Abstract: Systems and methods for detecting human impersonations on the internet, comprising receiving, by an ingestion layer of a detection system, a plurality of web data; filtering, by a processing engine of the detection system, the plurality of web data to identify relevant websites; extracting, via a scoring engine of the detection system, categories of data of a website of the relevant websites; assigning a score, via the scoring engine, for each category of the categories of data; and determining a risk score for the website, via the scoring engine, based on the score for each category.

Abstract: A method and an apparatus that includes performing a material alteration detection process: sampling the document images based at least in part on account information indicated on the document images; performing image pre-processing on the sampled document images; determining a document type for each image of the sampled document images; for handwritten documents, analyzing the document image using a machine learning (ML) algorithm trained to detect material alterations on handwritten documents; for printed documents, analyzing the document image using a ML algorithm trained to detect material alterations on printed documents; and outputting a fraud probability representation for each analyzed document image; and a signature forgery detection process: obtaining past signatures corresponding to the document images; performing signature image pre-processing; authenticating each signature using the past signatures and a ML algorithm trained to match signatures; outputting a similarity measure; and adjusting the

Abstract: Techniques are disclosed for automatically pre-processing data to generate a single view of the data that is suitable for machine learning and data analytics operations. Multiple data sets are joined together using one or more primary keys if raw data in the data sets have a same frequency. On the other hand, if raw data in the data sets do not have the same frequency, then for raw data in data sets having a different frequency than data in a user-specified base data set, the raw data is normalized and resampled. The normalized and resampled data in the data sets is further aggregated based on timestamps associated with the base data set, and the data sets are then joined to the base data set using one or more primary keys. The joined data sets can be stored and used to train machine learning models and/or for data analytics operations.

Abstract: Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by monitoring usage of a data retrieval point with respect to a defined unit of work. A multilayer feed-forward neural network is used to predict, as a function of training sets composed of historical data generated by the monitored usage of the data retrieval point, a future value of a data size of results from an eager fetch setting for the data retrieval point. The eager fetch is automatically revised to a lazy fetch setting in response to determining that the future data size value of the eager fetch setting results is larger than a permissible memory resource threshold.

Abstract: Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by monitoring usage of a data retrieval point with respect to a defined unit of work. A multilayer feed-forward neural network is used to predict, as a function of training sets composed of historical data generated by the monitored usage of the data retrieval point, a future value of a data size of results from an eager fetch setting for the data retrieval point. The eager fetch is automatically revised to a lazy fetch setting in response to determining that the future data size value of the eager fetch setting results is larger than a permissible memory resource threshold.

Abstract: Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a data size of a defined retrieval pattern is larger than a permissible memory resource threshold.

Abstract: Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a data size of a defined retrieval pattern is larger than a permissible memory resource threshold.

Abstract: Embodiments of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a datasize of a defined retrieval pattern is larger than a permissible memory resource threshold.

Abstract: Virtual file system virtual nodes are grouped in subpools in response to identified resource components and managed as a function of their subpool groupings. Virtual nodes are decomposed into individual components linked to each other within each node. The components that have repetitive accesses by applications within the virtual file system and their respective frequencies of repetitive access are identified. Modules of linked occurrences of the repetitive components within each of the plurality of virtual nodes are formed, and subsets of the virtual nodes sharing common modules are grouped into subpools. Accordingly, in response to an application of the virtual file system requesting a service that is satisfied by a subpool common module, selection of a virtual node for reinitiating for reuse by the application is restricted to virtual nodes within the subpool associated with the common module.

Abstract: Virtual file system virtual nodes are grouped in subpools in response to identified resource components and managed as a function of their subpool groupings. Virtual nodes are decomposed into individual components linked to each other within each node. The components that have repetitive accesses by applications within the virtual file system and their respective frequencies of repetitive access are identified. Modules of linked occurrences of the repetitive components within each of the plurality of virtual nodes are formed, and subsets of the virtual nodes sharing common modules are grouped into subpools. Accordingly, in response to an application of the virtual file system requesting a service that is satisfied by a subpool common module, selection of a virtual node for reinitiating for reuse by the application is restricted to virtual nodes within the subpool associated with the common module.

Abstract: Embodiments of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a datasize of a defined retrieval pattern is larger than a permissible memory resource threshold.

Abstract: One or more attributes of a user are determined. A given advertisement for an product or service, such as a product or a service, is selected from a number of advertisements for the product or service, based on the attributes of the user. The given advertisement for the product or service that is selected is then electronically displayed, for viewing by the user.