Method and apparatus for stress management in a searchable data service. The searchable data service may provide a searchable index to a backend data store, and an interface to build and query the searchable index, that enables client applications to search for and retrieve locators for stored entities in the backend data store. Embodiments of the searchable data service may implement a distributed stress management mechanism that may provide functionality including, but not limited to, the automated monitoring of critical resources, analysis of resource usage, and decisions on and performance of actions to keep resource usage within comfort zones. In one embodiment, in response to usage of a particular resource being detected as out of the comfort zone on a node, an action may be performed to transfer at least part of the resource usage for the local resource to another node that provides a similar resource.

In some embodiments, a method receives a connection to a data source. The method analyzes metadata of the data source to determine a first type of metadata for a first type of database access and a second type of metadata for a second type of database access. The first type of metadata and the second type of metadata are combined into a data structure. Then, the method stores the data structure where the data structure is used to analyze a query to determine which of the first type of database access and the second type of database access to use for the query.

In some embodiments, a method receives a connection to a data source. The method analyzes metadata of the data source to determine a first type of metadata for a first type of database access and a second type of metadata for a second type of database access. The first type of metadata and the second type of metadata are combined into a data structure. Then, the method stores the data structure where the data structure is used to analyze a query to determine which of the first type of database access and the second type of database access to use for the query.

Methods and apparatus for extracting structured data from weblogs are disclosed. In some examples, the methods and apparatus include a web crawler to access a home page of a weblog, and identify a feed associated with the weblog. The methods and apparatus also include a feed finder to determine whether items in the feed contain sufficient content for feed-guided segmentation. The methods and apparatus also include a feed classifier to determine whether the items in the feed contain full content of the weblog. The methods and apparatus also include a wrapper to map data found in the feed into a representation of a weblog post, and screen scrape the weblog into the representation of the weblog post.

Methods and apparatus for extracting structured data from weblogs are disclosed. In some examples, the methods and apparatus include a web crawler to access a home page of a weblog, and identify a feed associated with the weblog. The methods and apparatus also include a feed finder to determine whether items in the feed contain sufficient content for feed-guided segmentation. The methods and apparatus also include a feed classifier to determine whether the items in the feed contain full content of the weblog. The methods and apparatus also include a wrapper to map data found in the feed into a representation of a weblog post, and screen scrape the weblog into the representation of the weblog post.

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

Annotations are customized for a tenant-specific search within a public corpus. In a non-limiting embodiment of the invention, a cartridge file is received by a semantic search application. The cartridge file includes a new attribute definition that is not available in an index of the semantic search application. The new attribute definition is incorporated within the index based on an approximation of one or more existing attributes in the index. One or more documents are retrieved from the public corpus based on a concept search using the incorporated new attribute definition and the one or more documents are annotated based on the incorporated new attribute definition. The annotated one or more documents are stored in a tenant-specific dataset separate from the public corpus.

A method for setting a display panel dynamically and an electronic device are provided. In a booting stage of the electronic device, a display driver is executed, wherein a motherboard of the electronic device includes at least one specified pin, a storage device and a processor. A predetermined pin value is set in the at least one specified pin and read from the at least one specified pin of the motherboard through the display driver. A database is queried through the display driver and includes multiple reference pin values corresponding to multiple sets of parameter values. The set of parameter values corresponding to the predetermined pin value is obtained according to the reference pin values; and the display panel is initialized through the display driver using the set of parameter values corresponding to the predetermined pin value.

A method for setting a display panel dynamically and an electronic device are provided. In a booting stage of the electronic device, a display driver is executed, wherein a motherboard of the electronic device includes at least one specified pin, a storage device and a processor. A predetermined pin value is set in the at least one specified pin and read from the at least one specified pin of the motherboard through the display driver. A database is queried through the display driver and includes multiple reference pin values corresponding to multiple sets of parameter values. The set of parameter values corresponding to the predetermined pin value is obtained according to the reference pin values; and the display panel is initialized through the display driver using the set of parameter values corresponding to the predetermined pin value.