Microsoft Technology Licensing, LLC

Other

Kanstantsyn Zoryn

Surajit J Chaudhuri

Vivek Ravindranath Narasayya

Kris Kuppuswamy Ganjam

Kaushik Chakrabarti

Zhimin Chen

James P Finnigan

Alexander Alperovich

Boris Shulman

Zhongyuan Wang

Lev Novik

Zhong Chen

Xindi Zhang

Krishna G Mamidipaka

Shoupei Li

Vivek R Narasayya

Vivek Narasayya

Surajit Chaudhuri

Kris Ganjam

Data events of an event stream are processed in accordance with temporally valid machine learning models. A streaming node may receive data events via an event stream. Each data event may be associated with a timestamp. The streaming node may also utilize punctuation events that specify the temporal validity of available machine learning models. The streaming node performs a temporal join operation for each data event based on its timestamp and the temporal validity. If the data event's timestamp is less than or equal to the punctuation event's timestamp, the data event is provided to the temporally valid machine learning model for processing thereby. If the data event's timestamp is greater than the punctuation event's timestamp, the data event is held until a subsequent punctuation event specifying a later timestamp is received.

Punctuation controlled machine learning model temporal validity

A method for joining an event stream with reference data includes loading a plurality of reference data snapshots from a reference data source into a cache. Punctuation events are supplied that indicate temporal validity for the plurality of reference data snapshots in the cache. A logical barrier is provided that restricts a flow of data events in the event stream to a cache lookup operation based on the punctuation events. The cache lookup operation is performed with respect to the data events in the event stream that are permitted to cross the logical barrier.

Punctuation controlled temporal reference data lookup

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

The present invention extends to methods, systems, and computer program products for computing features of structured data. Aspects of the invention include computing features of table components (e.g., of rows, columns, cells, etc.). Computed features can be used for ranking the table components. When aggregated, features for different components of a table can be used for ranking the table (e.g., a web table).

Computing features of structured data

The present invention extends to methods, systems, and computer program products for understanding tables for search. Aspects of the invention include identifying a subject tuple (e.g., a subject column) for a table, detecting a tuple header (e.g., a column header) using other tables, and detecting a tuple header (e.g., a column header) using a knowledge base. Implementations can be utilized in a structured data search system (SDSS) that indexes structured information, such as, tables in a relational database or html tables extracted from web pages. The SDSS allows users to search over the structured information (tables) using different mechanisms including keyword search and data finding data.

Understanding tables for search

The present invention extends to methods, systems, and computer program products for refining structured data indexes. Aspects of the invention include associating structured data, such as, for example, tables, with additional content. Additional content can include content outside the &#x3c;table&#x3e; and &#x3c;/table&#x3e; tags of a web table. Indexes for structured data (e.g., table indexes) can be refined based on the additional content to improve the relevance of providing parts of the structured data (e.g., parts of the table) in search results.

Refining structured data indexes

Various technologies that facilitate performance of a data finding data (DFD) search are described herein. A user specifies entities, for example, by entering the entities into a query field, selecting the entities from a computer-executable application, or the like. The user further specifies an attribute of the entities that is of interest. A query is constructed based upon the entities and the attribute, and a search for tables is performed based upon the entities and the attribute. Values of the attribute for the selected entities are identified in a table, and the values of the attribute are returned.

Retrieval of attribute values based upon identified entities

The present invention extends to methods, systems, and computer program products for annotating structured data for search. Aspects of the invention include associating structured data, such as, for example, tables, with additional content to improve indexing of the structured data for search and/or provide improved search results for structured data. Web pages can include tables as well as other content. The other content in a web page, such as, for example, content outside the &#x3c;table&#x3e; and &#x3c;/table&#x3e; tags of a web table, can be useful in supporting searches for web tables. Content in one web page can also be useful in supporting searches for a table in another web page.

Annotating structured data for search

The present invention extends to methods, systems, and computer program products for ranking tables for keyword search. Aspects of the invention include generating lists of candidate tables for inclusion in a search query response, computing table hit matrices, retrieving content from fields of candidate tables having keyword hits, generating ranking features of tables, and computing ranking scores for tables. Aspects of the invention can be used to match keywords against column names, to match keywords against values in subject and non-subject columns, and to match keywords against table descriptions like page titles, table captions, cell values, nearest headings and surrounding text. Which keywords are matched against which fields can depend on the table and/or the query (referred to as 'late binding').

Ranking tables for keyword search

The present invention extends to methods, systems, and computer program products for understanding tables for search. Aspects of the invention include identifying a subject column for a table, detecting a column header using other tables, and detecting a column header using a knowledge base. Implementations can be utilized in a structured data search system (SDSS) that indexes structured information, such as, tables in a relational database or html tables extracted from web pages. The SDSS allows users to search over the structured information (tables) using different mechanisms including keyword search and data finding data.

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Redmond, WA, United States of America

Kanstantsyn Zoryn