Microsoft Technology Licensing, LLC

Savas Parastatidis

Henricus Johannes Maria Meijer

Tihomir Tsvetkov Tarnavski

Stephen Harris Toub

Roger Shane Barga

Michael Isard

Alexander Sasha Stojanovic

Carl M Carter-Schwendler

Bart De Smet

Bart J F De Smet

Evelyne Viegas

Stream-based programming models allow subscriber to observe a stream of stream items received from a source, such as event notifications and updates to observed data. Presented herein is a stream-based reactive programming platform that allows observers to discover sources and streams, and to specify queries applicable to sources that results in new streams, including conditions that apply to the properties of a federation of sources. Variations of the reactive programming platform include generating a new stream from a non-reactive data source; generating a graph of sources and observers; and providing a directory of discoverable elements respectively identified by uniform resource identifiers. The reactive stream-based programming platform may also permit parameterization to configure the functionality of the elements participating in the stream; delegation of query tasks to discoverable elements of the programming platform; and federation of a heterogeneous set of elements to achieve a query using the reactive stream-based programming platform.

Stream-based reactive programming platform

Computation can be encoded in a lightweight and optionally typed data representation. The data representation can be specified in a prefix-based notation potentially including nested function-argument pairs. Further, the data representation can comprise optional static type information associated with at least a portion of computation. Still further, the data representation can be programming language and platform independent or surfaced in a particular programming language or platform.

Lightweight optionally typed data representation of computation

A technique for protecting the privacy of a query is provided using permissions that may be derived from an analysis of the context of the query. A monitoring component can be provided to receive or intercept queries directed at a datastore, and a privacy component is provided that analyzes permissions associated with the queries. The privacy component can also determine access levels of the queries based on the analysis of the permissions. A response component can then be provided to answer the queries in accordance with the access levels of the queries.

Permission based query processing

Techniques for defocusing queries over big datasets and dynamic datasets are provided to broaden search results and incorporate all potentially relevant data and avoid overly narrowing queries. An analytic component can receive queries directed at one region of a dataset and analyze the queries to generate inferences about the queries. The queries can then be defocused by a defocusing component and incorporate a larger dataset than originally searched to broaden the queries. The larger dataset can incorporate all, or a part of the original dataset and can also be disparate from the original dataset. Clusters of queries can also be merged and unified to deal with 'local minima' issues and broaden the understanding of the dataset. In other embodiments, dynamic data can be monitored and changes tracked, to ensure that all portions of the dataset are being searched by the queries.

De-focusing over big data for extraction of unknown value

Growing community of inventors

Seattle, WA, United States of America

Savas Parastatidis