One or more computing devices, systems, and/or methods are provided. In an example, purchase data associated with users may be determined. The purchase data may be indicative of purchases by users from entities. The purchase data may be analyzed to determine purchase metrics associated with the users. The purchase metrics may be analyzed to determine sets of groups of users associated with the entities. One or more groups of users, of the sets of groups of users, that include the user may be determined. Content may be selected for presentation via a first device associated with the first user based upon the one or more groups of users.

According to one implementation, a system includes a computing platform having processing hardware, a system memory storing a software code; and a machine learning model based classifier. The processing hardware is configured to execute the software code to receive tagging quality assurance (QA) data including multiple terms applied as tags and corrections to those tags, to identify, using the tagging QA data, a first problematic term, and to classify, using the machine learning model based classifier, the first problematic term as one of confusing or flawed. The processing hardware is further configured to execute the software code to obtain, when the first problematic term is classified as confusing, a comparative sample for clarifying use of the first problematic term, and to obtain, when the first problematic term is classified as flawed, modification data for editing a predetermined annotation taxonomy including the first problematic term.

The present disclosure involves computer-implemented method, medium, and system for automatically correlating semantically connected data and metadata. One example method includes identifying a document that is to be analyzed using a semantics based mapping (SBM) infrastructure. A matching process is performed for the identified document using the SBM infrastructure, where the matching process identifies a plurality of matching terms within the document, the plurality of matching terms are assigned to a plurality of semantics identifiers (IDs), and each semantics ID corresponds to one or more terms in the plurality of matching terms. Each of the plurality of matching terms is replaced with a respective term ID to generate an updated document. A request to search for a target term in the document is received. The target term is translated to a target term ID based on the SBM infrastructure. The updated document is searched for one or more matching terms.

Described is an improved approach to implement fast detection of node death. Instead of just relying on multiple heart beats to fail in order to determine whether a node is dead, the present approach performs an on demand validation using RDMA to determine whether the node is reachable, where the approach of using RDMA is significantly faster than the heartbeat approach.

Embodiments of the present invention provide systems and methods for automated and intelligent analysis of information. The system receives interaction data, interaction metadata, and external information in order to identify parties of interactions, subjects of interactions, and infer relationships between parties and subjects based on the content, context, frequency, and amount of available interaction data. Weighted score scores are generated and used to rank the inferred relationships and determined relevance between parties and subjects. This data may be stored in a graphical database and later used to response to user data queries to facilitate collaboration.

Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.

A system includes one or more processors to receive a representation of a document from a client computing device, the document comprising one of a scanned document, a faxed document, and an electronic document, determine a document type of the document based at least on the representation of the document, index the document using a classification and index processing engine based on the document type, the document type comprising at least one of a plurality of document types used by an electronic health record (EHR) system, extract index data from the document based on the document type using the classification and index processing engine, and match the document with a patient from a database of the EHR system using the index data when the classification and index processing engine successfully indexes the document and extracts index data from the document.

Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.

There is provided a method, comprising: extracting user feature profiles for users of a social network, each feature profile being structured and including user features extracted from unstructured user generated text, indications of participation in groups, and structured user profiles, training a clustering-component of a model to cluster the feature profiles, training a matching-component of the model to compute a distance score indicative of statistical similarity between a feature profile of a target user and features profiles of other users of a same cluster, using a training dataset of pairs of feature profiles extracted from common clusters, each pair assigned a distance score label, providing the model for: identifying a certain cluster of a certain user, and computing distance scores between the feature profile of the certain user and other feature profiles of other users of the certain cluster for selecting one user for establishment of a communication session.

Systems and methods for identifying one or more master tables of a datalake are described. A system may obtain a plurality of computer readable tables of a datalake (with each computer readable table including one or more features). The system may also group the plurality of computer readable tables into a plurality of groups based on a number of features of each computer readable table of the plurality of computer readable tables. The system may further generate, for each of one or more groups of the plurality of groups, one or more neighborhoods based on a similarity of features between computer readable tables of the group. The system may also identify, for each neighborhood, one or more master tables from the one or more computer readable tables of the group. The system may further provide an indication of one or more master tables identified in the datalake.