Abstract: A method for labeling data items includes receiving, from a client, user input generated by a user of the client. The method also includes generating query conditions based on the user input. Further, the method includes retrieving, from a custom knowledge system integration module and using the query conditions, a list of label candidates. In addition, the method includes determining, via the client, that the user approves the list of label candidates. Moreover, the method includes generating, based on the determination, a label candidate query using at least the list of label candidates. Further, the method includes submitting the label candidate query to a large language model (LLM) engine. Moreover, the method includes obtaining, in response to the label candidate query, a list of target labels from the LLM engine. Also, the method includes labeling a plurality of unlabeled data items using the list of target labels.

Abstract: A method for labeling data items includes receiving, from a client, user input generated by a user of the client. The method also includes generating query conditions based on the user input. Further, the method includes retrieving, from a custom knowledge system integration module and using the query conditions, a list of label candidates. In addition, the method includes determining, via the client, that the user approves the list of label candidates. Moreover, the method includes generating, based on the determination, a label candidate query using at least the list of label candidates. Further, the method includes submitting the label candidate query to a large language model (LLM) engine. Moreover, the method includes obtaining, in response to the label candidate query, a list of target labels from the LLM engine. Also, the method includes labeling a plurality of unlabeled data items using the list of target labels.

Abstract: An apparatus in an illustrative embodiment comprises at least one processing device that includes at least a processor and a memory coupled to the processor. The at least one processing device is configured to receive a video signal in an edge computing site of an information processing system configured in accordance with a core-edge architecture, and to extract key frames from the received video signal. The at least one processing device is further configured, for each of at least a subset of the extracted key frames, to generate a multimodal embedding comprising one or more key frame vectors each characterizing one or more of image information, audio information and text information of the extracted key frame. The at least one processing 10 device is still further configured to process a search query based at least in part on the key frame vectors.

Abstract: A method for managing a data stream processing pipeline includes: monitoring, by an orchestrator, data stream ingestion of a streaming storage system (SSS) to obtain data stream metrics; analyzing, by the orchestrator, the data stream metrics based on a user-defined scaling policy; making, based on the analyzing and by the orchestrator, a first determination that task manager scaling is required; making, by the orchestrator, a second determination that the data stream ingestion is increased, in which the second determination indicates an increase in a number of parallel stream segments associated with a data stream, in which a segment store hosted by the SSS manages the parallel stream segments; and to increase a stream processing system's compute capability and by the orchestrator, initiating scaling of a number of the plurality of task managers to support the increase in the number of the parallel stream segments.