Salesforce Com, Inc.

Salesforce, Inc.

Business Objects Software Limited

Stephen Petschulat

Alex Gitelman

Lingtao Zhang

Alex Field

Julian Gosper

Daniel Scott Perry

Chang Lu

Twisha Mehta

Soroush Momen-pour

Brittany Zenger

Yi Zhao Liu

Magnus Byne

Henry Lo

Qing Li

Luke Kysow

Gary Gong

Matthew Gilham

Torrey Teats

Ian Forneri

Oliver Woolgar

Paul McArthur

Methods, systems, and devices supporting dynamic process orchestration are described. An orchestration server may receive a request defining a data modification process from a user device. The orchestration server may generate an execution file based on the request, and the execution file may include a set of tasks for performing the data modification process and an order for performing the set of tasks. The orchestration server may execute, for the execution file, a first set of tasks according to the order for performing the set of tasks and, in some cases, may update the execution file based on executing the first subset of tasks. For example, updating the execution file may involve modifying a second subset of tasks of the set of tasks. The orchestration server may execute, for the updated execution file, the modified second subset of tasks according to the order for performing the set of tasks.

Orchestration for data pipeline execution plans

Interactive dataflow preview

Methods, systems, and devices for data processing within a distributed data system are described. In a multi-tenant distributed data system, a provider may supply executable code for processing data using declarative processing instructions received from a tenant. For example, a tenant may provide tenant-specific processing instructions for a requested set of data. The processing instructions may indicate input information (e.g., a data structure, tenant-specific fields, etc.), transformation information (e.g., from a set of pre-defined transformations), and output information. The provider-supplied code may use the tenant-specific processing instructions to process and generate the requested set of data, where the code may be executed by multiple nodes within the system. As such, the code executed by multiple nodes may utilize the input information, transformation information, and output information from the tenant-specific processing instructions to generate the requested data and provide the data to the tenant.

Distributed data processing in multi-tenant environments

An online system manages data by determining relevance of data dimensions to users. The online system determines which data dimensions a user is likely to be interested in. If a user requests to access a data set that includes data of different dimensions, the online system analyzes the dimensions' relevance to the user before providing the data set to the user. The online system provides the data to the user by prioritizing data dimensions that are more relevant to the user. As such, the online system improves the user experience by allowing users to conveniently and quickly locate relevant data and minimizing the distraction caused by irrelevant data. The online system may create and provide a user interface to present data dimensions that are determined to be relevant.

High-dimensional data management and presentation

Described herein are methods and systems for analyzing multidimensional data that use tangential exploration of data via a third or Z-dimension to the current two-dimensional view. The tangential exploration allows higher dimensionality to be explored without causing visual clutter.

Peeking into the Z-dimensional drawer

A system includes reception of a partial query, determination of one or more previously-selected query phrases based on the partial query, determination, for each of the one or more previously-selected query phrases, of a dataset of a data source, determination, for each dataset determined for each of the one or more previously-selected query phrases, of a plurality of columns, and presentation, for each of the one or more previously-selected query phrases, of the previously-selected query phrase, the dataset determined for the previously-selected query phrase, and the columns determined for the dataset.

Answer-augmented query suggestion

Growing community of inventors

Coquitlam, Canada

Stephen Petschulat