Abstract: Embodiments of this specification disclose large-scale alarm deployment computer-implemented methods, systems, apparatuses, devices, and computer-readable media. In an example, whether an indicator in a large-scale indicator set is a non-long-tail indicator or a long-tail indicator is determined. For the non-long-tail indicator, a first task is scheduled for traffic data of the non-long-tail indicator by using a first time interval, and alarm calculation is performed by executing the first task. For the long-tail indicator, aggregation processing is performed on traffic data of the long-tail indicator, a second task is scheduled for correspondingly obtained aggregated data by using a second time interval longer than the first time interval, and alarm calculation is performed by executing the second task.

Abstract: Relation network data query includes receiving a query request for an edge using a first node as a starting node, where the edge satisfies a specified filter condition comprising that the edge has a first edge data item and a corresponding first data value. Multiple index blocks associated with the first node are obtained from a non-volatile storage storing a relation network. A first index block corresponding to the first edge data item is determined from the multiple index blocks based on a correspondence between an index block and an edge data item, where the first index block comprises multiple data values of the first edge data item and location information of edges having the multiple data values and using the first node as starting nodes. Location information of an edge having the corresponding first data value is determined from the first index block and the edge obtained.

Abstract: Embodiments of this specification provide methods and apparatuses for generating a graph node relationship representation and methods and apparatuses for predicting a graph node service relationship. In an implementation, a method includes: determining node representations of a first graph node and a second graph node based on performing node representation propagation and node representation aggregation starting from the first graph node and the second graph node, and generating a node relationship representation between the first graph node and the second graph node based on the node representations.

Abstract: To insert data into a graph database, an insert statement for a target graph database is received, where the insert statement includes at least an insert keyword and several element definition lists corresponding to several graphic elements including nodes and/or edges. The several element definition lists describe respective element identifiers of the several graphic elements and object types of objects represented by the several graphic elements, and each element definition list includes each attribute value of each pre-described attribute of a corresponding graphic element. The insert statement is executed, including forming each data row corresponding to each graphic element based on each attribute value in each element definition list and determining a primary key value of a corresponding data row based on an element identifier and an object type of each graphic element. Each data row is inserted into the target graph database based on the primary key value.

Abstract: Embodiments of this specification disclose graph data partition computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems. A computer-implemented method includes partitioning vertices in graph data into a plurality of dataset. Edges in the graph data are partitioned into datasets that include target vertices of the edges, where the datasets are used by nodes in a distributed cluster to perform graph computation, and where computational loads of the plurality of datasets are similar Implementations of this specification can achieve load balancing between nodes in the distributed cluster and can reduce communication overhead.

Abstract: Implementations of this specification provide data processing methods, apparatuses, and systems. In one implementation, a method includes: obtaining a statement for a data operation, wherein the statement comprises a command field, parsing the statement by a compiler, wherein the compiler is configured to support at least one syntax and provide a field translation relation among different syntaxes, in response to determining that the command field is absent from the at least one syntax supported by the complier, determining a target field in the at least one syntax that corresponds to the command field, and performing the data operation based on the target field.

Abstract: This specification relates to the field of knowledge graphs, and in particular, to knowledge reuse methods, apparatuses, computer-readable media, and systems. In an example computer-implemented method, a child entity is defined based on a parent entity, where the parent entity is selected from entities in a knowledge graph. A knowledge inheritance method is performed to inherit a portion of instance data of the parent entity. Instance data of the child entity are determined. First graph increment information corresponding to the instance data of the child entity is stored.

Abstract: Examples in this application disclose data recording methods, media, and systems. One example method includes receiving an access request for target data from a data user, obtaining the target data from a data owner, generating a blockchain transaction based on the target data, transmitting the blockchain transaction to a blockchain node, where the blockchain node performs a transaction consensus with remaining blockchain nodes, stores the blockchain transaction in the blockchain upon the transaction consensus is reached, and generates index information for the blockchain transaction when the data user is determined to have an access permission to the target data, receiving the index information from the blockchain node, where the index information is used to generate a request transaction to obtain the blockchain transaction or the target data from the blockchain, and sending the target data or the index information to the data user.

Abstract: This specification provide example Java bytecode injection methods, apparatuses and machine-readable storage media, which can be applied to a Javaagent framework deployed in a Java virtual machine. In an example, a to-be-injected Java bytecode submitted by a target service module in at least one service module is obtained, and a target implementation class corresponding to the to-be-injected Java bytecode is determined. A target injection point type corresponding to the target implementation class is determined, and the target injection point type is respectively matched with mapping relationships maintained in the Java virtual machine to determine a target injection mode corresponding to the target injection point type. A globally unique class transformer is invoked so that the class transformer injects, based on the target injection mode, the Java bytecode into a class file that corresponds to the target service module and that is to be loaded by the Java virtual machine.

Abstract: Embodiments of this specification provide distributed data processing methods, apparatuses, and devices. One method includes: determining an active vertex set that currently participates in data processing in target graph data, in response to determining that an external memory of a first distributed node stores an active vertex in the active vertex set, determining, from a plurality of predetermined data processing modes, a target data processing mode that matches the active vertex set, determining, based on the target data processing mode, a to-be-updated vertex according to the association relationship with the active vertex, and sending, based on first data of the active vertex in the external memory, a first update message to a target distributed node in which the to-be-updated vertex is located.

