The presently disclosed subject matter includes a computerized method and system that provide the ability to train and execute a unique machine learning (ML) model specifically configured to enhance classifier (e.g., RegEx) output by identifying and removing false positive results from the classifiers output. Classifier output, comprising a collection of data-subsets (e.g., columns in a relational database) of one or more structured or semi-structured data sources (e.g., tables of a relational database), are transformed to be represented by a plurality of numerical vectors. The numerical vectors are used during a training phase (as well as the execution phase) for training a machine learning model to enhance the classifier output and reduce false positives.

Provided are systems and methods for automatic search query refinement. An example method commences with identifying a plurality of electronic sources of data content of an entity stored at different network-accessible locations. The content may be dynamically assigned fields based on criteria specified by the entity. Thereupon, a unified search interface may be provided to authorized users to search the content. A search query subsequently received from a user may be parsed. The method continues with determining, upon the parsing and based on predetermined rules, triggers associated with the search query. In some embodiments, the triggers include search triggers to be used for searching content, filter triggers to be applied for filtering search results, and structural triggers to be used for ranking the search results. The method further includes searching the content based on the triggers to retrieve the search results and providing the search results to the user.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

An information processing method includes accepting a query including first information for specifying a target period and a condition for extracting a processing result, selecting, based on the query, a processing unit that has a processing result updated in the target period from among a plurality of the processing units included in the stream processing infrastructure by referring to correspondence information representing, in association with each other, second information indicating each of the plurality of the processing units and third information indicating a timing of a last update of a processing result by the processing unit, and transmitting, to the selected processing unit, a request to execute a predetermined process on the processing result that has updated in the target period and satisfies the condition.

A data movement system is provided for moving data using a data-to-file-to-data movement path. The data movement system includes a source database, a target database, a configuration database, and a data movement server. The data movement is in communication with the source database, the target database, and the configuration database. The processor is configured to receive a configuration record including source details and target details. The processor is also configured to define an extraction query based on the source details and to apply the extraction query to the source database to obtain an extraction load. The processor is further configured to generate a load file based on the extraction load, to define a load script based on the target details, and to apply the load script to the load file to obtain a load query. The processor is also configured to update the target database with the load query.

A data movement system is provided for moving data using a data-to-file-to-data movement path. The data movement system includes a source database, a target database, a configuration database, and a data movement server. The data movement is in communication with the source database, the target database, and the configuration database. The processor is configured to receive a configuration record including source details and target details. The processor is also configured to define an extraction query based on the source details and to apply the extraction query to the source database to obtain an extraction load. The processor is further configured to generate a load file based on the extraction load, to define a load script based on the target details, and to apply the load script to the load file to obtain a load query. The processor is also configured to update the target database with the load query.

Systems and methods for throttling requests submitted to a database are designed to maximize the rate at which information can be obtained from the database. In the throttling methods, the time required for the database to perform a certain operation is monitored. If the time required to perform the operation exceeds a threshold time period, a request limit is imposed on the database, the request limit limiting the number of data read and/or write requests that can be submitted to the database per unit of time.