alert condition for an Internet of Things (IoT) device. The rule is dynamically generated for determining an alert condition that is appropriate to a specific use or application of the IoT device. A processor receives a device-data definition and captures a specific one of a plurality of selectable use cases of the IoT device. The processor generates the rule for determining an alert condition based, at least in part, on the specific one of the plurality of selectable use cases captured. The processor receives a data stream provided by the IoT component and applies the rule generated to the data stream. The processor determines the alert condition based on the rule applied to the data stream and generates an alert signal in response to the rule applied to the data stream satisfying the alert condition determined.

Abstract: A method of improving a main output of a main AI model processing first video data includes analyzing incoming video data via a first processing pipeline and analyzing incoming video data via a second processing pipeline. The second processing pipeline includes receiving the incoming video data from the camera; identifying, by a parameter optimization module, first test preprocessing parameters; preprocessing the incoming video data according to the first test preprocessing parameters defined within a test runtime configuration file, wherein the first test preprocessing includes formatting the incoming video data to create first test video data; accessing the first test video data by a test AI model; processing the first test video data by the test AI model to determine a first test output that is indicative of a first test inference dependent upon the first test video data; and comparing the first test output and the main output to a baseline criterion.

Abstract: A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

Abstract: Pathways between reference locations in a physical system are generated based on a layout table. Nodes and edges of the directed graph are associated with cell locations of the layout table. The cell locations define features of the reference locations. Parameters of the nodes and edges are defined based on descriptors recalled from the cells associated with the nodes and edges. The nodes and edges are configured based on the descriptors. Path data regarding potential pathways is generated based on the defined nodes and edges.

Abstract: A method of improving compute performance of a distributed database system includes querying a control node of the distributed database system to ascertain a plurality of attributes characterizing an initial state of a table object in the distributed database system. Queries executed in the distributed database system are recorded. Based on these queries and at least some of the plurality of attributes, an candidate state of the table object with lower compute load than the initial state is generated. An executable transformation mapping the initial state of the table object to the candidate state is then assembled and applied to the table object.

Abstract: A method of processing first video data of a region from incoming video data collected by a camera. The method includes subscribing, by a first subscriber, to an endpoint at which the first video data is published after the first video data is preprocessed according to preprocessing parameters defined within a runtime configuration file. The preprocessing includes formatting the incoming video data to create the first video data of the first region of interest with a second field of view that is less than the first field of view. The method further includes processing, by a computer processor, the first video data to determine at least one output that is indicative of a first inference dependent upon the first video data. The preprocessing parameters that format incoming video data to create the first video data are dependent upon the processing to be performed on the first video data.