Microsoft Technology Licensing, LLC

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Lev Novik

Moe Khosravy

Irena Hudis

Michael Ray Clark

Yunxin Wu

Ashish B Shah

Tomas Talius

Vivek Jawahir Jhaveri

Ashvinkumar J Sanghvi

Raymond W McCollum

Alexander Evgenievich Nosov

Darshatkumar A Shah

Patrick R Kenny

Oliver C Lee

Howard M Hance

Surajit J Chaudhuri

Douglas Brian Terry

Shaoyu Zhou

Boris Shulman

Siva Muhunthan

Henricus Johannes Maria Meijer

John Carlton Platt

Philip Daniel Piwonka

Sudarshan Anand Chitre

Katrika Marie Woodcock

Zhong Chen

Neil Leonard Padgett

Michael Scott Beckerman

Jason Duane Clark

Clemens Alden Szyperski

Ajay Kalhan

Marc Todd Friedman

Olivier Nano

Andrew Eyring Kimball

Katrika Marie Morris

Rajesh M Rao

Joachim Hammer

Muhunthan Sivapragasam

Cihangir Biyikoglu

Istvan Cseri

Vladimir Sadovsky

Darryl Ellis Rubin

Efim Hudis

Yeye He

Kunal Mukerjee

Christopher Alan Hays

Sameet Harishanker Agarwal

Rahul Potharaju

Tihomir Tsvetkov Tarnavski

Michael W Thomas

Myron C Thomas

David Michael Robinson

Wentao Wu

Sanjay Anand

Alan Geoffrey Boshier

Raxit A Kagalwala

Lukasz Gwozdz

Alexander Alperovich

Santeri Olavi Voutilainen

Ivo Jose Garcia Dos Santos

Laurent Bussard

Terry Yumin Kim

Jorg-Thomas Pfenning

Tony Petrossian

Edward Eric Thomas

Nils H Pohlmann

Michael A Thatcher

Andrei Maksimenka

Vassilii Khachaturov

Hari Sivaramakrishnan

Gayathri Tambaram Kailasam

Fred E Shaudys

Roy Dsouza

Subramanian Muralidhar

Balan Sethu Raman

Jonathan David Goldstein

Philip Alan Bernstein

Hanumantha Rao Kodavalla

Nigel Robin Ellis

Badrish Chandramouli

David J Simons

Shankar Pal

Anil Kumar Nori

Robin Dhananjay Dhamankar

David John Nettleton

Gregory Scott Friedman

Martin J Sleeman

Jason Todd Hunter

Vasile Paraschiv

Oliver Nicholas Seeliger

Kanstantsyn Zoryn

Ivo José Garcia Dos Santos

Eldar Akchurin

Vishal Kathuria

Marc Levy

Zhongwei Wu

Sankara J Subramanian

Bruno H M Denuit

Thomas Pfenning

Eli Cortez Custodio Vilarinho

Methods, systems and computer program products are described herein that provide a serverless, multi-engine, multi-user data lake indexing subsystem and application programming interface. Indexes are defined as derived datasets and stored on the data lake in a universal format that enables disparate engines to create and/or discover indexes for workload optimization. Embodiment of indexes enable stateful control and management of an index via metadata included in the index and stored on the data lake.

Serverless data lake indexing subsystem and application programming interface

A system enables metadata to be gathered about a data store beginning from the creation and generation of the data store, through subsequent use of the data store. This metadata can include keywords related to the data store and data appearing within the data store. Thus, keywords and other metadata can be generated without owner/creator intervention, with enough semantic meaning to make a discovery process associated with the data store much easier and efficient. Usage of or communication regarding a data store are monitored and keywords are extracted from the usage or communication. The keywords are then written to otherwise associated with metadata of the data store. During searching, keywords in the metadata are made available to be used to attempt to match query terms entered by a searcher.

Developing implicit metadata for data stores

Described herein is a system and method for startup and/or recovery for stream processing. During a startup phase: start anchor request(s), each identifying a particular time, are accumulated until request(s) are pending from downstream nodes. A minimum time of the accumulated start anchor request(s) is determined. If the processing system is an input node, an anchor associated with the determined minimum time is generated. Otherwise, a start anchor request is provided to an upstream node identifying the determined minimum time. Once the anchor associated with the determined minimum time is received (or generated), the anchor is provided in response to a polled start anchor request for the determined minimum time from a downstream node. Asynchronous requests for batches of data bounded by two specific anchors are performed in accordance with information stored in an ordered collection of anchors during a recovery phase.

