A data recovery method includes receiving a request to recover target audio and video behavior data generated during use of an audio and video application by a target user. The target audio and video behavior data has been deleted from a database. The method includes obtaining a target data category of the target audio and the video behavior data; searching a blockchain system for the target audio and video behavior data based on the target data category, the blockchain system being configured to store operation data generated by the audio and video application that includes first operation data of audio and video behavior data. The method includes storing the target audio and video behavior data in the database; and returning the target audio and video behavior data to the target user.

A video retrieval method and apparatus, a device and a storage medium are provided. The method comprises the following steps: acquiring a comparison video clip from a video library according to the duration of a to-be-tested video (S110); determining the similarity between the to-be-tested video and the comparison video clip by a target spatio-temporal neural network, a spatio-temporal convolutional layer of the target spatio-temporal neural network being configured to be capable of performing two-dimensional convolution and temporal dimension information processing, respectively (S120); and traversing the video library, and outputting a retrieval result according to the similarity (S130).

A video retrieval method and apparatus, a device and a storage medium are provided. The method comprises the following steps: acquiring a comparison video clip from a video library according to the duration of a to-be-tested video (S110); determining the similarity between the to-be-tested video and the comparison video clip by a target spatio-temporal neural network, a spatio-temporal convolutional layer of the target spatio-temporal neural network being configured to be capable of performing two-dimensional convolution and temporal dimension information processing, respectively (S120); and traversing the video library, and outputting a retrieval result according to the similarity (S130).

A system for integrated analysis and management of video data includes a routing server configured to receive video data from an external input video providing device, a video database configured to store the received video data, a video analyzing server cluster including a plurality of video analyzing servers configured to analyze the video data, a metadata database configured to store metadata of the video data, and a video search server configured to search the metadata database and the video database. As described in various embodiments of the present disclosure, the system can perform integrated analysis of various video data received from a number of channels and provide an analysis result, and can also rapidly provide an accurate search result in response to a user's request for search.

A system for integrated analysis and management of video data includes a routing server configured to receive video data from an external input video providing device, a video database configured to store the received video data, a video analyzing server cluster including a plurality of video analyzing servers configured to analyze the video data, a metadata database configured to store metadata of the video data, and a video search server configured to search the metadata database and the video database. As described in various embodiments of the present disclosure, the system can perform integrated analysis of various video data received from a number of channels and provide an analysis result, and can also rapidly provide an accurate search result in response to a user's request for search.

Methods, systems, and media for demoting search results based on search query risk are provided. In some embodiments, a method for demoting search results includes: receiving a search query for a video content item; generating a plurality of search results in response to the search query; calculating a set of result goodness values; calculating a query goodness value associated with the search query based on the set of result goodness values; identifying a threshold goodness value based on the query goodness value; in response to determining that a first result goodness value of the set of result goodness values is less than the threshold goodness value, demoting a first search result of the plurality of search results, wherein the first result goodness value is associated with the first search result; and causing at least a portion of the plurality of search results to be presented based on the demotion.

Methods, systems, and media for demoting search results based on search query risk are provided. In some embodiments, a method for demoting search results includes: receiving a search query for a video content item; generating a plurality of search results in response to the search query; calculating a set of result goodness values; calculating a query goodness value associated with the search query based on the set of result goodness values; identifying a threshold goodness value based on the query goodness value; in response to determining that a first result goodness value of the set of result goodness values is less than the threshold goodness value, demoting a first search result of the plurality of search results, wherein the first result goodness value is associated with the first search result; and causing at least a portion of the plurality of search results to be presented based on the demotion.

A system and method perform cross-modal information retrieval, by generating a graph representing the set of media objects. Each node of the graph corresponds to a media object and is labeled with a set of features corresponding to a text part of the respective media object. Each edge between two nodes represents a similarity between a media part of the two nodes. A first relevance score is computed for each media object of the set of media objects that corresponds to a text-based score. A second relevance score is computed for each media object by inputting the graph into a graph neural network. The first relevance score and the second relevance score are combined to obtain a final ranking score for each media object.

A computer implemented method that includes receiving first user usage data and second user usage data indicative of user usage during consumption of a first content and of a second content, respectively. The method includes determining a first user usage pattern for the first content and a second user usage pattern for the second content based on the first user usage data and on the second user usage data, respectively. The method determines potential content overlap between the first content and the second content based on a comparison of the first usage pattern and the second usage pattern and performs an optimization operation based on the potential content overlap.

A process mining system performs process mining using visual logs generated from video streams of worker devices. Specifically, for a given worker device, the process mining system obtains a series of images capturing a screen of a worker device while the worker device processes one or more tasks related to an operation process. The process mining system determines activity labels for a plurality of images. An activity label for an image may indicate an activity performed on the worker device when the image was captured. The activity label is determined by extracting information from pixels of the image and inferring the activity of the worker device from the extracted information. The process mining system generates event logs from the visual logs of worker devices and uses the event logs for process mining.