Provided are systems and methods for classifying and tagging records in a record management system using information extracted and analyzed from specific areas or regions of records. A specific area or region of the record may be scanned, and the content disposed therein processed against a plurality of classification templates. Based on proximity to the classification templates, the record may be assigned one or more tags corresponding to the classification templates.

A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

A cloud computing system, which works in cooperation with a plurality of relay devices, is configured to receive measurement data transmitted from each of the relay devices arranged in respective bases and perform accumulation processing in a hierarchical structure of a logical tree form in a measurement database, and perform aggregation analysis processing on the measurement data subjected to the accumulation processing in the respective bases and for each integration target between the bases. The aggregation analysis processing is performed on the measurement data for the each integration target between the bases by recognizing a relationship between the bases under the same starting point on the basis of a measurement unit of a measurement value or a type of a measurement source as for the value in the source accumulated in the database as an ending point of the hierarchical structure.

Herein are techniques for dynamic aggregation of results of a database request, including concurrent grouping of result items in memory based on quasi-dense keys. Each of many computational threads concurrently performs as follows. A hash code is calculated that represents a particular natural grouping key (NGK) for an aggregate result of a database request. Based on the hash code, the thread detects that a set of distinct NGKs that are already stored in the aggregate result does not contain the particular NGK. A distinct dense grouping key for the particular NGK is statefully generated. The dense grouping key is bound to the particular NGK. Based on said binding, the particular NGK is added to the set of distinct NGKs in the aggregate result.

Techniques are described for generating HLSD for a textual format source code, which, when rendered, causes a display of visual content. The rendering of the source code generates a tree hierarchy of visual source elements, which logically is possible to map to any graph tree. In an embodiment, visual source elements of the source code are classified to higher-level semantic data (HLSD) labels based on their property(s) and/or the property(s) of neighbor visual source element(s) in the tree hierarchy (context). The HLSD labels indicate the type of HLSD widget mapped to the visual source elements. Techniques further include determining features and a layout arrangement for HLSD widgets and generating a template thereof for the visual content.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

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.

An example operation may include one or more of acquiring, by a promotion processor node, consumer exploration of a product data from a blockchain, determining, by the promotion processor node, features of the product, receiving, by the promotion processor node, a promotion plan from at least one product retailer node, and executing a smart contract to generate a plurality of promotion tokens based on the features of the product and the promotion plan.