Abstract: The present disclosure describes apparatuses and methods for contextual search of a storage system. In some aspects, a metadata manager of a storage system receives a query to search the data stored on the storage media of the apparatus. The metadata manager identifies an entry in a relational database of the metadata manager that includes a label that is relevant to the query and determines, based on the entry in the relational database, a reference address of a target node in a navigational database of the metadata manager that corresponds to the label. As results for the query to search, the metadata manager returns an object of the target node at the reference address in the navigational database and corresponding objects of relative nodes connected to the target node via respective links. By so doing, the metadata database may enable contextual or implicit search of data in the storage system.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: A storage control device coupled to a storage device and located remotely from a host device receives media object data from the host device. The storage control device identifies a type of the media object data and select, based on the identified type, a computational model from among a plurality of computational models for use by a computational engine of the storage control device. The computational engine uses the selected computational model to generate metadata describing the media object data. The metadata is stored in the storage device so as to be selectively retrievable from the storage device separately from the media object data.

Abstract: The present disclosure describes apparatuses and methods for artificial intelligence-enabled search of a storage system. In some aspects, a metadata manager of a storage system receives a label of an object that an AI engine detects in data stored in the storage system. The metadata manager creates, in a relational section of a metadata database, an entry for the detected object with an identifier of the label of the detected object and an address of a node corresponding to the detected object. The metadata manager also creates, in a navigational portion of the metadata database and with the address of the detected object, the node that includes a reference to a relative node of another object and a weight of a relationship between the node and the relative node. By so doing, the metadata database may be searched based on weighted relationships between various nodes, thereby enabling contextual or implicit search of data in the storage system.

Abstract: Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.

Abstract: A storage control device coupled to a storage device and located remotely from a host device receives media object data from the host device. The storage control device identifies a type of the media object data and select, based on the identified type, a computational model from among a plurality of computational models for use by a computational engine of the storage control device. The computational engine uses the selected computational model to generate metadata describing the media object data. The metadata is stored in the storage device so as to be selectively retrievable from the storage device separately from the media object data.

Abstract: Various embodiments provide a Gray code detector that is not designed to look for a specific defect signature, but rather looks for wedges that are in error or close to being in error, as determined through a probabilistic analysis that considers the mean and sigma of a minimum Viterbi Metric Margin (minVMM) distribution of a servo wedge relative to a defined threshold for a Wedge Error Rate (WER).