Methods and systems for cross cluster replication are provided. Exemplary methods include: periodically requesting by a follower cluster history from a leader cluster, the history including at least one operation and sequence number pair, the operation having changed data in a primary shard of the leader cluster; receiving history and a first global checkpoint from the leader cluster; when a difference between the first global checkpoint and a second global checkpoint exceeds a user-defined value, concurrently making multiple additional requests for history from the leader cluster; and when a difference between the first global checkpoint and the second global checkpoint is less than a user-defined value, executing the at least one operation, the at least one operation changing data in a primary shard of the follower cluster, such that an index of the follower cluster replicates an index of the leader cluster.

Systems and methods for handling database transactions within a database session. A first client request to update a first data piece of a database session is received. A first response to the first client request indicates an update of the first data piece in accordance with the first request and publishes the update to enable further processing of the updated first data piece. An indication indicates that the updated first data piece is to be further updated and/or a second data piece of the database session is to be updated. A second client request is received to update a third data piece of the database session and a second response to the second client request indicates an update of the third data piece in accordance with the second request and an update of the updated first data piece in accordance with the indication.

Briefly stated, the invention is directed to retrieving a semantically matched knowledge structure. A question and answer pair is received, wherein the answer is received from a query of a search engine. A question is constraint-matched with the answer based on maximizing a plurality of constraints, wherein at least one of the plurality of the constraints is a similarity score between question and answer, wherein the constraint matching generates a matched sequence. For one or more answer sequences, a subsequence is found that are not parsed as answer slots. Query results are obtained from another search engine based on a combination of the answer or question, and the non-answer subsequence. And a KB based is refined on the query results and the constraint matching and based on a neural network training, for a further subsequent semantic matching, wherein the KB includes a dense semantic vector indication of concepts.

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.

In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

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.

Systems and methods are provided for updating data in a computer network. An exemplary method includes: receiving a first data from one or more data servers; determining a second data for which a first update information is used to update at least one element of the second data; generating a second update information associated with the second data; generating a third update information by executing an operation that compares the first update information and the second update information; generating a third data by applying the third update information to the second data; allowing a user to select via a display device which of the different update elements to update the second data; generating a fourth update information by determining the selected different update elements to update the second data; and updating the second data using the fourth update information.

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.

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.

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.