A method for computational storage may include storing, at a storage device, two or more portions of data, wherein a first one of the two or more portions of data comprises a first fragment of a record and a second one of the two or more portions of data comprises a second fragment of the record, and performing, by the storage device, an operation on the first and second fragments of the record. The method may further include performing, by the storage node, a second operation on first and second fragments of a second record. The operation may include a data selection operation, and the method may further include sending a result of the data selection operation to a server. The method may further include sending a result of a first data selection operation to a server.

A method for computational storage may include storing, at a storage device, a first portion of data, wherein the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, and appending the second fragment of the record to the first portion of data. The method may further include performing, at the storage device, an operation on the first and second fragments of the record. The method may further include determining that the first portion of data may include a first fragment of a record, and a second portion of data may include a second fragment of the record, wherein appending the second fragment of the record to the first portion of data may include appending, based on the determining, the second fragment of the record to the first portion of data.

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.

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 real-time dynamic API traffic shaping and infrastructure resource protection in a multiclient network environment is provided. A traffic rules engine (TRE) applies traffic shaping only to customers that are utilizing “more than their fair share” of the currently available bandwidth without allowing them to negatively impact the user experience of other users. The present invention takes current API traffic into consideration, allowing one or a few high volume users to utilize most of all available bandwidth as long as other users do not need that bandwidth. This includes dynamically measuring and adjusting which users had traffic shaping applied to them based on the overall traffic during any given second. The solution of the present invention avoids any slowdown of customer API requests unless the maximum allowable TPS limit is near to being reached.

A real-time dynamic API traffic shaping and infrastructure resource protection in a multiclient network environment is provided. A traffic rules engine (TRE) applies traffic shaping only to customers that are utilizing “more than their fair share” of the currently available bandwidth without allowing them to negatively impact the user experience of other users. The present invention takes current API traffic into consideration, allowing one or a few high volume users to utilize most of all available bandwidth as long as other users do not need that bandwidth. This includes dynamically measuring and adjusting which users had traffic shaping applied to them based on the overall traffic during any given second. The solution of the present invention avoids any slowdown of customer API requests unless the maximum allowable TPS limit is near to being reached.

Disclosed in some examples are methods, systems, and machine readable mediums which utilize volume to improve the ordering of search results for various information retrieval systems. This improves relevance as volume is a proxy for interest. As volume changes over time, the relevance of a particular result to a particular search query will increase or decrease over time.

Disclosed in some examples are methods, systems, and machine readable mediums which utilize volume to improve the ordering of search results for various information retrieval systems. This improves relevance as volume is a proxy for interest. As volume changes over time, the relevance of a particular result to a particular search query will increase or decrease over time.

Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Embodiments are directed to managing data visualizations. Candidate data fields from a data source may be determined based on a search expression. The candidate data fields may be displayed in the model panel. A working data model may be generated based on a portion of the candidate data fields such that the portion of the candidate data fields may be included in the working data model. Visualizations may be determined based on recommendation models and the working data model such that a portion of the visualizations may be determined based on shared data fields that may be included in the working data model and the visualizations. A working visualization may be determined based on a visualization listed in the display panel and the working data model such that data fields included in the working data model may be associated with the working visualization.