Microsoft Technology Licensing, LLC

Microsoft Corporation

David G Campbell

Chris Demetrios Karkanias

Brian Scott Aust

Thomas Jackson

Stuart M Bowers

Peter M Spiro

Mark H Lucovsky

Amit Mital

Joseph D Belfiore

Eric D Rudder

Haroon Ahmed

Allen L Brown, Jr

Robert M Bruckner

David Paul Hill

Tandy Woodson Trower, Ii

Keith W Short

Steven M Cellini

Steve Capps

Kaviraj Singh

Vivek Gundotra

Patrick McKay

David Vaskevitch

Charles T Fitzgerald

Anil Kumar Nori

Pedro Celis

Paul A Maritz

James Robert Hamilton

Anders Bertil Vinberg

Emre Mehmet Kiciman

Sameet Harishanker Agarwal

F Soner Terek

J Patrick Thompson

Quentin J Clark

Tandy W Trower, Ii

Eric Ray Christensen

Silvia C Vega

Allen Brown

Donald F Ferguson

Paul Martiz

Embodiments render on a second data processing system, a result derived from a set of data by performing data processing across the first data processing system and a second data processing system. The amount of processing performed by the second data processing system can be dynamically adjusted. The first data processing system receives information indicating an amount of interface characteristics of the first data processing system as compared to interface characteristics of the second data processing system to be presented to a user. Data processing is dynamically allocated between the first data processing system and the second data processing system, based on an amount of interface characteristics of the first data processing system as compared to interface characteristics of the second data processing system to be presented to a user.

Dynamically adjusting brand and platform interface elements

A method includes acts for a method of rendering a result derived from a set of data by performing data processing across first and second data processing systems. The amount of processing performed by the second data processing system can be dynamically adjusted depending on business factors. The first data processing system receives information defining how the result will be rendered. The first data processing system receives information indicating at least one business constraints affecting at least one of the first data processing system or the second data processing system. The first data processing system determines data processing needed for providing the result. The first data processing system dynamically allocates the needed data processing between the first data processing system and the second data processing system, based on the business constraints affecting at least one of the first data processing system or the second data processing system.

Division of processing between systems based on business constraints

A method includes acts for rendering, on a data processing system, a result derived from a set of data by performing data processing across a first data processing system and a second data processing system. The amount of processing performed by the second data processing system can be dynamically adjusted depending on factors affecting the second data processing system. The first data processing system receives information defining how the result will be rendered at the second data processing system. The first data processing system receives information indicating factors affecting the second data processing system. The first data processing system dynamically allocates the needed data processing between the first data processing system and the second data processing system, based on factors affecting the second data processing system.

Division of processing between systems based on external factors

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

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

Described is a technology by which a system corresponding to a large scale application is built from subsystems that are differentiated from one another based on characteristics of each subsystem. Example characteristics include availability, reliability, redundancy, statefulness and/or performance. Subsystems are matched to known design patterns, based on each subsystem's individual characteristics. Each subsystem's characteristics are associated with that subsystem for subsequent use in operation of the system, e.g., for managing/servicing the subsystem. The known design patterns may be provided in a library, in a programming framework, in conjunction with a development tool, and/or as data associated with one or more operating system services, server systems and/or hosted services that include at least one configuration, policy and or schema. Certain design patterns and/or characteristics patterns may be blocked to prevent their usage.

Highly available large scale network and internet systems

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

A server federation cooperatively interacts to fulfill service requests by communicating using data structures that follow a schema in which the meaning of the communicated data is implied by the schema. Thus, in addition to the data being communicated, the meaning of the data is also communication allowing for intelligent decisions and inferences to be made based on the meaning of the data. Cooperative interaction is facilitated over a wide variety of networks by messaging through a common API that supports multiple transport mechanisms. Also, mid-session transfer between client devices is facilitated by schema and the transport-independent messaging structure. The user interfaces of the client devices will appear consistent even if the client devices have different user interface capabilities.

Distributed computing services platform

Various embodiments of the present invention are directed to a data store comprising Items, Elements, and Relationships. An Item is a unit of data storable in a data store and further comprises said Element and said Relationship. An Element is an instance of a type comprising one or more fields. A Relationship is a link between at least two Items. The data store further comprising a Core Schema to define a set of Core Items by which a hardware/software interface system understands and directly processes said set of Core Items in a predetermined and predictable way. The Core Items are derived (directly or indirectly) from a common single Base Item which, in turn, is a foundational Item in a Base Schema.

Systems and methods for data modeling in an item-based storage platform

Various embodiments of the present invention are directed to a storage platform comprising a database engine, a data store, an application programming interface. The data store is implemented on the database engine for storing data therein, and the data store implements a data model that supports the organization, searching, sharing, synchronization, and security of data stored in the data store. Specific types of data are described in schemas. The application programming interface enables application programs to access services and capabilities of the storage platform and to access the data described in the schemas. The storage platform supports interoperability with existing file systems, enables users and systems to synchronize data stored in different instances of the data store, and provides the ability for application programs to be notified about and to track changes made to the data in the data store.

Storage platform for organizing, searching, and sharing data

A method, apparatus, software and data structure are disclosed for more efficient fault testing of system software. A table is used to track routines that have been subjected to induced faults. As call paths are encountered in operating the system software, the table is consulted to determine which call paths have not yet been subjected to induced exceptions. These call paths are then subjected to the exceptions, thereby improving uniformity of distribution of induced exceptions.

Uniformly distributed induction of exceptions for testing computer software

A multilevel data locking mechanism is described. The mechanism determines the cost of multiple granularities of locks for a query prior to initiating a transaction. Locking selection is performed by a lock manager, and is based on row, page and table granularities. An SQL server provides access to a database comprising rows of data arranged in tables. A table has multiple rows per page, and multiple pages per table. Characteristics of the database schema and query are considered in determining which granularity will result in the lowest cost. These factors include the number of rows per page, the number of rows per index scan, the isolation level per scan, an update factor which is related to how long modified rows are held, the memory load on the system, the cost of concurrency which is based on the number of rows per page and pages per table, and the number of active scans currently on the table. The proper granularity is determined at run time to ensure that the current state of the system is considered.

Dynamic scalable lock mechanism

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Sammamish, WA, United States of America

David G Campbell