Mimosa Systems, Inc.

Foghorn Systems, Inc.

Tyco Fire + Security Gmbh

Mimosa, Inc.

Thirumalai Muppur Ravi

Roy P D'Souza

Mohan Reddy

Kamesh Raghavendra

Sastry K M Malladi

Sanjay Mehta

Bhushan Pandit

Sastry Km Malladi

Mohan Komalla Reddy

A method for enabling intelligence at the edge. Features include: triggering by sensor data in a software layer hosted on either a gateway device or an embedded system. Software layer is connected to a local-area network. A repository of services, applications, and data processing engines is made accessible by the software layer. Matching the sensor data with semantic descriptions of occurrence of specific conditions through an expression language made available by the software layer. Automatic discovery of pattern events by continuously executing expressions. Intelligently composing services and applications across the gateway device and embedded systems across the network managed by the software layer for chaining applications and analytics expressions. Optimizing the layout of the applications and analytics based on resource availability. Monitoring the health of the software layer. Storing of raw sensor data or results of expressions in a local time-series database or cloud storage.

Edge computing platform

A method for enabling intelligence at the edge. Features include: triggering by sensor data in a software layer hosted on either a gateway device or an embedded system. Software layer is connected to a local-area network. A repository of services, applications, and data processing engines is made accessible by the software layer. Matching the sensor data with semantic descriptions of occurrence of specific conditions through an expression language made available by the software layer. Automatic discovery of pattern events by continuously executing expressions. Intelligently composing services and applications across the gateway device and embedded systems across the network managed by the software layer for chaining applications and analytics expressions. Optimizing the layout of the applications and analytics based on resource availability. Monitoring the health of the software layer. Storing of raw sensor data or results of expressions in a local time-series database or cloud storage. Services and components can be containerized to ensure smooth running in any gateway environment.

Edge intelligence platform, and internet of things sensor streams system

Multi-dimensional surrogation systems and methods are provided that generate at least one up-to-date data surrogate using information of data and numerous data changes received from at least one data source, including a production system. Embodiments described further perform bulk-to-brick transformation of the up-to-date data surrogate. Brick-level data, or item data, is further indexed and stored in an application-aware manner. The stored item data is easily accessible by many data management applications for integrated item search and recovery functions, audit functions, monitoring and supervision functions, legal discovery functions, compliance functions, archival functions, backup functions, disaster recovery functions, and more. Bulk-to-brick transformation and access of the stored item data occur off of the production system, thus contributing no performance degradation to the production system.

Synthetic full copies of data and dynamic bulk-to-brick transformation

Dynamic bulk-to-brick transformation of data

Multi-dimensional surrogation systems and methods are provided that generate at least one data surrogate using information of data and numerous data changes received from at least one data source. The multi-dimensional surrogation transforms data of each data surrogate from a format of the data source to at least one alternative format. Original metadata is extracted from each data surrogate. New metadata is generated by combining portions of the original metadata and components of each data surrogate. The multi-dimensional surrogation generates an extended data surrogate comprising the data surrogate and the new metadata.

Data surrogate generation for data management

Systems and methods are described for service availability that provides automated recovery of server service in a timely and application intelligent manner, maintaining application consistency and integrity, while preserving server identity. The systems and methods, referred to herein as a Service Preservation System (SPS), manage complete recovery of server data and preserve continuity of server service, reestablishing user access to server(s) after an event or disaster in which in which primary or other server(s) fail. The failures, disasters, and losses referred to herein can be at many levels and include, but are not limited to, accidental deletion of an item, loss of an entire mailbox, loss of an entire disk drive, loss of an entire server, and/or loss of an entire server site.

Enterprise server version migration through identity preservation

Enterprise service availability through identity preservation

Multi-dimensional surrogates for data management

Growing community of inventors

Santa Clara, CA, United States of America

Thirumalai Muppur Ravi