Enhanced anchor protocol for event stream processing

A method for facilitating anchor shortening across streaming nodes in an event stream processing system may include receiving a full anchor at an upstream marshaller. The full anchor may be associated with a data batch that corresponds to one or more event streams. The full anchor may include an indication of an input point for the one or more event streams. The full anchor may be received from an upstream compute processor. The method may also include mapping the full anchor to an index anchor and passing the index anchor to a downstream marshaller.

Anchor shortening across streaming nodes

Systems, methods, and computer-executable instructions for partitioning a data set include receiving anchor attributes of a data set. The data set includes records, with each record including attributes. A set of filter attributes that are not mutually exclusive with any of the anchor attributes is determined. A set of candidate attributes that include each unique attribute from the first data set, excluding the anchor attributes and the filter attributes, is determined. For each of the anchor attributes and the anchor attributes, an attribute context is determined. For each of the candidate attributes, a context similarity between each of the anchor attributes is determined. A new anchor attribute is selected from the set of candidate attributes based on the context similarity.

Discovering schema using anchor attributes

POIs and content for POIs can be predicted for users based on a variety of factors, such as an analysis of their present path and directional changes, rates of changes, speed or other factors.

Predictive services for devices supporting dynamic direction information

A data custodian and curation system may store data from a data supplier in encrypted form and may allow users to consume the data when the consumers obtain access to the data through an agreement. The curation system may manage access to the data, even allowing a consumer to use the data as if it were their own, but may track each usage of the data to implement a payment scheme or various usage restrictions. The curation system may encrypt downstream uses of the data and operate as a digital rights management system for the data. The custodian and curation system may operate as a cloud service that may contain encrypted data from many data providers but where the data providers may individually control access to their data in a managed system at any granular level.

Data custodian and curation system

With the addition of directional information and gesture based input in a location based services environment, a variety of service(s) can be provided. For example, a computer system can identify a spatially-related point of interest based on a geographical location of the computer system and based on directional information. The computer system can display a first visualization of a first type, including an indication of the point of interest, which represents the geographical location. Then, based on detecting a gesture, the computer can display a second visualization of a second type, also including an indication of the point of interest, which also represents the geographical location.

Data services based on gesture and location information of device

Techniques and constructs that improve annotating target columns of a target database by performing automated annotation of the target columns using sources. The techniques include calculating a similarity score between a target column and columns extracted from a table that is included in a source. The similarity score is calculated based at least in part on a similarity between a value in the target column of the target database and a column value of the extracted column from the table and on a similarity between an identity of the target column of the target database and column identities of the extracted columns from the table. In some examples, the techniques calculate similarity scores for one or more extracted columns and annotate the target column based on the similarity scores.

Automated database schema annotation

Systems and methods that enable removal of data from an endpoint, while not propagating such removal to other endpoints when synchronizing data sources associated therewith. A designation component can indicate items as forgotten items, wherein such items are being known to an endpoint and deleted therefrom&#x2014;yet, reintroduced therein and reappearing (e.g., as a create) upon re-synchronizing with the replica over again. Moreover, the forgotten items can employ additional metadata such as in from of an additional bit that indicates whether an item is a forgotten item.

Forgetting items with knowledge based synchronization

The formulation of transformations on one or more input event streams to generation one or more output event streams. Accordingly, the transformations may be considered to be a query on the original input event stream(s). The event query includes event stream source representations representing an input event stream available in a particular execution context. The event query also includes a transformation module identifying the transformation set to be performed on the input event streams in the execution context. Once the query is properly formed, an execution module may then cause the transformations to be executed upon the designated input event stream(s) to generate output event streams.

Event stream transformations

Described is a technology by which a mobile device outputs an alert for a reminder based upon a current location of the mobile device with respect to an entity (person, place or thing) associated with the reminder. The reminders may be maintained and provided by a service. Each reminder includes a reference to target-related information. When the device moves to within a threshold proximity of the target, and other criteria (e.g., time of day) is satisfied, an alert is output by the device. A user may gesture with a device towards an entity to set a reminder associated with that entity, or to get any reminder associated with that entity. A device may also get reminders by scanning or image processing, e.g., by reading a barcode associated with a product to get any reminders associated with that product.

Geographic reminders

Stream processing can be performed using a pull-based, anchor-based methodology that guarantees once and only once processing and repeatability of the creation of output with no additional communication overhead during normal processing. Each node (computing device) in the graph (representing interconnected computing devices) establishes a system of anchors. An anchor describes a point in the output stream of the node, so that every event in the stream is either before or after any given anchor.

Using anchors for reliable stream processing

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Bellevue, WA, United States of America

Lev Novik