Abstract: Computer-implemented methods, apparatus, and systems for data storage and data query are described. During data storage, the number of neighboring graph nodes in each starting graph node in directed graph graph data to be stored is determined, and a data storage mode is determined according to the number of neighboring graph nodes. When the data storage mode is not an ultra-large node data storage, node data, neighbor information, outgoing edge index feature information, and outgoing edge data of the starting graph node are stored in the same data fragment. When the data storage mode is an ultra-large node data storage, node data, neighbor information, outgoing edge index feature range information, and outgoing edge data are stored in a starting graph node data fragment, and the outgoing edge data and outgoing edge data storage address information of the starting graph node are stored in at least two outgoing edge data fragments.

Abstract: This specification discloses model running methods, apparatuses, computer-readable storage media and systems. In an example method, an original model is split to obtain a basic model and a trusted execution environment (TEE) model; and data of the basic model and data of the TEE model are delivered to a terminal device, so that a rich execution environment (REE) in the terminal device runs the data of the basic model, and a TEE in the terminal device runs the data of the TEE model.

Abstract: Embodiments of this specification provide multi-party data query methods and apparatuses for data privacy protection. One implementation of the methods includes obtaining, from each of a plurality of data owners, attribute value ciphertexts of N target objects to form a ciphertext table, disordering the ciphertext table in units of rows to obtain a disordered table, sorting, in response to a query instruction of querying sorting-related data for a target attribute item in the plurality of attribute items, attribute value ciphertexts corresponding to the target attribute item in the disordered table to obtain a target sorted table, and obtaining the sorting-related data as a query result based on the target sorted table.

Abstract: Embodiments of this specification provide methods, apparatuses systems, and computer-readable media for data privacy-preserving training of a service prediction model. In an example training process, a member device performs prediction by using the service prediction model and object feature data held by the member device, and determines, by using a prediction result, update parameters used to update model parameters, where the update parameters include sub-parameters for computational layers of the service prediction model; and divides the computational layers into first-type and second-type computational layers using the sub-parameters; and performs privacy processing on sub-parameters of the first-type computational layers, and outputs processed sub-parameters. Processed sub-parameters of member devices can be aggregated into aggregated sub-parameters.

Abstract: Embodiments of this specification provide methods and apparatuses for performing model ownership verification based on an exogenous feature. An implementation of the methods includes: selecting initial samples from an initial sample set to form a selected sample set, processing sample data of the initial samples to obtain transform samples that form a transform sample set, training a meta-classifier based on a target model, an auxiliary model, and the transform sample set, inputting data associated with a suspicious model into the meta-classifier, and determining, based on an output result of the meta-classifier, whether the suspicious model is stolen from a deployment model, wherein the deployment model has feature knowledge of the exogenous feature.

Abstract: Embodiments of this specification provide methods and apparatuses for data privacy protection. An embodiment of the methods comprises receiving, by a first party from a second party, an encrypted integrated vector, determining an encrypted result vector based on the original matrix and the encrypted integrated vector, determining a data processing result based on the encrypted result vector, and sending the data processing result to the second party for the second party to obtain a multiplication calculation result of the original matrix and the n original vectors based on the data processing result.

Abstract: Computer-implemented methods, apparatuses and systems for obtaining data authorization are described. In an example method, a first computing node in a trusted computing center receives an authentication request from a first provider. Authentication information is returned to the first provider, where the authentication information comprises a first code hash of a first computing logic running in the first computing node. A channel establishment request sent by the first provider is received after determining that the first computing node passes trusted authentication and the first code hash passes correctness verification. A first trusted channel is established between the first provider and the first computing node according to the channel establishment request. A target encryption key is received through the first trusted channel, thereby authorization to perform computation on a target encrypted shard corresponding to the target encryption key based on the first computing logic is obtained.

Abstract: This specification discloses a container quantity adjustment method and apparatus for an application. One method includes: determining historical data of an application, predicting traffic distribution of the application within a predetermined duration based on the historical data by using a pre-trained traffic prediction model, predicting container quantity distribution of the application within the predetermined duration based on the traffic distribution and a predetermined target utilization of a container by using a pre-trained quantity prediction model, and adjusting a container quantity of the application at each of a plurality of predetermined moments within the predetermined duration based on the container quantity distribution.

Abstract: Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems implementing a one-touch login service are described. Information about a first IP address is obtained from a verification request sent by an application client device. A token is sent to the application client device. Information about a second IP address is obtained from a number acquisition request sent by an application server. Whether the first IP address is the same as the second IP address is determined. If the same, based on a token carried in the number acquisition request, a mobile phone number of a terminal device in which the application client device is located is obtained and the mobile phone number is sent to the application server. If not the same, sending the mobile phone number of the terminal device to the application server is refused.

Abstract: A computer-implemented system for encoding includes an encoding layer and at least one joint encoding unit. The encoding layer encodes a received first modal initial feature vector and a received second modal initial feature vector, to generate, respectively, a first modal feature vector and a second modal feature vector, joint encoded by the at least one joint encoding unit, where the at least one joint encoding unit includes an encoding module and a modal input switching module. The modal input switching module processes the first modal feature vector and the second modal feature vector, to obtain, respectively a first modal switching encoding vector and a second modal switching encoding vector. The encoding module processes the first modal switching encoding vector and the second modal switching encoding vector, to generate, respectively a first target modal fusion vector and a second target modal fusion vector.