The present disclosure discloses a task processing method and a distributed computing framework. A specific embodiment of the method includes: parsing an expression corresponding to a distributed computing task, and constructing task description information corresponding to the distributed computing task, the task description information being used to describe a corresponding relationship between an operator and a distributed dataset, and the operator acting on at least one of the distributed dataset or distributed datasets obtained by grouping the distributed dataset; determining, based on the task description information, a distributed dataset the operator acting on; and performing distributed computing on the distributed dataset the operator acting on using the operator. In the distributed computing, the acting scope and nesting relationship of the operator is described by constructing a topology.

A method includes receiving a data set including medical information of respective patients, respective types of a clinical procedure performed on the patients, and respective survival rates of the patients. A survival tree graph is generated by maximizing a cost function of differences in the survival rates between the types of the clinical treatment procedure. A type of the clinical procedure is selected for a given patient, based on the survival tree graph.

A cybersecurity appliance monitoring application traffic to a web application programming interface (API) dynamically updates tree structures for the web API using the application traffic. An API tree generator generates batches of API trees from paths indicated in the application traffic. An API tree merger/pruner updates the generated batches of API trees with various merging, pruning, compacting, and malicious detection operations on the generated batches of API trees. The cybersecurity appliance implements the updated API trees with an API agent that filters the application traffic prior to processing by the web API.

The present disclosure is related to methods, systems, and machine-readable media for determining shared nodes between snapshots using probabilistic data structures. A unique identifier can be assigned to each node of a first tree data structure corresponding to a first snapshot of a virtual computing instance (VCI). A first probabilistic data structure representing the first tree data structure can be created that includes hashes of the identifiers assigned to the nodes of the first tree data structure. A unique identifier can be assigned to each node of a second tree data structure corresponding to a second snapshot of the VCI. A second probabilistic data structure representing the second tree data structure can be created that includes hashes of the identifiers assigned to the nodes of the second tree data structure. A particular node of the second tree data structure can be determined to be shared by the first tree data structure responsive to a determination that the first probabilistic data structure includes a hash of an identifier assigned to the particular node.

Embodiments of methods, apparatuses, devices and/or systems for manipulating hierarchical sets of data are disclosed. In particular, methods, apparatus devices and or/or systems for enumerating rooted partial subtrees are disclosed.

Provided are a graph generation method and apparatus, a device, and a storage medium. The graph generation method comprises: parsing target logical information to obtain a plurality of logical elements; converting the plurality of logical elements into a syntax tree according to logic of the target logical information, where the syntax tree comprises a plurality of layers of tree nodes, and tree nodes between layers have a dependency relationship; converting the plurality of layers of tree nodes into a plurality of graph nodes; and connecting the plurality of graph nodes according to the dependency relationship to obtain a logical graph corresponding to the target logical information to allow a user to perform special effect editing based on the logic.

Techniques are described herein for enabling autonomous agents to generate conclusive answers. An example of a conclusive answer is text that addresses concerns of a user who is interacting with an autonomous agent. For example, an autonomous agent interacts with a user device, answering user utterances, for example questions or concerns. Based on the interactions, the autonomous agent determines that a conclusive answer is appropriate. The autonomous agent formulates the conclusive answer, which addresses multiple user utterances. The conclusive answer provided to the user device.

An improved data structure approach, and corresponding computational systems and methods are described to provide a technical approach that can be used for improving computational performance where a blockchain data structure is being accessed continuously or periodically for validation of recordals of one or more events that have taken place. A hybrid off-chain (or off-contract)/on-chain solution is utilized to provide a mechanism for establishing data linkages between the off-chain (or off-contract) records and on-chain data payloads.

Systems and methods are described for a data breach detection based on snapshot analytics. The described systems and methods identify a plurality of snapshots of a data structure, identify a plurality of leaf nodes of the data structure for each of the snapshots, generate a vector of data attributes for each of the leaf nodes, assign a weight to each of the vectors to produce a set of weighted vectors for each of the snapshots, compute a distance metric between each pair of the snapshots based on the corresponding sets of weighted vectors, and detect an abnormal snapshot among the plurality of snapshots based on the distance metrics.

A reference snapshot of a storage is stored. Data changes that modify the storage are received. The data changes are captured by a write filter of the storage. The received data changes are logged. The data changes occurring after an instance time of the reference snapshot are applied to the reference snapshot to generate a first incremental snapshot corresponding to a first intermediate reference restoration point. The data changes occurring after an instance time of the first incremental snapshot are applied to the first incremental snapshot to generate a second incremental snapshot corresponding to a second intermediate reference restoration point.