Fortinet, Inc.

Xxv Inc.

Helgi K Sigurbjarnarson

Yijou Chen

Úlfar Erlingsson

Jean-philippe E Martin

Ross T Bunker

Gurunatha Karaje

Sai Samrat Karlapudi

Saurabh Srivastava

Henele I Adams

Arthur Tilley

Timothy Hayes

Corianna Jacoby

Audrey Shang

William Stern

Naga Venkata Koundinya Muppalla

Neil B Vachharajani

Amey Sakhadeo

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

Semantic layer for data platform

An example method includes receiving, by a data streaming platform from a client, a write request to write data to a data stream; storing, by the data streaming platform and based on the receiving the write request, the data in a particular partition of a plurality of partitions of the data stream and on a particular cluster of a plurality of clusters of the data streaming platform; translating, by the data streaming platform, an actual offset specifying the particular cluster and the particular partition into a virtual offset defining an ordering of the data relative to other data in the data stream stored across the plurality of partitions and the plurality of clusters; and storing, by the data streaming platform, a mapping of the virtual offset to the actual offset.

Virtual data streams in a data streaming platform

Described herein are systems and methods for automatically building automations from desktop recordings using program synthesis. The problem of building automations for desktop applications can be lifted to a generalized concept of automations that operate on worlds whose 'world state' can change asynchronously to the actions of the automation. Advantageously, in contrast with synthesis systems that take input-output demonstrations to synthesize a function that maps between them, the method presented here can synthesize from time-series traces of actions to automations that generalizes each step. The present disclosure describes ways to a) build assisted automations, b) synthesize them from recordings, c) running assisted automations using interpreters that observe the world state and adjust the actions accordingly, d) discovering them from an always-on recorder, e) suggesting them in partial progress from an always-on recorder, and lastly f) iteratively improving the assisted automation by recordings of every subsequent run.

Framework and system for building assisted automations from recordings using program synthesis

Growing community of inventors

Seattle, WA, United States of America

Helgi K Sigurbjarnarson