Salesforce Com, Inc.

Salesforce, Inc.

Zuye Zheng

Thomas J Tobin

Arunkumaran Varadharajan

Vladislav Eroshin

Jeremiah Lu

James Diefenderfer

Patrick Louie

James Iry

Fiaz Hossain

Suyog Anil Deshpande

Amruta Moktali

Nagraj Kulkarni

Khushboo Shah

Vaibhav Garg

William John Gallagher

Sandeep Rawat

Niranjan Yadavali

Nadeem Marfani Aboobaker

Ravishankar Arivazhagan

Ryoji Osawa

Siddhi Mehta

Daphne Kao

Pragya Anand

Behzad Farhang Richey

Katherine Jeanne Rushton

Scott Musser

Srividhya Agandeswaran

Reena Mathew

Luis Maya

Nagraj Kulkami

Andrew Son Nhat Tran

Irene Fung

Julian Navaratnam

Martin Desrosiers

Saloni Shah

Shaila Shree

Yaoyao Lin

Anirudh Sridhar

Deepinder Badesha

Thushara Paul

Si Wan Kim

Evan Tsao

Timothy Noonan

Sumitkumar Kukkar

Andrew Ouyang

Meghana Ashok

Asra Yousuf

Raghav Ravichandran

Aditya Kochhar

Sameer Sethi

Nadeem Aboobaker

Venkata Vepa

Praveen Pendey

Sreevatson Mahabalipuram Chakravarthy

Danny Lai

Methods, systems, and devices supporting primitive-based query generation are described. A database system may receive a natural language query associated with a subset of data sets stored at the database system, determine a set of tokens included in the natural language query, and label one or more tokens of the set of tokens with one or more primitives from a set of supported primitives associated with the subset of data sets. The database system may determine a set of higher order concepts corresponding to query operations, query conditions, or both based on the one or more labeled tokens. The database system may train a neural network to learn a grammatical structure of the natural language query and generate a database query for querying the database system based on the one or more labeled tokens, the set of higher order concepts, and the neural network.

Primitive-based query generation from natural language queries

Methods, systems, and devices for generating a query using training observations are described. According to the techniques described herein, a device (e.g., an application server) may receive a set of queries including a set of fields in a tenant-specific dataset associated with the query. The device may generate a set of training observations for the queries based on the set of fields. The device then trains a first machine learning model to determine grouping hierarchies and a second machine learning model to determine aggregation predictions. The device then builds a combined machine learning model based on the determined grouping hierarchies and the aggregation predictions. According to techniques described herein, the device uses the determined grouping hierarchies and the aggregation predictions to rank a set of suggested queries determined in response to an input query and selects a suggested query for display based on the ranking.

Generating a query using training observations

Techniques described herein may support a learned ontology or meaning for user, organization, or customer specific data. According to the techniques described herein, a set of datasets corresponding to an entity may be processed to generate a master dataset including rows that include at least a field name and a value corresponding to the field. The master dataset is processed to generate a corpus of text strings that is input into a word embedding function which generates a set of vectors based on the corpus. Because the configuration of the text string positions values by field names and field values, implicit relationships and contexts are identified within the data using the word embedding function.

Learned data ontology using word embeddings from multiple datasets

Described herein are systems, apparatus, methods and computer program products configured for componentized dashboards for data visualization. In certain embodiments, a component may be configured to be integrated within a dashboard. The component may receive data from one or more user database and provide one or more representations of the data. The component may be integrated within the dashboard as a separate component. That is, the component may provide the plurality of representations independent of the dashboard. The component may be maintained independent of the dashboard and, thus, may be updated independent of the dashboard.

Componentized dashboards

Methods, systems, and devices supporting querying disparate data sources are described. Querying disparate data sources may include receiving an input for data stored at a first data source from a plurality of data sources, selecting a first data connector from a plurality of data connectors, wherein the first data connector corresponds to the first data source, and identifying a first query language corresponding to the first data source from a plurality of query languages. Querying the disparate data sources may further include generating a converted query based at least in part on the first query language and retrieving the data from the first data source using the first data connector based at least in part on the converted query.

Query conversion for querying disparate data sources

Systems, methods, and computer-readable media for implementing a modulate runtime environment (MRTE) are described. The MRTE includes multiple layers that communicate with one another using messages that are routed by connectors. A computer system may implement an MTRE that includes a platform-specific visualization layer and a data layer, which may include a headless execution engine. The data layer may be implemented by multiple types of computing platforms, and the platform-specific visualization layer may be replaced for different computing platforms. Other embodiments may be described and/or claimed.

