Microsoft Technology Licensing, LLC

Other

Brian Scott Aust

Chris Demetrios Karkanias

Navjot Virk

Stuart M Bowers

Yun Lin

Shishir P Pardikar

Mohammed Samji

David C Steere

Thomas Jackson

David G Campbell

Allen L Brown, Jr

David George De Vorchik

Timothy P McKee

Jason Fergus Moore

Paul Stephen Hellyar

Joel K Grossman

Arish Ali

George G Robertson

David Matthew Kruse

Brandon T Hunt

Sriram Srinivasan

Thomas Ewan Jolly

Thomas A Langan

Oscar Gerardo Naim

Yin Xie

Silvia C Vega

Tom Jackson

Jim Karkanias

Dave Campbell

Sonia Patricia Carlson

Allen Brown

Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Expressing and executing semantic queries within a relational database

Technology is described that includes a method of feature specification via semantic queries. The method can include the operation of obtaining a data set having an identifier for each data row and a plurality of data features for each data row. A semantic query can be received that can be applied to the dataset that is usable by a machine learning tool. A entity feature map can be supplied that has entities and associated features for use by the machine learning tool. Further, a query structure can be analyzed using the entity feature map to identify input from the dataset for the machine learning tool.

Feature specification via semantic queries

A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.

Efficient probabilistic reasoning over semantic data

The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.

Model for hosting and invoking applications on virtual machines in a distributed computing environment

Described is a technology for composing queries by user interaction with objects and facets. A facet-based user interface allows users to select facets for use as filtering criteria, and a logic-based user interface allows users to logically combine object data. Query logic that processes the filtering criteria and/or logically combines the object data into a query. The facet-based user interface and logic-based user interface may be accessed via a unified user interface. The unified user interface may also provide a text editor for composing a text-based query.

Facet, logic and textual-based query composer

Described are techniques to facilitate temporal features in a semantic data store. Information about lifetimes of facts in a semantic store is maintained. Even when a fact is logically deleted, a physical record is kept available. The record of a logically deleted or invalid fact has associated lifetime information. For example, valid-from and valid-to time values. The record of a fact not yet deleted may have a valid-from time value indicating when it was created, became valid, etc. Queries against the semantic store may specify a timeslice (a point in time or a time range). The lifetime information can be used to satisfy such time-specific queries. Because records are maintained after they are logically deleted, it is also possible to accurately query a past state of the semantic store. Even if such a query is run at different times, same results may be obtained.

Temporal binding for semantic queries

Implementing a consistent user name-space on networked computing devices includes various components and methods. When a network connection between a local or host computing device and one or more remote computing devices is present, remote items are represented using the same methodology as items located on the host computing device. To the user, remote and local items are indistinguishable. When the network connection is lost or items located on a remote computer are otherwise unavailable, the unavailable items remain represented on the host computing device. Unavailable items are represented in a way that informs the user that the items may not be fully accessed.

System and method for generating a consistent user name-space on networked devices

A system and method for implementing a consistent user name-space on networked computing devices is provided. When a network connection between a local or host computing device and one or more remote computing devices is present, remote items are represented using the same methodology as items located on the host computing device. To the user, remote and local items are indistinguishable. When the network connection is lost or items located on a remote computer are otherwise unavailable, the unavailable items remain represented on the host computing device. Unavailable items are represented in a way that informs the user that the items may not be fully accessed.

System and method for generating a consistent user namespace on networked devices

Aspects of the subject matter described herein relate to synchronizing for directory actions performed while offline. In aspects, file system objects that reside on a remote data store are made available for offline use. While offline, a client receives an indication that a directory is to be deleted or renamed. In response, the client modifies its local copy of the directory and its descendants if any and stores one or more tombstones that include information that the client can use when synchronizing the changes made to the directory when the client is reconnected to the remote data store. When the client reconnects to the remote data store, the client synchronizes changes made while offline with the remote data store. Resolution conflict mechanisms may be used during this synchronization.

Synchronizing for directory changes performed while offline

The present invention provides a novel client side caching (CSC) infrastructure that supports transition states at the directory level to facilitate a seamless operation across connectivity states between client and remote server. More specifically, persistent caching is performed to safeguard the user (e.g., client) and/or the client applications across connectivity interruptions and/or bandwidth changes. This is accomplished in part by caching to a client data store the desirable file(s) together with the appropriate file access parameters. Moreover, the client maintains access to cached files during periods of disconnect. Furthermore, portions of a path can be offline while other portions upstream can remain online. CSC operates on the logical path which cooperates with DFS which operates on the physical path to keep track of files cached, accessed and changes in the directories. In addition, truth on the client is facilitated whether or not a conflict of file copies exists.

Persistent caching directory level support

Described is a system and method that synchronizes cached files and directories with a server's files and directories via a layered architecture, by collapsing state information into an index into a fixed set of known states, and looking up the action that synchronizes that state. The actions are then taken to synchronize the content. Any of fifty possible synchronization states are thus handled. A synchronization controller requests a subsystem to evaluate a local cache and remote file system to generate state flags for each item considered for synchronization. The synchronization controller processes the state information to identify a selected table from among a plurality of tables that contain the fifty values, and uses other flags to compute an index into the table. The selected table contains values that index into an action table containing actions that specify an operation set that is requested to synchronize the particular state scenario.

Method and system for synchronizing cached files

Described is a system and method that facilitates fast and reliable synchronization of computer/file system directories. A synchronization (FastSync) subsystem operates in a discovery/enumeration phase to provide a calling client with a set of the differences between directories, and then operates in an action phase to perform operations as directed by the client to synchronize the different directories. The discovery/enumeration and action phases use parallel operation and I/O (input/output) pipelining. Multiple threads are used during enumeration to enumerate each directory's children, and enqueues each sub-directory to be handled by a new thread. During the action phase, when an operation is requested, the FastSync subsystem packages up the operation, item pointer, and context information into an internal context block and queues that packet as a work item for a process thread pool to handle.

Synchronizing file system directories

A data presentation system dynamically generates and renders a user interface (UI) in the form of a Hypertext Markup Language (HTML) document. Extensible Markup Language (XML) data files contain data that defines the UI. The XML data is maintained independently of any application and/or computing system's settings and controls that define the presentation of the UI. The XML data files also have reference links to secondary data files that contain data further defining the UI. The UI is dynamically generated when an application invokes the XML data files. The HTML document incorporates the data from both the XML data files and from the secondary data files when rendering the UI.

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Redmond, WA, United States of America

Brian Scott Aust