Salesforce.com

About: Salesforce.com is a based out in . It is known for research contribution in the topics: User interface & Object (computer science). The organization has 2418 authors who have published 2775 publications receiving 63956 citations.

Method and system for document integration

Abstract: A method for integrating a document from a first document repository to another document repository in a cloud computing environment is disclosed. The method embodiment includes receiving by a server a configuration setup file including information identifying a source document repository, a destination document repository located in a cloud computing environment, and at least one web portal. When an indication to upload a document from the source document repository to the destination document repository is received by the server, the server is configured to retrieve automatically the document from the source document repository, to convert automatically the document from a first format to a second format, and to transmit the converted document to the destination document repository, whereby the converted document is accessed via the at least one web portal.

Method and System for Identifying Errors in Code

Abstract: A method for identifying errors in code is provided. The method may include rebuilding object dependencies from a heap dump, calculating memory usage of each object, identifying top consumers of memory by object class, analyzing how much memory each class consumes with respect to how much other classes consume, building a corpus of data that may be used in a progressive machine learning algorithm, and identifying suspect classes. Additionally, the suspect classes and the memory usage statistics of the suspect classes may then be used as an identifying signature of the associated out of memory error. The identifying signature of the associated out of memory error may then be used to compare with the signatures of other out of memory occurrences for identifying duplicate error occurrences.

Methods and systems for regulating database activity

Abstract: Methods and systems are provided for regulating access to a database by a server. One exemplary method involves the server receiving one or more requests for an activity with respect to the database and determining an attempted usage for the activity over a monitoring period by a source of the requests. When the attempted usage exceeds an allowed usage of the activity for the monitoring period, the server provides a human verification test to the source and thereafter initiates the activity with respect to the database in response to receiving a satisfactory human verification response to the human verification test from the source.

Neural network based translation of natural language queries to database queries

Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.

Sequence-to-sequence prediction using a neural network model

Abstract: A method for sequence-to-sequence prediction using a neural network model includes generating an encoded representation based on an input sequence using an encoder of the neural network model and predicting an output sequence based on the encoded representation using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. At least one of the encoder or the decoder includes a branched attention layer. Each branch of the branched attention layer includes an interdependent scaling node configured to scale an intermediate representation of the branch by a learned scaling parameter. The learned scaling parameter depends on one or more other learned scaling parameters of one or more other interdependent scaling nodes of one or more other branches of the branched attention layer.