Modular runtime environment

A database system may include a data storage configured to store one or more data sets and a processor coupled to the data storage. The processor may receive a query for first data included in the one or more data sets and access the data storage to obtain the first data based on the query. The processor may transmit the first data to a user system. The processor may receive an indication of a filter request to filter the first data based on a characteristic. The processor may identify linked data within second data based on the characteristic, the linked data linked to a portion of the first data associated with the filter request. The processor may access the data storage to obtain the linked data and transmit the linked data to the user system, to enable updating of a data presentation to display the linked data.

Filter of data presentations via user-generated links

Methods, systems, apparatus, and machine-readable media facilitate a system for data extraction using object relationship templates. In an enterprise cloud computing environment, a system for data extraction using object relationship templates is implemented to dynamically generate data extraction flows from one or more enterprise data sources quickly and accurately in response to changing business needs. Object relationship templates representing all of a portion of a data extraction flow are created and stored for reuse either alone or in combination with other data extraction flows and other templates to create new data extraction flows. Corresponding methods, systems, apparatus, and machine-readable media for data extraction using object relationship templates can be implemented in servers supporting the enterprise cloud computing environment.

Data extraction using object relationship templates

Dataflow optimization is described for extractions from a data depository. In one example an object-relationship graph of a data extraction definition is traversed in a first pass. The object-relationship graph has a node for each object. The steps from each node of the graph in the first pass is determined. It is determined if any of the determined steps are repeated. A single instance of each repeated step is placed before other steps in a query plan. Data is then extracted in a second pass from the object-oriented database system according to the object-relationship graph by performing the repeated steps first and then reusing the repeated steps in other determined steps.

Dataflow optimization for extractions from a data repository

A user system includes a user interface, a processor, and one or more stored sequences of instructions. The one or more stored sequences of instructions, when executed by the processor, cause the processor to display a script field within an editor dashboard, of a runtime environment, displayed on the user interface, the editor dashboard configured to define an interactive dashboard of the runtime environment, identify a script entry input into the script field, parse the script entry to identify an operation to be performed within the interactive dashboard in response to a trigger event, and associate the operation with the interactive dashboard, so that the operation will be performed within the interactive dashboard in response to the trigger event based on the association.

Dashboard execution engine

Navigation of a data extraction graph is described. The data extraction graph may be a graph representation of data and metadata in an object-oriented database. In one example, a data extraction definition of metadata from a database system is presented in a graph format. The graph format is transformed into a linear object tree, and/or an object list. Navigation commands are received from a user to navigate the transformed graph format. Selections of objects are received from the user to select objects in the transformed graph format. A summary view of the selected objects is then provided in the transformed graph format.

Navigation of a data extraction graph of data and metadata from a data repository

A graph representation is described that may be used for data extraction for a data repository. In one example, the graph representation defines an extraction dataset from an object. A selection from a user for a root node is received. Additional are presented for selection by the user based on fields and properties of the selected root node. The root node and selected additional objects are presented as a data graph. The selected objects are joined and presented in the data graph. Finally a dataset is extracted from the object-oriented database based on the data graph.

Graph representation of data extraction for use with a data repository

In accordance with embodiments, there are provided mechanisms and methods for progressive rendering of report results. These mechanisms and methods for progressive rendering of report results can enable embodiments to render report results in portions as they are received. The ability of embodiments to render report results in portions as they are received can enable report results to be progressively rendered such that delay is avoided which would otherwise occur when rendering only in response to receipt of an entirety of the report results.

System, method and computer program product for progressive rendering of report results

In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamic cross-block joining of reports in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving, at a server computing device, a joining request to merge a plurality of reports into a joined report, each of the plurality of reports having data including customer relationship management (CRM) data, where the request is placed at a client computing device, and merging the plurality of report into the joined report. The joined report comprises a single report representing the merged plurality of reports. The method may further include facilitating access, via a user interface at the client computing device, to the plurality of reports represented as the joined report.

Facilitating dynamic cross-block joining of reports in an on-demand services environment

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.

Method and system for identifying errors in code

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San Francisco, CA, United States of America

Zuye Zheng