Microsoft Technology Licensing, LLC

Hui Li

Dmitriy Meyerzon

Neal Robert Christiansen

Sarosh Cyrus Havewala

John Michael Winn

Omar Zia Khan

John Guiver

Gurashish Brar

Matteo Venanzi

Alexander Armin Spengler

Pavel Myshkov

Junyi Chai

Ivan Korostelev

Yordan Kirilov Zaykov

Elena Pochernina

Matthew R Yandek

Martin Kukla

Noura Farra

John Michael Sheehan

Vladimir V Gvozdev

Matthew Stuart Garson

Paul Trunley

Avinash G Pillai

Minglei Xu

Sravya Narala

Gurashish Singh Brar

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by an enterprise named entity recognition (ENER) model, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Enterprise knowledge graphs using enterprise named entity recognition

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. The plurality of entity names are linked based on entity metadata by traversing various relationships between people, files, sites, groups, associated with entities. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Enterprise knowledge graphs using multiple toolkits

Mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. A plurality of entity records are generated within a knowledge graph for mined entity names from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity names. Pattern recognition is applied to an active document using an enterprise named entity recognition (ENER) system to identify potential entity names within the document that match a respective one of a plurality of entity records in the knowledge graph. One or more matching entity names are annotated within the document with information from the knowledge graph for the respective ones of the plurality of entity records. The annotated information is displayed with the active document.

Annotations for enterprise knowledge graphs using multiple toolkits

Embodiments are directed to rapidly launching a virtual software application using on-demand file staging, rapidly launching a virtual software application using on-demand registry staging and to rapidly publishing a virtual software application using on-demand staging. In one scenario, a computer system creates a high-level file listing that includes a list of specified high-level data files and directories that are part of a virtual application package. The computer system receives an indication from an application user that the virtual application is to be accessed and dynamically creates sparse data files for those data files that are to be accessed based on the received indication. Then, upon determining that the virtual application is attempting to access the sparse data files, the computer system dynamically streams the data corresponding to the sparse data files on demand from the remote data store.

Fast application streaming using on-demand staging

In accordance with one or more aspects of the application-managed file versioning, a request to store a new version of a file is received from an application, the request having been generated by the application. A filename for the new version of the file is generated, derived from a standardized naming convention. The filename includes a first portion having at least a portion of a name of the file, and a second portion having data indicating that the new version of the file is a version of the file. A set of application programming interfaces (APIs) can be exposed that allow the application to manage file versions, such as creating a new version of a file, identifying one or more versions of the file, listing one or more versions of the file, and deleting one or more versions of the file.

Application-managed file versioning

A virtualization system is described herein that facilitates communication between a virtualized application and a host operating system to allow the application to correctly access resources referenced by the application. When the operating system creates a virtualized application process, the virtualization system annotates a data structure associated with the process with an identifier that identifies the virtualized application environment associated with the process. When operating system components make requests on behalf of the originating virtual process, a virtualization driver checks the data structure associated with the process to determine that the helper process is doing work on behalf of the virtualized application process. Upon discovering that the thread is doing virtual process work, the virtualization driver directs the helper process's thread to the virtual application's resources, allowing the helper process to accomplish the requested work with the correct data.

Annotating virtual application processes

Aspects of the subject matter described herein relate enforcing quotas in transactional file systems. In aspects, a filter monitors operations that may affect quota usage/charge of a file system object having a quota allotment. In doing so, the filter determines a quota value outside of any transaction for the object and a quota value associated with at least one transaction affecting the object. The filter receives a request that involves the use of additional quota. The filter then determines whether to allow or fail the request depending on whether enough quota is available to satisfy the request.

Quota enforcement with transacted file systems

Aspects of the subject matter described herein relate to tracking file system namespace changes during transactions. In aspects, a filter monitors operations that may affect a tracked portion of a transactional file system's namespace. When an operation that affects the tracked portion is received, a data structure is modified to track the changes. Nodes within the data structure are marked to indicate whether they can be seen inside or outside of the transaction. If the transaction commits or rolls back, nodes within the data structure are deleted and made visible as appropriate.

Tracking file system namespace changes during transactions

Aspects of the subject matter described herein relate to simulating storage devices. In aspects, a test automation engine instructs a simulator to simulate a storage device having certain characteristics such as a storage area network. The test automation engine then tests an application against the simulated storage device. Tests may include storage management requests and storage access requests. A provider may translate a request to one or more operations suitable to perform the request on the underlying simulated device. Shadow copies and the results of other storage management-related operations may be shared across computers via aspects of a simulation framework described herein.

Simulated storage area network

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Malden, MA, United States of America

Hui Li