Abstract: Embodiments described herein are directed to an adaptive AI model for 3D object detection using synthetic training data. For example, an ML model is trained to detect certain items of interest based on a training set that is synthetically generated in real time during the training process. The training set comprises a plurality of images depicting containers that are virtually packed with items of interest. Each image of the training set is a composite of an image comprising a container that is packed with items of non-interest and an image comprising an item of interest scanned in isolation. A plurality of such images is generated during any given training iteration of the ML model. Once trained, the ML model is configured to detect items of interest in actual containers and output a classification indicative of a likelihood that a container comprises an item of interest.

Abstract: The maintaining of a database that is distributed across a front database and one or more cloud databases. Rather than have the front and remote portions of the database be fixed, a data distribution manager evaluates databases segments following a data distribution policy to identify one or more database segments that are to be transferred to or from the one or more cloud databases from or to the front database. In response, the data distribution manager causes the identified database segment to be moved. Accordingly, the data that is in the front database (and the data that is stored in the cloud) may vary over time.

Abstract: Models and tools for facilitating database instance consolidation are described. One approach obtains standardized database instance performance data, receives instance isolation criteria which are applied to divide instances into sets, determines resource usage for each set, and assigns instance sets to target machines based on resource usage and capacity. Each instance set is associated with a virtual machine in some cases. A security isolation criterion indicates that specified instances shall not co-reside on a target machine; an organizational criterion indicates that specified instances shall be located together in a single data center; a compatibility criterion indicates that instances shall not reside in the same virtual machine, the same physical server, or the same server pool. Users iteratively reach an acceptable consolidation of instances at one or more data centers, such as an enhanced assignment of instances that is more efficient than a linear additive solution.

Abstract: Models and tools for facilitating database instance consolidation are described. One approach obtains standardized database instance performance data, receives instance isolation criteria which are applied to divide instances into sets, determines resource usage for each set, and assigns instance sets to target machines based on resource usage and capacity. Each instance set is associated with a virtual machine in some cases. A security isolation criterion indicates that specified instances shall not co-reside on a target machine; an organizational criterion indicates that specified instances shall be located together in a single data center; a compatibility criterion indicates that instances shall not reside in the same virtual machine, the same physical server, or the same server pool. Users iteratively reach an acceptable consolidation of instances at one or more data centers, such as an enhanced assignment of instances that is more efficient than a linear additive solution.

Abstract: A supply chain (or other process) visibility solution is disclosed that combines RFID technology with a data visibility architecture to provide real-time or near real-time supply chain information at various stages of the supply chain (or other process). The system uses a data gathering architecture which collects data from multiple sources about an object. The sources of data include RFID tags, scanners and manual input. The data from the RFID tags is used to correlate the gathered data to attributes associated with the object (e.g., name, description or price). The gathered data and attributes are stored in a data store according to a customizable schema and published for consumption by one or more business processes.