Salesforce Com, Inc.

Salesforce, Inc.

Praveen Innamuri

Scott Walter Bishel

Zhidong Ke

Kevin Terusaki

Shardul Vikram

Shan-Cheng Ho

Utsavi Benani

Noah William Burbank

Alexis Roos

Ankush Goyal

Gabriel Starr Krupa

Narek Asadorian

William Christopher Fama Roller

Alex Michael Noe

Joseph Gerald Keller

Jon Tomas Gretarsson

Rohith Ramprasad

Nathaniel Richard Price

Sudarshan Vijay Sarolkar

Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.

Calculating relationship strength using an activity-based distributed graph

Described is a method and system for verifying the end-to-end distribution of messages within an on-demand services platform. To provide the ability to monitor and trace such messages, the system may include a specialized and queryable datastore (or database) that allows the system to track the distribution and acknowledgement of messages throughout the services platform. Accordingly, a specialized verification process may be initiated to query the database to verify the receipt of a message has been acknowledged by various components within the system. In addition, the verification process may automatically retry the distribution the messages to ensure particular downstream components have received the message. Accordingly, the system alleviates the need to manual re-crawl various message source to ensure the end-to-end distribution of such messages.

Monitoring and verification system for end-to-end distribution of messages

Described is a system and method for compacting data into customized (e.g. optimal) file sizes for processing by computing resources. The mechanism may leverage various computing resources such as a cluster computing frameworks combined with a stream processing platform to efficiently process the activity data. For example, activity data of an organization may be processed by a set of jobs (or sub-jobs) as part of a data stream by a set of distributed computing resources. In order to efficiently process such data, the mechanism may compact the data into customized (e.g. optimal) file sizes. For example, the customized file sizes may provide an optimal (or near optimal) amount of data to be processed by each job, for example, to improve performance.

Performing customized data compaction for efficient parallel data processing amongst a set of computing resources

A system may utilize a combination of real-time and polling connectors for data crawling. A real-time connector may use a collector to write data associated with a new event from a database to a channel and record external identifiers (IDs) associated the data. A polling connector may use a verifier to compare the recorded IDs with IDs associated with a batch of data, and may write any data of the batch of data to the channel that has not been written previously written by the collector. The system may include an onboarder for reading data previously stored in the database.

Synergizing real-time and polling connectors for data ingestion

Growing community of inventors

Sunnyvale, CA, United States of America

Praveen Innamuri