Microsoft Technology Licensing, LLC

Other

James P Finnigan

Surajit J Chaudhuri

Vivek Ravindranath Narasayya

Kris Kuppuswamy Ganjam

Kaushik Chakrabarti

Zhimin Chen

Kanstantsyn Zoryn

Srivatsan Parthasarathy

Robert M Fries

Alan C Shi

William L Scheidel

Zhongyuan Wang

Hariharan Sivaramakrishnan

Kevin Roland Powell

Shon Kiran Shah

Anthony Nino Bice

Douglas W Potter

Curtis E Huttenhower

Prasanna H Sridhar

Alan H Goodman

Shon K Shah

Vivek R Narasayya

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

Deployment and servicing tasks associated with multi-tier, distributed applications, application environments and data centers are automated so that a person does not have to manually perform these tasks. All of the information describing and defining the distributed service is modeled and stored in a re-useable service template that can be used to drive an automated system to programmatically deploy and manage the service over time. Deployment and servicing of a distributed application can be automated using re-useable models that capture hardware and workload definitions. The re-useable models in the form of service templates enable delta-based servicing of the application. The service can be deployed to one or more physical machines, one or more virtual machines or to a combination thereof. A default deployment plan can be customized with instance-specific customizations of service parameters.

Automated deployment and servicing of distributed applications

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.

The present invention extends to methods, systems, and computer program products for recommending data based on user and data attributes. User information and accessed data sets are periodically (and possibly automatically) accessed and updated. Source attributes are derived from user information and accessed data sets. Target attributes are derived from data directories and data services. Source attributes for an accessed data set are used along target attributes for a data directory or data service to determine the desirability of data directory or data service as a source of data relevant to the accessed data set. The data directory and/or data service can be recommended as able to provide relevant data. Accordingly, recommend relevant data can be recommended to a user without the user having to expressly search for the relevant data or even know that the relevant data exists.

Recommending data based on user and data attributes

Configuration drift refers to changes made over time that cause a computer or service to deviate from a desired configuration. Configuration drift of a group of machines can be managed by defining configuration intent. Intent is defined by defining a configuration baseline comprised of a collection of related configuration rules. Configuration rules include settings, and targets which can be any managed entity that enables reporting of non-compliance at a more granular level. A configuration baseline can be completed by reading configuration rules from one or more well-configured computers. Configuration drift is assessed by comparing actual values to the configuration baseline values and is reported at a managed entity level instead of at a machine level. Remediation, returning the computer to a state of compliance with the configuration baseline, can be performed on demand. Remediations performed over time are retained and applied to a new instance of the service to eliminate configuration drift on the new instance.

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Redmond, WA, United States of America

James P Finnigan