Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and techniques for indexing and/or querying a database are described herein. Multiple, large disparate data sources may be processed to cleanse and/or combine item data and/or item metadata. Further, attributes may be extracted from the item data sources. The interactive user interfaces allow a user to select one or more attributes and/or other parameters to present visualizations based on the processed data.

A publish and subscribe architecture can be utilized to manage records, which can be used to accomplish the various functional goals. At least one template having definitions for managing production and consumption of data within an unconfigured group of computing resources is maintained. Records organized by topic collected from multiple disparate previously configured producers are utilized to initiate configuration of the unconfigured group of computing resources. Records within a topic are organized by a corresponding topic sequence. A first portion of the computing resources are configured as consumers based on the at least one template. The consumers to consume records at a pace independent of record production. A second portion of the computing resources are configured as producers based on the at least one template. The producers to produce records at a pace independent of record consumption.

The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

A data classification apparatus includes a data transformation unit that generates a feature vector by using classification target data, a classification estimation process observation unit that acquires, from a classification estimation unit that estimates classification of the classification target data and including a plurality of weak classifiers, observation information in a classification process based on the feature vector, and generates a classification estimation process feature vector based on the observation information, and an error determination unit that determines, in accordance with an input of the classification estimation process feature vector generated by the classification estimation process observation unit and a classification result output from the classification estimation unit to which the feature vector is input, whether the classification result is correct.

The present disclosure relates to database technology and in particular to dynamically updating and customizing database partitions. A computer-implemented engine is disclosed for identifying and retrieving a number of data records applicable to generate a response to a request, the engine having access to at least two partitions. Partition statistics are generated indicating correlations between the data records and, based on that partition statistics, the data records having the strongest correlation with each other are relocated to partitions so that the number of partitions which have to be queried in order to generate a response to a data request is minimized. Furthermore, the computational load caused when generating responses is more equally distributed across the partitions.

A non-transitory computer-readable storage medium storing an explanatory information output program for causing a computer to execute processing includes obtaining a contribution of each of a plurality of factors to an output result of a machine learning model in a case of inputting each of a plurality of pieces of data, each of the plurality of factors being included in each of the plurality of pieces of data; clustering the plurality of pieces of data based on the contribution of each of the plurality of factors to generate a plurality of groups of factors; and outputting explanatory information that includes a diagram representing magnitude of the contribution of each of the plurality of factors to the output result in a case of inputting data included in the group for each of the plurality of groups.

A method for graph data processing comprises obtaining graph data which includes a plurality of nodes and data corresponding to the plurality of nodes respectively; classifying the plurality of nodes into at least one category of a plurality of categories, wherein the plurality of categories are associated with a plurality of node relationship patterns; determining, from a plurality of candidate parameter value sets of a graph convolutional network (GCN) model, parameter value subsets respectively matching at least one category, wherein the plurality of candidate parameter value sets are determined by training the GCN model respectively for the plurality of node relationship patterns; and using the parameter value subsets respectively matching the at least one category to respectively perform a graph convolution operation in the GCN model on data corresponding to the nodes classified into the at least one category to obtain a processing result for the graph data.